diff --git a/.circleci/config.yml b/.circleci/config.yml index 371a2d0978..a0d74247de 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -6,6 +6,7 @@ version: 2.1 orbs: aws-cli: circleci/aws-cli@0.1.19 + slack: circleci/slack@3.4.2 dependencies: cache_directories: @@ -81,6 +82,27 @@ jobs: cd security-content source venv/bin/activate python bin/doc-gen.py --path . --output docs -v + - slack/status: + webhook: '${SLACK_WEBHOOK}' + fail_only: true + + test-links: + executor: content-executor + steps: + - run: + name: checkout repo + command: | + if [[ ! -z "${CIRCLE_PULL_REQUEST}" && ! -z "${CIRCLE_PR_NUMBER}" ]]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + cd security-content + git fetch origin pull/${CIRCLE_PR_NUMBER}/head:${CIRCLE_BRANCH} + git checkout ${CIRCLE_BRANCH} + elif [ "${CIRCLE_BRANCH}" == "" ]; then + git clone https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + else + git clone --branch ${CIRCLE_BRANCH} https://${GITHUB_TOKEN}@github.com/splunk/security-content.git + fi + - run: *apt-install - run: name: check for broken links using liche command: | @@ -92,8 +114,12 @@ jobs: source $BASH_ENV go get -u github.com/raviqqe/liche cd security-content - liche docs/stories_categories.md -v -t 45 - liche README.md -v -t 45 + liche docs/stories_categories.md -v -t 60 + liche README.md -v -t 60 + - slack/status: + webhook: '${SLACK_WEBHOOK}' + fail_only: true + build-sources: executor: content-executor steps: @@ -159,6 +185,10 @@ jobs: root: security-content/ paths: - content-pack-build.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + build-package: executor: content-executor steps: @@ -196,6 +226,10 @@ jobs: root: ~/dist paths: - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + run-appinspect: executor: content-executor steps: @@ -228,6 +262,10 @@ jobs: root: ~/ paths: - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + community-api-update: executor: aws-cli/default steps: @@ -311,6 +349,10 @@ jobs: root: ~/ paths: - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + publish-github-release: docker: - image: cibuilds/github:0.10 @@ -326,6 +368,10 @@ jobs: root: ~/ paths: - DA-ESS-ContentUpdate-latest.tar.gz + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + attack-range-update: executor: aws-cli/default steps: @@ -339,6 +385,10 @@ jobs: aws s3 cp ~/DA-ESS-ContentUpdate-latest.tar.gz s3://attack-range-appbinaries/ # make the file public since it is not by default aws s3api put-object-acl --bucket attack-range-appbinaries --key DA-ESS-ContentUpdate-latest.tar.gz --acl public-read + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + master-api-update: executor: aws-cli/default steps: @@ -366,8 +416,23 @@ jobs: aws s3 cp lookups s3://security-content/lookups --recursive --exclude "*" --include "*.csv" aws s3 cp macros s3://security-content/macros --recursive --exclude "*" --include "*.yml" aws s3 cp deployments s3://security-content/deployments --recursive --exclude "*" --include "*.yml" + - slack/status: + fail_only: true + webhook: '${SLACK_WEBHOOK}' + workflows: version: 2.1 + test-dead-links: + triggers: + - schedule: + cron: "0 0 * * *" + filters: + branches: + only: + - master + - develop + jobs: + - test-links validate-and-build: jobs: - validate-content: diff --git a/baselines/previously_seen_zoom_child_processes_initial.yml b/baselines/previously_seen_zoom_child_processes_initial.yml new file mode 100644 index 0000000000..4a25fdc2bc --- /dev/null +++ b/baselines/previously_seen_zoom_child_processes_initial.yml @@ -0,0 +1,20 @@ +name: Previously Seen Zoom Child Processes - Initial +id: 60b9c00f-a9d6-4e51-803c-5d63ea21b95b +version: 1 +date: '2020-05-20' +description: This search returns the first and last time a process was seen per endpoint with + a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is outputed to disk. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints, to populate the Endpoint + data model in the Processes node. +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen + from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) + by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` + | table dest, process_name, firstTimeSeen, lastTimeSeen + | outputlookup zoom_first_time_child_process' +tags: + analytics_story: + - Suspicious Zoom Child Processes + detections: + - First Time Seen Child Process of Zoom diff --git a/baselines/previously_seen_zoom_child_processes_update.yml b/baselines/previously_seen_zoom_child_processes_update.yml new file mode 100644 index 0000000000..cc739fbc1b --- /dev/null +++ b/baselines/previously_seen_zoom_child_processes_update.yml @@ -0,0 +1,25 @@ +name: Previously Seen Zoom Child Processes - Update +id: 80aea7fd-5da2-4533-b3c2-560533bfbaee +version: 1 +date: '2020-05-20' +description: This search returns the first and last time a process was seen per endpoint with + a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates + this information with historical data and filters out proces_name and endpoint pairs that have not + been seen within the specified time window. This updated table is outputed to disk. +how_to_implement: You must be ingesting endpoint data that tracks process activity, + including parent-child relationships from your endpoints, to populate the Endpoint + data model in the Processes node. +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen + from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) + by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` + | table firstTimeSeen, lastTimeSeen, process_name, dest + | inputlookup zoom_first_time_child_process append=t + | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by process_name, dest + | where lastTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_forget_window`") + | outputlookup zoom_first_time_child_process' +tags: + analytics_story: + - Suspicious Zoom Child Processes + detections: + - First Time Seen Child Process of Zoom diff --git a/bin/generate.py b/bin/generate.py index 2afda820a8..54fc9781eb 100644 --- a/bin/generate.py +++ b/bin/generate.py @@ -21,7 +21,6 @@ REPO_PATH = '' VERBOSE = False OUTPUT_PATH = '' - def load_objects(file_path, VERBOSE): files = [] manifest_files = path.join(path.expanduser(REPO_PATH), file_path) @@ -57,6 +56,22 @@ def generate_transforms_conf(lookups): return output_path +def generate_collections_conf(lookups): + filtered_lookups = list(filter(lambda i: 'collection' in i, lookups)) + sorted_lookups = sorted(filtered_lookups, key=lambda i: i['name']) + + utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() + + j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), + trim_blocks=True) + template = j2_env.get_template('collections.j2') + output_path = OUTPUT_PATH + "/default/collections.conf" + output = template.render(lookups=sorted_lookups, time=utc_time) + with open(output_path, 'w') as f: + f.write(output) + + return output_path + def generate_savedsearches_conf(detections, response_tasks, baselines, deployments): @@ -152,6 +167,7 @@ def generate_use_case_library_conf(stories, detections, response_tasks, baseline sto_res = map_response_tasks_to_stories(response_tasks) for story in stories: + story['author_name'], story['author_company'] = parse_author_company(story) if story['name'] in sto_det: story['detections'] = list(sto_det[story['name']]) if story['name'] in sto_res: @@ -237,6 +253,12 @@ def generate_workbench_panels(response_tasks, stories): trim_blocks=True) template = j2_env.get_template('panel.j2') output_path = OUTPUT_PATH + "/default/data/ui/panels/workbench_panel_" + response_file_name + ".xml" + + if response_task['search'].find(">") is not -1: + response_task['search']= response_task['search'].replace(">",">") + if response_task['search'].find("<") is not -1: + response_task['search']= response_task['search'].replace("<","<") + output = template.render(search=response_task['search']) with open(output_path, 'w') as f: f.write(output) @@ -265,6 +287,22 @@ def parse_data_models_from_search(search): return False +def parse_author_company(story): + match_author = re.search(r'^([^,]+)', story['author']) + if match_author is None: + match_author = 'no' + else: + match_author = match_author.group(1) + + match_company = re.search(r',\s?(.*)$', story['author']) + if match_company is None: + match_company = 'no' + else: + match_company = match_company.group(1) + + return match_author, match_company + + def get_deployments(object, deployments): matched_deployments = [] @@ -496,6 +534,7 @@ if __name__ == "__main__": print("WARNING: Generation of Mitre lookup failed.") lookups_path = generate_transforms_conf(lookups) + lookups_path = generate_collections_conf(lookups) detections = sorted(detections, key=lambda d: d['name']) response_tasks = sorted(response_tasks, key=lambda i: i['name']) diff --git a/bin/jinja2_templates/collections.j2 b/bin/jinja2_templates/collections.j2 new file mode 100644 index 0000000000..f0176a5cbd --- /dev/null +++ b/bin/jinja2_templates/collections.j2 @@ -0,0 +1,13 @@ +############# +# Automatically generated by generator.py in splunk/security-content +# On Date: {{ time }} UTC +# Author: Splunk Security Research +# Contact: research@splunk.com +############# + +{% for lookup in lookups %} +[{{ lookup.name }}] +enforceTypes = false +replicate = false + +{% endfor %} \ No newline at end of file diff --git a/bin/jinja2_templates/savedsearches.j2 b/bin/jinja2_templates/savedsearches.j2 index 68f28ae60f..4fb2b1a1c4 100644 --- a/bin/jinja2_templates/savedsearches.j2 +++ b/bin/jinja2_templates/savedsearches.j2 @@ -94,7 +94,7 @@ search = {{ detection.search }} ### ESCU BASELINES ### {% for baseline in baselines %} -[ESCU - {{ baseline.name }} - Baseline] +[ESCU - {{ baseline.name }}] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support diff --git a/bin/jinja2_templates/transforms.j2 b/bin/jinja2_templates/transforms.j2 index da640c650a..797d3ed917 100644 --- a/bin/jinja2_templates/transforms.j2 +++ b/bin/jinja2_templates/transforms.j2 @@ -11,6 +11,7 @@ filename = {{ lookup.filename }} {% else %} collection = {{ lookup.collection }} +external_type = kvstore {% endif %} {% if lookup.default_match is defined %} default_match = {{ lookup.default_match }} @@ -30,5 +31,11 @@ max_matches = {{ lookup.max_matches }} {% if lookup.min_matches is defined %} min_matches = {{ lookup.min_matches }} {% endif %} +{% if lookup.fields_list is defined %} +fields_list = {{ lookup.fields_list }} +{% endif %} +{% if lookup.filter is defined %} +filter = {{ lookup.filter }} +{% endif %} -{% endfor %} +{% endfor %} \ No newline at end of file diff --git a/bin/jinja2_templates/use_case_library.j2 b/bin/jinja2_templates/use_case_library.j2 index 292ca19b92..02d026f95c 100644 --- a/bin/jinja2_templates/use_case_library.j2 +++ b/bin/jinja2_templates/use_case_library.j2 @@ -13,7 +13,7 @@ category = {{ story.tags.category[0] }} last_updated = {{ story.date }} version = {{ story.version }} references = {{ story.references | tojson }} -maintainers = {{ story.author | tojson }} +maintainers = [{"company": "{{ story.author_company }}", "email": "-", "name": "{{ story.author_name }}"}] spec_version = 3 searches = {{ story.searches | tojson }} description = {{ story.description }} diff --git a/detections/detect_prohibited_applications_spawning_cmd_exe.yml b/detections/detect_prohibited_applications_spawning_cmd_exe.yml index 94a4c9cb39..9f73b49c61 100644 --- a/detections/detect_prohibited_applications_spawning_cmd_exe.yml +++ b/detections/detect_prohibited_applications_spawning_cmd_exe.yml @@ -24,6 +24,7 @@ tags: analytics_story: - Suspicious Command-Line Executions - Suspicious MSHTA Activity + - Suspicious Zoom Child Processes mitre_attack_id: - T1059 kill_chain_phases: diff --git a/detections/first_time_seen_child_process_of_zoom.yml b/detections/first_time_seen_child_process_of_zoom.yml new file mode 100644 index 0000000000..26cf530e42 --- /dev/null +++ b/detections/first_time_seen_child_process_of_zoom.yml @@ -0,0 +1,38 @@ +name: First Time Seen Child Process of Zoom +id: e91bd102-d630-4e76-ab73-7e3ba22c5961 +version: 1 +date: '2020-05-20' +description: This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. +how_to_implement: You must be ingesting data that records process activity from your + hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. +type: ESCU +references: [] +author: David Dorsey, Splunk +search: '| tstats `security_content_summariesonly` min(_time) as firstTime + values(Processes.parent_process_name) as parent_process_name + values(Processes.parent_process_id) as parent_process_id + values(Processes.process_name) as process_name values(Processes.process) as process + from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) + by Processes.process_id Processes.dest + | `drop_dm_object_name(Processes)` + | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen + | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") + | `security_content_ctime(firstTime)` + | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter`' +known_false_positives: A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +tags: + analytics_story: + - Suspicious Zoom Child Processes + mitre_attack_id: + - T1068 + kill_chain_phases: + - Actions on Objectives + cis20: + - CIS 3 + - CIS 8 + nist: + - PR.PT + - DE.CM + - PR.IP + security_domain: endpoint + asset_type: Endpoint diff --git a/detections/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml b/detections/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml new file mode 100644 index 0000000000..2545cacc05 --- /dev/null +++ b/detections/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml @@ -0,0 +1,18 @@ +author: "Rod Soto, Splunk" +date: "2020-05-26" +description: "This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb" +how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" +id: 55a2264a-b7f0-45e5-addd-1e5ab3415c72 +known_false_positives: "Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness." +name: "Kubernetes Azure detect most active service accounts by pod namespace" +references: [] +search: "sourcetype:mscs:storage:blob:json category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter`" +tags: + analytics_story: + - "Kubernetes Sensitive Role Activity" + asset_type: "Azure AKS Kubernetes cluster" + kill_chain_phases: + - Lateral Movement + security_domain: threat +type: ESCU +version: 1 diff --git a/detections/kubernetes_azure_detect_rbac_authorization_by_account.yml b/detections/kubernetes_azure_detect_rbac_authorization_by_account.yml new file mode 100644 index 0000000000..9186dfae04 --- /dev/null +++ b/detections/kubernetes_azure_detect_rbac_authorization_by_account.yml @@ -0,0 +1,18 @@ +author: "Rod Soto, Splunk" +date: "2020-05-26" +description: "This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences" +how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" +id: 47af7d20-0607-4079-97d7-7a29af58b54e +known_false_positives: "Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted." +name: "Kubernetes Azure detect RBAC authorization by account" +references: [] +search: "sourcetype:mscs:storage:blob:json category=kube-audit | spath input=properties.log | search annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_rbac_authorization_by_account_filter`" +tags: + analytics_story: + - "Kubernetes Sensitive Role Activity" + asset_type: "Azure AKS Kubernetes cluster" + kill_chain_phases: + - Lateral Movement + security_domain: threat +type: ESCU +version: 1 diff --git a/detections/kubernetes_azure_detect_sensitive_object_access.yml b/detections/kubernetes_azure_detect_sensitive_object_access.yml new file mode 100644 index 0000000000..2726aea834 --- /dev/null +++ b/detections/kubernetes_azure_detect_sensitive_object_access.yml @@ -0,0 +1,18 @@ +author: "Rod Soto, Splunk" +date: "2020-05-20" +description: "This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets" +how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" +id: 1bba382b-07fd-4ffa-b390-8002739b76e8 +known_false_positives: "Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection." +name: "Kubernetes Azure detect sensitive object access" +references: [] +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access_filter`" +tags: + analytics_story: + - "Kubernetes Sensitive Object Access Activity" + asset_type: "Azure AKS Kubernetes cluster" + kill_chain_phases: + - Lateral Movement + security_domain: threat +type: ESCU +version: 1 diff --git a/detections/kubernetes_azure_detect_sensitive_role_access.yml b/detections/kubernetes_azure_detect_sensitive_role_access.yml new file mode 100644 index 0000000000..0201873217 --- /dev/null +++ b/detections/kubernetes_azure_detect_sensitive_role_access.yml @@ -0,0 +1,18 @@ +author: "Rod Soto, Splunk" +date: "2020-05-20" +description: "This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets" +how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" +id: f27349e5-1641-4f6a-9e68-30402be0ad4c +known_false_positives: "Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. " +name: "Kubernetes Azure detect sensitive role access" +references: [] +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter`" +tags: + analytics_story: + - "Kubernetes Sensitive Role Activity" + asset_type: "Azure AKS Kubernetes cluster" + kill_chain_phases: + - Lateral Movement + security_domain: threat +type: ESCU +version: 1 diff --git a/detections/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml b/detections/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml new file mode 100644 index 0000000000..f13081df0e --- /dev/null +++ b/detections/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml @@ -0,0 +1,18 @@ +author: "Rod Soto, Splunk" +date: "2020-05-20" +description: "This search provides information on Kubernetes service accounts with failure or forbidden access status" +how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" +id: 019690d7-420f-4da0-b320-f27b09961514 +known_false_positives: "This search can give false positives as there might be inherent issues with authentications and permissions at cluster." +name: "Kubernetes Azure detect service accounts forbidden failure access" +references: [] +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter`" +tags: + analytics_story: + - "Kubernetes Sensitive Object Access Activity" + asset_type: "Azure AKS Kubernetes cluster" + kill_chain_phases: + - Lateral Movement + security_domain: threat +type: ESCU +version: 1 diff --git a/detections/kubernetes_azure_detect_suspicious_kubectl_calls.yml b/detections/kubernetes_azure_detect_suspicious_kubectl_calls.yml new file mode 100644 index 0000000000..f16bacfe26 --- /dev/null +++ b/detections/kubernetes_azure_detect_suspicious_kubectl_calls.yml @@ -0,0 +1,18 @@ +author: "Rod Soto, Splunk" +date: "2020-05-26" +description: "This search provides information on Kubectl calls with IP, verb namespace and object access context" +how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" +id: 4b6d1ba8-0000-4cec-87e6-6cbbd71651b5 +known_false_positives: "Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets" +name: "Kubernetes Azure detect suspicious kubectl calls" +references: [] +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_suspicious_kubectl_calls_filter`" +tags: + analytics_story: + - "Kubernetes Sensitive Object Access Activity" + asset_type: "Azure AKS Kubernetes cluster" + kill_chain_phases: + - Lateral Movement + security_domain: threat +type: ESCU +version: 1 diff --git a/detections/kubernetes_azure_pod_scan_fingerprint.yml b/detections/kubernetes_azure_pod_scan_fingerprint.yml new file mode 100644 index 0000000000..4dad0447e8 --- /dev/null +++ b/detections/kubernetes_azure_pod_scan_fingerprint.yml @@ -0,0 +1,18 @@ +author: "Rod Soto, Splunk" +date: "2020-05-20" +description: "This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure" +how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" +id: 86aad3e0-732f-4f66-bbbc-70df448e461d +known_false_positives: "Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context." +name: "Kubernetes Azure pod scan fingerprint" +references: [] +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod |`kubernetes_azure_pod_scan_fingerprint_filter`" +tags: + analytics_story: + - "Kubernetes Scanning Activity" + asset_type: "Azure AKS Kubernetes cluster" + kill_chain_phases: + - Reconnaissance + security_domain: threat +type: ESCU +version: 1 diff --git a/detections/kubernetes_azure_scan_fingerprint.yml b/detections/kubernetes_azure_scan_fingerprint.yml new file mode 100644 index 0000000000..d35cf6ce49 --- /dev/null +++ b/detections/kubernetes_azure_scan_fingerprint.yml @@ -0,0 +1,18 @@ +author: "Rod Soto, Splunk" +date: "2020-05-19" +description: "This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure" +how_to_implement: "You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics" +id: c5e5bd5c-1013-4841-8b23-e7b3253c840a +known_false_positives: "Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context." +name: "Kubernetes Azure scan fingerprint" +references: [] +search: "`kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason |`kubernetes_azure_scan_fingerprint_filter`" +tags: + analytics_story: + - "Kubernetes Scanning Activity" + asset_type: "Azure AKS Kubernetes cluster" + kill_chain_phases: + - Reconnaissance + security_domain: threat +type: ESCU +version: 1 diff --git a/docs/README.md b/docs/README.md index dda91ce796..9ff68be2bf 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,9 +1,9 @@ -# Welcome to Splunk Security Research! +# Splunk Security Content ![](static/logo.png) -Thanks for stopping by the Splunk Security Research Team's resource portal! Here you'll find background and links to our security content and other related tools. +Welcome to the Splunk Security Content -Splunk security content is organized into "Analytic Stories," themed security guides that provide background on TTPs, mapped to the MITRE framework, the Lockheed Martin Kill Chain, and CIS controls. They include Splunk searches, machine-learning algorithms, and Splunk Phantom playbooks (where available)—all built to work together to detect, investigate, and respond to threats. The other apps were designed to help you derive more value from this content. +This project gives you access to our repository of Analytic Stories that are security guides which provide background on TTPs, mapped to the MITRE framework, the Lockheed Martin Kill Chain, and CIS controls. They include Splunk searches, machine-learning algorithms, and Splunk Phantom playbooks (where available)—all designed to work together to detect, investigate, and respond to threats. ## View Our Content You can review our Analytic Stories by category [here](stories_categories.md), or in our [Splunk App](https://github.com/splunk/security-content/releases). @@ -13,21 +13,13 @@ If you prefer working with the command line, check out our [API](https://docs.sp ``` curl -s https://content.splunkresearch.com | jq { - "hello": "welcome to Splunks Research security content api", - "available_endpoints": [ - "/stories", - "/detections", - "/investigations", - "/baselines", - "/responses", - "/package" - ] + "hello": "welcome to Splunks Research security content api" } ``` ## Getting Started -Once you've cloned the security-content repo, we recommend using our Analytic Story Execution App [(ASX)](https://github.com/splunk/analytics_story_execution) to execute all of the searches, machine-learning models, and Splunk Phantom playbooks in the story automatically. +Once you've installed our [app](https://github.com/splunk/security-content/releases), we recommend using our Analytic Story Execution App [(ASX)](https://github.com/splunk/analytics_story_execution) to execute and schedule all of the detections a story automatically. ## Test Out The Detections The [attack_range](https://http://github.com/splunk/attack_range) project allows you to spin up an enviroment and launch attacks against it to test the detections. @@ -36,15 +28,25 @@ The [attack_range](https://http://github.com/splunk/attack_range) project allows If you get stuck or need help with any of our tools, see our [support options](https://github.com/splunk/security-content#support). ## Contribute Content -If you want to help the rest of the security community by sharing your own detections, see our [contributor guide](https://github.com/splunk/security-content#Contributing). Digital defenders unite! +If you want to help the rest of the security community by sharing your own detections, see our [contributor guide](https://github.com/splunk/security-content/blob/develop/docs/CONTRIBUTING.md). Digital defenders unite! -## Content Spec Documentation -* [Story](spec/story.spec.md) -* [Detections](spec/detections.spec.md) -* [Investigations](spec/investigations.spec.md) -* [Responses](spec/responses.spec.md) -* [Baselines](spec/baselines.spec.md) +## Content Parts +* [stories/](stories/): All Analytic Stories +* [detections/](detections/): Splunk Enterprise, Splunk UBA, and Splunk Phantom detections that power Analytic Stories +* [response_tasks/](response_tasks/): Splunk Enterprise and Splunk Phantom investigative searches and playbooks employed by Analytic Stories +* [responses/](responses/): Automated Splunk Enterprise and Splunk Phantom responses triggered by Analytic Stories +* [baselines/](baselines/): Splunk Phantom and Splunk Enterprise baseline searches needed to support detection searches in Analytic Stories + +#### Content Spec Files +* [stories](docs/spec/stories.spec.md) +* [detections](docs/spec/detections.spec.md) +* [deployments](docs/spec/deployments.spec.md) +* [responses](docs/spec/responses.spec.md) +* [response_tasks](docs/spec/response_tasks.spec.md) +* [baselines](docs/spec/baselines.spec.md) +* [lookups](docs/spec/lookups.spec.md) +* [macros](docs/spec/macros.spec.md) diff --git a/docs/spec/baselines.spec.json b/docs/spec/baselines.spec.json new file mode 100644 index 0000000000..c13fc47497 --- /dev/null +++ b/docs/spec/baselines.spec.json @@ -0,0 +1,107 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "description": "schema for baselines", + "properties": { + "author": { + "$id": "#/properties/author", + "default": "", + "description": "Author of the baseline", + "examples": [ + "Bahvin Patel, Splunk" + ], + "type": "string" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "date of creation or modification, format yyyy-mm-dd", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "A detailed description of the baseline ", + "examples": [ + "This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days" + ], + "type": "string" + }, + "how_to_implement": { + "$id": "#/properties/how_to_implement", + "default": "", + "description": "information about how to implement. Only needed for non standard implementations.", + "examples": [ + "This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "UUID as unique identifier", + "examples": [ + "fc0edc95-ff2b-48b0-9f6f-63da3789fd63" + ], + "type": "string" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "examples": [ + "Previously Seen AWS Regions" + ], + "title": "Name of baseline", + "type": "string" + }, + "search": { + "$id": "#/properties/search", + "default": "", + "description": "The Splunk search for the baseline", + "examples": [ + "cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv" + ], + "type": "string" + }, + "tags": { + "$id": "#/properties/tags", + "additionalProperties": true, + "default": {}, + "description": "An array of key value pairs for tagging", + "examples": [ + { + "analytics_story": "suspicious_aws_ec2_activities", + "custom_key": "custom_value" + } + ], + "minItems": 1, + "type": "object", + "uniqueItems": true + }, + "version": { + "$id": "#/properties/version", + "default": 0, + "description": "version of baseline, e.g. 1 or 2 ...", + "examples": [ + 1 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "author", + "search", + "tags" + ], + "title": "Baseline Schema", + "type": "object" +} diff --git a/docs/spec/deployments.spec.json b/docs/spec/deployments.spec.json new file mode 100644 index 0000000000..c417cc0bf3 --- /dev/null +++ b/docs/spec/deployments.spec.json @@ -0,0 +1,264 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "default": {}, + "description": "schema for deployment", + "properties": { + "alert_action": { + "$id": "#/properties/alert_action", + "additionalProperties": true, + "default": {}, + "description": "Set alert action parameter for search", + "examples": [ + { + "email": { + "message": "Splunk Alert $name$ triggered %fields%", + "subject": "Splunk Alert $name$", + "to": "test@test.com" + }, + "index": { + "name": "asx" + }, + "notable": { + "rule_description": "%description%", + "rule_title": "%name%" + } + } + ], + "properties": { + "email": { + "$id": "#/properties/alert_action/properties/email", + "additionalProperties": true, + "default": {}, + "description": "By enabling it, an email is sent with the results", + "examples": [ + { + "message": "Splunk Alert $name$ triggered %fields%", + "subject": "Splunk Alert $name$", + "to": "test@test.com" + } + ], + "properties": { + "message": { + "$id": "#/properties/alert_action/properties/email/properties/message", + "default": "", + "description": "message of email", + "examples": [ + "Splunk Alert $name$ triggered %fields%" + ], + "type": "string" + }, + "subject": { + "$id": "#/properties/alert_action/properties/email/properties/subject", + "default": "", + "description": "Subject of email", + "examples": [ + "Splunk Alert $name$" + ], + "type": "string" + }, + "to": { + "$id": "#/properties/alert_action/properties/email/properties/to", + "default": "", + "description": "Recipient of email", + "examples": [ + "test@test.com" + ], + "type": "string" + } + }, + "required": [ + "to", + "subject", + "message" + ], + "type": "object" + }, + "index": { + "$id": "#/properties/alert_action/properties/index", + "additionalProperties": true, + "default": {}, + "description": "By enabling it, the results are stored in another index", + "examples": [ + { + "name": "asx" + } + ], + "properties": { + "name": { + "$id": "#/properties/alert_action/properties/index/properties/name", + "default": "", + "description": "Name of the index", + "examples": [ + "asx" + ], + "type": "string" + } + }, + "required": [ + "name" + ], + "type": "object" + }, + "notable": { + "$id": "#/properties/alert_action/properties/notable", + "additionalProperties": true, + "default": {}, + "description": "By enabling it, a notable is generated", + "examples": [ + { + "rule_description": "%description%", + "rule_title": "%name%" + } + ], + "properties": { + "rule_description": { + "$id": "#/properties/alert_action/properties/notable/properties/rule_description", + "default": "", + "description": "Rule description of the notable event", + "examples": [ + "%description%" + ], + "type": "string" + }, + "rule_title": { + "$id": "#/properties/alert_action/properties/notable/properties/rule_title", + "default": "", + "description": "Rule title of the notable event", + "examples": [ + "%name%" + ], + "type": "string" + } + }, + "required": [ + "rule_title", + "rule_description" + ], + "type": "object" + } + }, + "type": "object" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "date of creation or modification, format yyyy-mm-dd", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "description of the deployment configuration", + "examples": [ + "This deployment configuration provides a standard scheduling policy over all rules." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "uuid as unique identifier", + "examples": [ + "fb4c31b0-13e8-4155-8aa5-24de4b8d6717" + ], + "type": "string" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "description": "Name of deployment configuration", + "examples": [ + "Deployment Configuration all Detections" + ], + "type": "string" + }, + "scheduling": { + "$id": "#/properties/scheduling", + "additionalProperties": true, + "default": {}, + "description": "allows to set scheduling parameter", + "examples": [ + { + "cron_schedule": "*/10 * * * *", + "earliest_time": "-10m", + "latest_time": "now", + "schedule_window": "auto" + } + ], + "properties": { + "cron_schedule": { + "$id": "#/properties/scheduling/properties/cron_schedule", + "default": "", + "description": "Cron schedule to schedule the Splunk searches.", + "examples": [ + "*/10 * * * *" + ], + "type": "string" + }, + "earliest_time": { + "$id": "#/properties/scheduling/properties/earliest_time", + "default": "", + "description": "earliest time of search", + "examples": [ + "-10m" + ], + "type": "string" + }, + "latest_time": { + "$id": "#/properties/scheduling/properties/latest_time", + "default": "", + "description": "latest time of search", + "examples": [ + "now" + ], + "type": "string" + }, + "schedule_window": { + "$id": "#/properties/scheduling/properties/schedule_window", + "default": "", + "description": "schedule window for search", + "examples": [ + "auto" + ], + "type": "string" + } + }, + "required": [ + "cron_schedule", + "earliest_time", + "latest_time" + ], + "type": "object" + }, + "tags": { + "$id": "#/properties/tags", + "additionalProperties": true, + "default": {}, + "description": "An array of key value pairs for tagging", + "examples": [ + { + "analytics_story": "credential_dumping" + } + ], + "minItems": 1, + "type": "object", + "uniqueItems": true + } + }, + "required": [ + "name", + "id", + "date", + "description", + "scheduling", + "alert_action", + "tags" + ], + "title": "Deployment Schema", + "type": "object" +} diff --git a/docs/spec/detections.spec.json b/docs/spec/detections.spec.json new file mode 100644 index 0000000000..9f9fc25482 --- /dev/null +++ b/docs/spec/detections.spec.json @@ -0,0 +1,157 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "description": "schema for detections", + "properties": { + "author": { + "$id": "#/properties/author", + "default": "", + "description": "Author of the detection", + "examples": [ + "Patrick Bareiss, Splunk" + ], + "type": "string" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "date of creation or modification, format yyyy-mm-dd", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "A detailed description of the detection", + "examples": [ + "dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain a memory dump of a process. This search detects the usage of this DLL for creating a memory dump of LSASS process. Memory dumps of the LSASS process can be created with tools such as Windows Task Manager or procdump." + ], + "type": "string" + }, + "how_to_implement": { + "$id": "#/properties/how_to_implement", + "default": "", + "description": "information about how to implement. Only needed for non standard implementations.", + "examples": [ + "This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "UUID as unique identifier", + "examples": [ + "fb4c31b0-13e8-4155-8aa5-24de4b8d6717" + ], + "type": "string" + }, + "known_false_positives": { + "$id": "#/properties/knwon_false_positives", + "default": "", + "description": "known false postives", + "examples": [ + "Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual." + ], + "type": "string" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "examples": [ + "Access LSASS Memory for Dump Creation" + ], + "title": "Name of detection", + "type": "string" + }, + "references": { + "$id": "#/properties/references", + "additionalItems": true, + "default": [], + "description": "A list of references for this detection", + "examples": [ + [ + "https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf" + ] + ], + "items": { + "$id": "#/properties/references/items", + "default": "", + "description": "An explanation about the purpose of this instance.", + "examples": [ + "https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf" + ], + "title": "The Items Schema", + "type": "string" + }, + "type": "array" + }, + "search": { + "$id": "#/properties/search", + "default": "", + "description": "The Splunk search for the detection", + "examples": [ + "`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* | stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId | rename Computer as dest | `security_content_ctime(firstTime)`| `security_content_ctime(lastTime)` | `access_lsass_memory_for_dump_creation_filter`" + ], + "type": "string" + }, + "tags": { + "$id": "#/properties/tags", + "additionalProperties": true, + "default": {}, + "description": "An array of key value pairs for tagging", + "examples": [ + { + "analytics_story": "credential_dumping", + "custom_key": "custom_value" + } + ], + "minItems": 1, + "type": "object", + "uniqueItems": true + }, + "type": { + "$id": "#/properties/type", + "default": "", + "description": "type of detection", + "examples": [ + "ESCU" + ], + "items": { + "enum": [ + "ESCU", + "SSE", + "RBA" + ], + "type": "string" + }, + "type": "string" + }, + "version": { + "$id": "#/properties/version", + "default": 0, + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + 2 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "type", + "author", + "search", + "known_false_positives", + "tags" + ], + "title": "Detection Schema", + "type": "object" +} diff --git a/docs/spec/lookups.spec.json b/docs/spec/lookups.spec.json new file mode 100644 index 0000000000..0c85720259 --- /dev/null +++ b/docs/spec/lookups.spec.json @@ -0,0 +1,88 @@ +{ + "$id": "https://api.splunkresearch.com/schemas/lookups.json", + "$schema": "http://json-schema.org/draft-07/schema#", + "description": "A object that defines a lookup file and its properties.", + "oneOf": [ + { + "required": [ + "collection" + ] + }, + { + "required": [ + "filename" + ] + } + ], + "properties": { + "case_sensitive_match": { + "description": "What the macro is intended to filter", + "enum": [ + "true", + "false" + ], + "examples": [ + "true" + ], + "type": "string" + }, + "collection": { + "description": "Name of the collection to use for this lookup", + "examples": [ + "prohibited_apps_launching_cmd" + ], + "type": "string" + }, + "default_match": { + "description": "The default value if no match is found", + "examples": [ + "true" + ], + "type": "string" + }, + "description": { + "description": "The description of this lookup", + "examples": [ + "This lookup contains file names that exist in the Windows\\System32 directory" + ], + "type": "string" + }, + "filename": { + "description": "The name of the file to use for this lookup", + "examples": [ + "prohibited_apps_launching_cmd.csv" + ], + "type": "string" + }, + "match_type": { + "description": "A comma and space-delimited list of () specification to allow for non-exact matching", + "examples": [ + "WILDCARD(process)" + ], + "type": "string" + }, + "max_matches": { + "description": "The maximum number of possible matches for each input lookup value", + "examples": [ + "100" + ], + "type": "integer" + }, + "min_matches": { + "description": "Minimum number of possible matches for each input lookup value", + "examples": [ + "1" + ], + "type": "integer" + }, + "name": { + "description": "The name of the lookup to be used in searches", + "examples": [ + "isWindowsSystemFile_lookup" + ], + "type": "string" + } + }, + "title": "Lookup Manifest", + "type": "object" +} diff --git a/docs/spec/macros.spec.json b/docs/spec/macros.spec.json new file mode 100644 index 0000000000..cd0f5cafe1 --- /dev/null +++ b/docs/spec/macros.spec.json @@ -0,0 +1,43 @@ +{ + "$id": "https://api.splunkresearch.com/schemas/macros.json", + "$schema": "http://json-schema.org/draft-07/schema#", + "description": "An object that defines the parameters for a Splunk Macro", + "properties": { + "arguments": { + "description": "A list of the arguments being passed to this macro", + "items": { + "type": "string" + }, + "minItems": 0, + "type": "array", + "uniqueItems": true + }, + "definition": { + "description": "The macro definition", + "examples": [ + "(query=fls-na* AND query = www* AND query=images*)" + ], + "type": "string" + }, + "description": { + "description": "What the macro is intended to filter", + "examples": [ + "Use this macro to filter out known good objects" + ], + "type": "string" + }, + "name": { + "description": "The name of the macro", + "examples": [ + "detection_search_output_filter" + ], + "type": "string" + } + }, + "required": [ + "name", + "description" + ], + "title": "Macro Manifest", + "type": "object" +} diff --git a/docs/spec/response_tasks.spec.json b/docs/spec/response_tasks.spec.json new file mode 100644 index 0000000000..257e12f5fb --- /dev/null +++ b/docs/spec/response_tasks.spec.json @@ -0,0 +1,159 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "default": {}, + "description": "schema for response tasks", + "properties": { + "author": { + "$id": "#/properties/author", + "default": "", + "description": "Author of response task", + "examples": [ + "Patrick Barei\u00df, Splunk" + ], + "type": "string" + }, + "dashboard": { + "$id": "#/properties/dashboard", + "default": "", + "description": "Name of dashboard used as response task", + "examples": [ + "process_chain_analysis.json" + ], + "type": "string" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "date of creation or modification, format yyyy-mm-dd", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "Description of response task", + "examples": [ + "Response Task example description" + ], + "type": "string" + }, + "how_to_implement": { + "$id": "#/properties/how_to_implement", + "default": "", + "description": "information about how to implement. Only needed for non standard implementations.", + "examples": [ + "This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "UUID as unique identifier", + "examples": [ + "fb4c31b0-13e8-4155-8aa5-24de4b8d6717" + ], + "type": "string" + }, + "inputs": { + "$id": "#/properties/inputs", + "default": [], + "description": "Inputs used from the response task", + "examples": [ + [ + "lookup_file" + ] + ], + "type": "array" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "description": "Namo fo response task", + "examples": [ + "Response Tas Example" + ], + "type": "string" + }, + "playbook": { + "$id": "#/properties/playbook", + "additionalProperties": true, + "default": {}, + "description": "A phantom playbook as response task", + "examples": [ + { + "name": "lets_encrypt_domain_investigate.json", + "url_json": "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json", + "url_python": "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py" + } + ], + "properties": { + "name": { + "$id": "#/properties/playbook/properties/name", + "default": "", + "description": "Name of Phantom Playbook", + "examples": [ + "lets_encrypt_domain_investigate.json" + ], + "type": "string" + }, + "url_json": { + "$id": "#/properties/playbook/properties/url_json", + "default": "", + "description": "URL for phantom playbook json file", + "examples": [ + "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.json" + ], + "type": "string" + }, + "url_python": { + "$id": "#/properties/playbook/properties/url_python", + "default": "", + "description": "URL for phantom playbook python file", + "examples": [ + "https://github.com/phantomcyber/playbooks/blob/4.6/lets_encrypt_domain_investigate.py" + ], + "type": "string" + } + }, + "required": [ + "name", + "url_json", + "url_python" + ], + "type": "object" + }, + "search": { + "$id": "#/properties/search", + "default": "", + "description": "Search as response task", + "examples": [ + "`sysmon` EventCode=1 | search [| inputlookup %lookup_file% ] | stats count by dest user process_name" + ], + "type": "string" + }, + "version": { + "$id": "#/properties/version", + "default": 0, + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + 3 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "author" + ], + "title": "Response Task Schema", + "type": "object" +} diff --git a/docs/spec/responses.spec.json b/docs/spec/responses.spec.json new file mode 100644 index 0000000000..f14d63f109 --- /dev/null +++ b/docs/spec/responses.spec.json @@ -0,0 +1,117 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "default": {}, + "description": "schema for response", + "properties": { + "author": { + "$id": "#/properties/author", + "default": "", + "description": "Author of the response", + "examples": [ + "Rico Valdez, Patrick Barei\u00df, Splunk" + ], + "type": "string" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "Description of response", + "examples": [ + "Response example." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "UUID as unique identifier", + "examples": [ + "fb4c31b0-13e8-4155-8aa5-24de4b8d6717" + ], + "type": "string" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "description": "Name of response", + "examples": [ + "Response Example" + ], + "type": "string" + }, + "response_tasks": { + "$id": "#/properties/response_tasks", + "additionalProperties": true, + "default": {}, + "description": "Response tasks divided into phases", + "examples": [ + { + "another_phase": [ + { + "id": "7c72d944-3995-4485-8e57-67b4c353989b", + "name": "Another investigation" + } + ], + "identification": [ + { + "id": "c36f3f48-e0bb-4c20-a62a-cdc8f6418892", + "name": "Investigate Indicator of Compromise Hash" + }, + { + "id": "0dc849b2-2eb4-4fd2-add1-b6cc475765f0", + "name": "Investigate Domains" + } + ] + } + ], + "minItems": 1, + "type": "array" + }, + "tags": { + "$id": "#/properties/tags", + "additionalProperties": true, + "default": {}, + "description": "An array of key value pairs for tagging", + "examples": [ + { + "analytics_story": "credential_dumping" + } + ], + "minItems": 1, + "type": "object", + "uniqueItems": true + }, + "version": { + "$id": "#/properties/version", + "default": 0, + "description": "version of detection, e.g. 1 or 2 ...", + "examples": [ + 1 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "author", + "response_tasks", + "tags" + ], + "title": "Response Schema", + "type": "object" +} diff --git a/docs/spec/stories.spec.json b/docs/spec/stories.spec.json new file mode 100644 index 0000000000..3eda58b394 --- /dev/null +++ b/docs/spec/stories.spec.json @@ -0,0 +1,106 @@ +{ + "$id": "http://example.com/example.json", + "$schema": "http://json-schema.org/draft-07/schema", + "additionalProperties": true, + "default": {}, + "description": "schema analytics story", + "properties": { + "author": { + "$id": "#/properties/author", + "default": "", + "description": "Author of the analytics story", + "examples": [ + "Rico Valdez, Patrick Barei\u00df, Splunk" + ], + "type": "string" + }, + "date": { + "$id": "#/properties/date", + "default": "", + "description": "date of creation or modification, format yyyy-mm-dd", + "examples": [ + "2019-12-06" + ], + "type": "string" + }, + "description": { + "$id": "#/properties/description", + "default": "", + "description": "description of the analytics story", + "examples": [ + "Uncover activity consistent with credential dumping, a technique where attackers compromise systems and attempt to obtain and exfiltrate passwords." + ], + "type": "string" + }, + "id": { + "$id": "#/properties/id", + "default": "", + "description": "UUID as unique identifier", + "examples": [ + "fb4c31b0-13e8-4155-8aa5-24de4b8d6717" + ], + "type": "string" + }, + "name": { + "$id": "#/properties/name", + "default": "", + "description": "Name of the Analytics Story", + "examples": [ + "Credential Dumping" + ], + "type": "string" + }, + "narrative": { + "$id": "#/properties/narrative", + "default": "", + "description": "narrative of the analytics story", + "examples": [ + "gathering credentials from a target system, often hashed or encrypted, is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems." + ], + "type": "string" + }, + "search": { + "$id": "#/properties/search", + "default": "", + "description": "An additional Splunk search, which uses the result of the detections", + "examples": [ + "index=asx mitre_id=t1003 | stats values(source) as detections values(process) as processes values(user) as users values(_time) as time count by dest" + ], + "type": "string" + }, + "tags": { + "$id": "#/properties/tags", + "additionalProperties": true, + "default": {}, + "description": "An explanation about the purpose of this instance.", + "examples": [ + { + "analytics_story": "credential_dumping" + } + ], + "minItems": 1, + "type": "object" + }, + "version": { + "$id": "#/properties/version", + "default": 0, + "description": "version of analytics story, e.g. 1 or 2 ...", + "examples": [ + 1 + ], + "type": "integer" + } + }, + "required": [ + "name", + "id", + "version", + "date", + "description", + "narrative", + "author", + "tags" + ], + "title": "Analytics Story Schema", + "type": "object" +} diff --git a/docs/splunk_docs_categories.wiki b/docs/splunk_docs_categories.wiki index ca0c275cb5..c535191979 100644 --- a/docs/splunk_docs_categories.wiki +++ b/docs/splunk_docs_categories.wiki @@ -1311,6 +1311,54 @@ version = 2 +===Suspicious Zoom Child Processes=== + +Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. + +
+
+ +====Narrative==== +Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ +Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. + +====Detections==== +* Detect Prohibited Applications Spawning cmd exe +* First Time Seen Child Process of Zoom + +====Data Models==== +* Endpoint + +====Tags==== + +=====ATT&CK===== +* T1059 +* T1068 + +=====Kill Chain Phases===== +* Actions on Objectives +* Exploitation + +=====CIS===== +* CIS 3 +* CIS 8 + +=====NIST===== +* DE.CM +* PR.IP +* PR.PT + +====References==== +* https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/ +* https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/ + +date = 2020-04-13 + +version = 1 + +
+
+ ===Windows Defense Evasion Tactics=== Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others @@ -1568,7 +1616,6 @@ Monitoring user accounts within your enterprise is a critical analytic function * PR.IP ====References==== -* https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf date = 2017-09-06 @@ -2195,6 +2242,8 @@ Kubernetes is the most used container orchestration platform, this orchestration * Amazon EKS Kubernetes Pod scan detection * Amazon EKS Kubernetes cluster scan detection * GCP Kubernetes cluster scan detection +* Kubernetes Azure pod scan fingerprint +* Kubernetes Azure scan fingerprint ====Data Models==== @@ -2219,6 +2268,82 @@ version = 1 +===Kubernetes Sensitive Object Access Activity=== + +This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. + +
+
+ +====Narrative==== +Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. + +====Detections==== +* Kubernetes Azure detect sensitive object access +* Kubernetes Azure detect service accounts forbidden failure access +* Kubernetes Azure detect suspicious kubectl calls + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Lateral Movement + +=====CIS===== + +=====NIST===== + +====References==== +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + +date = 2020-05-20 + +version = 1 + +
+
+ +===Kubernetes Sensitive Role Activity=== + +This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. + +
+
+ +====Narrative==== +Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities + +====Detections==== +* Kubernetes Azure detect RBAC authorization by account +* Kubernetes Azure detect most active service accounts by pod namespace +* Kubernetes Azure detect sensitive role access + +====Data Models==== + +====Tags==== + +=====ATT&CK===== + +=====Kill Chain Phases===== +* Lateral Movement + +=====CIS===== + +=====NIST===== + +====References==== +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + +date = 2020-05-20 + +version = 1 + +
+
+ ===Suspicious AWS EC2 Activities=== Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. diff --git a/docs/stories_categories.md b/docs/stories_categories.md index 0cf7630e17..8ff9372228 100644 --- a/docs/stories_categories.md +++ b/docs/stories_categories.md @@ -319,6 +319,8 @@ Another search detects incidents wherein a single password is used across multip * [Suspicious WMI Use](#Suspicious-WMI-Use) +* [Suspicious Zoom Child Processes](#Suspicious-Zoom-Child-Processes) + * [Windows Defense Evasion Tactics](#Windows-Defense-Evasion-Tactics) * [Windows Log Manipulation](#Windows-Log-Manipulation) @@ -1236,6 +1238,48 @@ In the event that unauthorized WMI execution occurs, it will be important for an * https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf * https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html +### Suspicious Zoom Child Processes +* id = aa3749a6-49c7-491e-a03f-4eaee5fe0258 +* date = 2020-04-13 +* version = 1 + +#### Description +Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. + +#### Narrative +Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ +Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. + +#### Detections +* Detect Prohibited Applications Spawning cmd exe +* First Time Seen Child Process of Zoom + +#### Data Models +* Endpoint + +#### Mappings + +##### ATT&CK +* T1059 +* T1068 + +##### Kill Chain Phases +* Actions on Objectives +* Exploitation + +###### CIS +* CIS 3 +* CIS 8 + +##### NIST +* DE.CM +* PR.IP +* PR.PT + +##### References +* https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/ +* https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/ + ### Windows Defense Evasion Tactics * id = 56e24a28-5003-4047-b2db-e8f3c4618064 * date = 2018-05-31 @@ -1486,7 +1530,6 @@ Monitoring user accounts within your enterprise is a critical analytic function * PR.IP ##### References -* https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf ### Asset Tracking * id = 91c676cf-0b23-438d-abee-f6335e1fce77 @@ -1753,6 +1796,10 @@ Various legacy protocols operate by default in the clear, without the protection * [Kubernetes Scanning Activity](#Kubernetes-Scanning-Activity) +* [Kubernetes Sensitive Object Access Activity](#Kubernetes-Sensitive-Object-Access-Activity) + +* [Kubernetes Sensitive Role Activity](#Kubernetes-Sensitive-Role-Activity) + * [Suspicious AWS EC2 Activities](#Suspicious-AWS-EC2-Activities) * [Suspicious AWS Login Activities](#Suspicious-AWS-Login-Activities) @@ -2049,6 +2096,8 @@ Kubernetes is the most used container orchestration platform, this orchestration * Amazon EKS Kubernetes Pod scan detection * Amazon EKS Kubernetes cluster scan detection * GCP Kubernetes cluster scan detection +* Kubernetes Azure pod scan fingerprint +* Kubernetes Azure scan fingerprint #### Data Models @@ -2066,6 +2115,70 @@ Kubernetes is the most used container orchestration platform, this orchestration ##### References * https://github.com/splunk/cloud-datamodel-security-research +### Kubernetes Sensitive Object Access Activity +* id = 2574e6d9-7254-4751-8925-0447deeec8ea +* date = 2020-05-20 +* version = 1 + +#### Description +This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. + +#### Narrative +Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. + +#### Detections +* Kubernetes Azure detect sensitive object access +* Kubernetes Azure detect service accounts forbidden failure access +* Kubernetes Azure detect suspicious kubectl calls + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Lateral Movement + +###### CIS + +##### NIST + +##### References +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + +### Kubernetes Sensitive Role Activity +* id = 2574e6d9-7254-4751-8925-0447deeec8ew +* date = 2020-05-20 +* version = 1 + +#### Description +This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. + +#### Narrative +Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities + +#### Detections +* Kubernetes Azure detect RBAC authorization by account +* Kubernetes Azure detect most active service accounts by pod namespace +* Kubernetes Azure detect sensitive role access + +#### Data Models + +#### Mappings + +##### ATT&CK + +##### Kill Chain Phases +* Lateral Movement + +###### CIS + +##### NIST + +##### References +* https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html + ### Suspicious AWS EC2 Activities * id = 2e8948a5-5239-406b-b56b-6c50f1268af3 * date = 2018-02-09 diff --git a/lookups/mitre_enrichment.csv b/lookups/mitre_enrichment.csv index 2ee84a99fd..3d99e2f3e4 100644 --- a/lookups/mitre_enrichment.csv +++ b/lookups/mitre_enrichment.csv @@ -47,91 +47,98 @@ T1222,File and Directory Permissions Modification,Defense Evasion,APT32 T1220,XSL Script Processing,Defense Evasion|Execution,Cobalt Group T1221,Template Injection,Defense Evasion,APT28|Tropic Trooper|Dragonfly 2.0|DarkHydrus T1197,BITS Jobs,Defense Evasion|Persistence,Leviathan +T1217,Browser Bookmark Discovery,Discovery,no T1191,CMSTP,Defense Evasion|Execution,Cobalt Group|MuddyWater +T1207,DCShadow,Defense Evasion,no +T1189,Drive-by Compromise,Initial Access,Darkhotel|APT38|Lazarus Group|Dragonfly 2.0|BRONZE BUTLER|Leafminer|Threat Group-3390|APT19|Dark Caracal|APT32|Elderwood|APT37|Patchwork|PLATINUM T1196,Control Panel Items,Defense Evasion|Execution,no T1214,Credentials in Registry,Credential Access,Soft Cell -T1207,DCShadow,Defense Evasion,no T1213,Data from Information Repositories,Collection,Ke3chang|APT28 -T1212,Exploitation for Credential Access,Credential Access,no -T1217,Browser Bookmark Discovery,Discovery,no -T1190,Exploit Public-Facing Application,Initial Access,Soft Cell|Night Dragon|Axiom -T1210,Exploitation of Remote Services,Lateral Movement,Threat Group-3390|APT28 -T1200,Hardware Additions,Initial Access,no -T1189,Drive-by Compromise,Initial Access,Darkhotel|APT38|Lazarus Group|Dragonfly 2.0|BRONZE BUTLER|Leafminer|APT19|Dark Caracal|Threat Group-3390|APT32|Elderwood|Patchwork|APT37|PLATINUM T1211,Exploitation for Defense Evasion,Defense Evasion,APT28 -T1203,Exploitation for Client Execution,Execution,APT41|admin@338|Threat Group-3390|APT12|The White Company|APT33|APT32|APT28|Tropic Trooper|BRONZE BUTLER|Lazarus Group|Cobalt Group|APT37|APT29|Patchwork|Leviathan|TA459|Elderwood -T1208,Kerberoasting,Credential Access,no +T1203,Exploitation for Client Execution,Execution,APT41|admin@338|Threat Group-3390|APT12|The White Company|APT33|APT32|APT28|Tropic Trooper|BRONZE BUTLER|Lazarus Group|Cobalt Group|APT37|APT29|Patchwork|TA459|Leviathan|Elderwood +T1202,Indirect Command Execution,Defense Evasion,no T1215,Kernel Modules and Extensions,Persistence,no +T1200,Hardware Additions,Initial Access,no +T1208,Kerberoasting,Credential Access,no +T1202,Indirect Command Execution,Defense Evasion,no T1201,Password Policy Discovery,Discovery,OilRig T1205,Port Knocking,Defense Evasion|Persistence|Command And Control,no +T1190,Exploit Public-Facing Application,Initial Access,Soft Cell|Night Dragon|Axiom +T1210,Exploitation of Remote Services,Lateral Movement,Threat Group-3390|APT28 +T1212,Exploitation for Credential Access,Credential Access,no +T1200,Hardware Additions,Initial Access,no +T1208,Kerberoasting,Credential Access,no +T1192,Spearphishing Link,Initial Access,Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|APT28|Cobalt Group|Turla|OilRig|Dragonfly 2.0|APT33|Elderwood|APT29|Leviathan|Magic Hound|FIN8|Patchwork +T1206,Sudo Caching,Privilege Escalation,no +T1199,Trusted Relationship,Initial Access,APT28|menuPass T1198,SIP and Trust Provider Hijacking,Defense Evasion|Persistence,no -T1218,Signed Binary Proxy Execution,Defense Evasion|Execution,TA505|Rancor|Cobalt Group -T1202,Indirect Command Execution,Defense Evasion,no T1194,Spearphishing via Service,Initial Access,FIN6|OilRig|Dark Caracal|Magic Hound +T1192,Spearphishing Link,Initial Access,Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|APT28|Turla|Cobalt Group|Dragonfly 2.0|OilRig|APT33|Elderwood|Patchwork|APT29|Leviathan|Magic Hound|FIN8 T1195,Supply Chain Compromise,Initial Access,APT41|Elderwood T1219,Remote Access Tools,Command And Control,Kimsuky|Night Dragon|Thrip|Cobalt Group|Carbanak T1216,Signed Script Proxy Execution,Defense Evasion|Execution,APT32 -T1193,Spearphishing Attachment,Initial Access,APT41|Machete|admin@338|Kimsuky|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|Tropic Trooper|Turla|Gorgon Group|Rancor|DarkHydrus|Lazarus Group|Cobalt Group|OilRig|APT19|FIN7|BRONZE BUTLER|Dragonfly 2.0|APT32|FIN8|MuddyWater|APT28|TA459|Elderwood|APT29|APT37|Patchwork|Leviathan|menuPass|Magic Hound|PLATINUM +T1193,Spearphishing Attachment,Initial Access,APT41|Machete|admin@338|Kimsuky|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|Tropic Trooper|Turla|Rancor|Gorgon Group|DarkHydrus|Lazarus Group|Cobalt Group|FIN7|APT19|BRONZE BUTLER|OilRig|Dragonfly 2.0|APT32|MuddyWater|FIN8|APT28|TA459|Patchwork|Elderwood|APT29|APT37|Leviathan|Magic Hound|menuPass|PLATINUM +T1218,Signed Binary Proxy Execution,Defense Evasion|Execution,TA505|Rancor|Cobalt Group T1209,Time Providers,Persistence,no -T1204,User Execution,Execution,Machete|admin@338|APT12|TA505|Silence|The White Company|APT39|FIN4|Night Dragon|Darkhotel|Gallmaker|Dragonfly 2.0|APT33|APT19|BRONZE BUTLER|Dark Caracal|Cobalt Group|FIN7|DarkHydrus|Turla|Gorgon Group|OilRig|MuddyWater|Patchwork|Lazarus Group|APT32|Rancor|APT37|APT28|APT29|menuPass|FIN8|TA459|Elderwood|Leviathan|Magic Hound|PLATINUM -T1192,Spearphishing Link,Initial Access,Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|APT28|Turla|Cobalt Group|Dragonfly 2.0|OilRig|APT33|Elderwood|APT29|Leviathan|Patchwork|Magic Hound|FIN8 -T1206,Sudo Caching,Privilege Escalation,no -T1199,Trusted Relationship,Initial Access,APT28|menuPass +T1204,User Execution,Execution,Machete|admin@338|APT12|TA505|Silence|The White Company|APT39|FIN4|Night Dragon|Darkhotel|Gallmaker|APT33|APT19|BRONZE BUTLER|Dragonfly 2.0|Dark Caracal|DarkHydrus|Cobalt Group|FIN7|Turla|Lazarus Group|APT32|Gorgon Group|OilRig|MuddyWater|Patchwork|Rancor|APT28|FIN8|APT37|APT29|menuPass|TA459|Elderwood|Leviathan|Magic Hound|PLATINUM T1182,AppCert DLLs,Persistence|Privilege Escalation,Honeybee -T1176,Browser Extensions,Persistence,Kimsuky|Stolen Pencil T1175,Component Object Model and Distributed COM,Lateral Movement|Execution,MuddyWater -T1181,Extra Window Memory Injection,Defense Evasion|Privilege Escalation,no -T1179,Hooking,Persistence|Privilege Escalation|Credential Access,PLATINUM +T1176,Browser Extensions,Persistence,Kimsuky|Stolen Pencil T1172,Domain Fronting,Command And Control,APT29 -T1187,Forced Authentication,Credential Access,DarkHydrus|Dragonfly 2.0 T1173,Dynamic Data Exchange,Execution,TA505|MuddyWater|Gallmaker|Patchwork|Cobalt Group|APT37|APT28|FIN7 -T1188,Multi-hop Proxy,Command And Control,FIN4|APT29 -T1171,LLMNR/NBT-NS Poisoning and Relay,Credential Access,no -T1177,LSASS Driver,Execution|Persistence,no -T1174,Password Filter DLL,Credential Access,no -T1180,Screensaver,Persistence,no +T1179,Hooking,Persistence|Privilege Escalation|Credential Access,PLATINUM T1183,Image File Execution Options Injection,Privilege Escalation|Persistence|Defense Evasion,TEMP.Veles -T1170,Mshta,Defense Evasion|Execution,Kimsuky|APT32|MuddyWater|FIN7 -T1184,SSH Hijacking,Lateral Movement,no +T1177,LSASS Driver,Execution|Persistence,no T1185,Man in the Browser,Collection,no +T1181,Extra Window Memory Injection,Defense Evasion|Privilege Escalation,no +T1188,Multi-hop Proxy,Command And Control,FIN4|APT29 +T1187,Forced Authentication,Credential Access,DarkHydrus|Dragonfly 2.0 +T1170,Mshta,Defense Evasion|Execution,Kimsuky|APT32|MuddyWater|FIN7 T1186,Process Doppelgänging,Defense Evasion,no +T1171,LLMNR/NBT-NS Poisoning and Relay,Credential Access,no +T1174,Password Filter DLL,Credential Access,no T1178,SID-History Injection,Privilege Escalation,no +T1180,Screensaver,Persistence,no +T1184,SSH Hijacking,Lateral Movement,no T1156,.bash_profile and .bashrc,Persistence,no T1134,Access Token Manipulation,Defense Evasion|Privilege Escalation,Turla|Lazarus Group|APT28 T1155,AppleScript,Execution|Lateral Movement,no T1138,Application Shimming,Persistence|Privilege Escalation,FIN7 T1139,Bash History,Credential Access,no -T1146,Clear Command History,Defense Evasion,APT41 T1136,Create Account,Persistence,APT41|Soft Cell|Dragonfly 2.0|Leafminer|APT3 -T1140,Deobfuscate/Decode Files or Information,Defense Evasion,Turla|WIRTE|Darkhotel|Tropic Trooper|Gorgon Group|Honeybee|menuPass|Threat Group-3390|APT19|Leviathan|MuddyWater|APT28|OilRig|BRONZE BUTLER T1157,Dylib Hijacking,Persistence|Privilege Escalation,no -T1148,HISTCONTROL,Defense Evasion,no -T1147,Hidden Users,Defense Evasion,no -T1143,Hidden Window,Defense Evasion,Gorgon Group|Deep Panda|DarkHydrus|CopyKittens|APT19|APT32|APT28|APT3|Magic Hound +T1146,Clear Command History,Defense Evasion,APT41 +T1140,Deobfuscate/Decode Files or Information,Defense Evasion,Turla|WIRTE|Darkhotel|Tropic Trooper|menuPass|Honeybee|Gorgon Group|Threat Group-3390|APT19|Leviathan|MuddyWater|APT28|OilRig|BRONZE BUTLER T1144,Gatekeeper Bypass,Defense Evasion,no +T1148,HISTCONTROL,Defense Evasion,no T1158,Hidden Files and Directories,Defense Evasion|Persistence,APT32|Tropic Trooper|APT28|Lazarus Group -T1149,LC_MAIN Hijacking,Defense Evasion,no -T1152,Launchctl,Defense Evasion|Execution|Persistence,no -T1168,Local Job Scheduling,Persistence|Execution,no T1141,Input Prompt,Credential Access,FIN4 -T1162,Login Item,Persistence,no -T1137,Office Application Startup,Persistence,APT32|APT28 -T1150,Plist Modification,Defense Evasion|Persistence|Privilege Escalation,no -T1145,Private Keys,Credential Access,no -T1163,Rc.common,Persistence,no -T1142,Keychain,Credential Access,no -T1159,Launch Agent,Persistence,no -T1151,Space after Filename,Defense Evasion|Execution,no T1161,LC_LOAD_DYLIB Addition,Persistence,no T1160,Launch Daemon,Persistence|Privilege Escalation,no -T1153,Source,Execution,no -T1154,Trap,Execution|Persistence,no +T1162,Login Item,Persistence,no +T1137,Office Application Startup,Persistence,APT32|APT28 +T1163,Rc.common,Persistence,no +T1147,Hidden Users,Defense Evasion,no +T1149,LC_MAIN Hijacking,Defense Evasion,no +T1152,Launchctl,Defense Evasion|Execution|Persistence,no +T1142,Keychain,Credential Access,no +T1159,Launch Agent,Persistence,no T1135,Network Share Discovery,Discovery,APT41|Tropic Trooper|APT1|Dragonfly 2.0|Sowbug T1164,Re-opened Applications,Persistence,no -T1169,Sudo,Privilege Escalation,no +T1143,Hidden Window,Defense Evasion,Gorgon Group|Deep Panda|DarkHydrus|CopyKittens|APT19|APT32|APT28|APT3|Magic Hound +T1168,Local Job Scheduling,Persistence|Execution,no +T1150,Plist Modification,Defense Evasion|Persistence|Privilege Escalation,no +T1145,Private Keys,Credential Access,no T1167,Securityd Memory,Credential Access,no T1166,Setuid and Setgid,Privilege Escalation|Persistence,no +T1153,Source,Execution,no +T1164,Re-opened Applications,Persistence,no +T1154,Trap,Execution|Persistence,no T1165,Startup Items,Persistence|Privilege Escalation,no +T1169,Sudo,Privilege Escalation,no +T1153,Source,Execution,no +T1151,Space after Filename,Defense Evasion|Execution,no +T1154,Trap,Execution|Persistence,no T1133,External Remote Services,Persistence|Initial Access,APT41|Soft Cell|TEMP.Veles|Night Dragon|OilRig|Ke3chang|Dragonfly 2.0|FIN5|Threat Group-3390|APT18 T1132,Data Encoding,Command And Control,APT33|APT19|Lazarus Group|BRONZE BUTLER|Patchwork T1131,Authentication Package,Persistence,no @@ -148,19 +155,19 @@ T1121,Regsvcs/Regasm,Defense Evasion|Execution,no T1120,Peripheral Device Discovery,Discovery,APT37|Gamaredon Group|Equation|APT28 T1119,Automated Collection,Collection,APT1|APT28|Patchwork|OilRig|FIN5|Threat Group-3390|FIN6 T1118,InstallUtil,Defense Evasion|Execution,no -T1117,Regsvr32,Defense Evasion|Execution,WIRTE|APT19|Cobalt Group|Leviathan|APT32|Deep Panda +T1117,Regsvr32,Defense Evasion|Execution,WIRTE|Cobalt Group|APT19|Leviathan|APT32|Deep Panda T1116,Code Signing,Defense Evasion,APT41|FIN6|TA505|FIN7|Honeybee|APT37|Leviathan|CopyKittens|Winnti Group|Suckfly|Molerats|Darkhotel T1115,Clipboard Data,Collection,APT38 -T1114,Email Collection,Collection,FIN4|Dragonfly 2.0|APT28|Magic Hound|Ke3chang|Leafminer|APT1 +T1114,Email Collection,Collection,FIN4|APT28|Magic Hound|Ke3chang|Dragonfly 2.0|Leafminer|APT1 T1113,Screen Capture,Collection,Silence|MuddyWater|OilRig|Dragonfly 2.0|Dark Caracal|FIN7|BRONZE BUTLER|Magic Hound|Group5|APT28 -T1112,Modify Registry,Defense Evasion,APT41|Turla|APT32|APT38|Dragonfly 2.0|Threat Group-3390|Patchwork|APT19|Honeybee|Gorgon Group|FIN8 +T1112,Modify Registry,Defense Evasion,APT41|Turla|APT32|APT38|Dragonfly 2.0|Patchwork|APT19|Threat Group-3390|Honeybee|Gorgon Group|FIN8 T1111,Two-Factor Authentication Interception,Credential Access,no T1110,Brute Force,Credential Access,APT41|APT33|Leafminer|OilRig|Dragonfly 2.0|APT3|Lazarus Group|Turla T1109,Component Firmware,Defense Evasion|Persistence,Equation T1108,Redundant Access,Defense Evasion|Persistence,Stolen Pencil|Cobalt Group|Leafminer|APT3|FIN5|OilRig|Threat Group-3390 -T1107,File Deletion,Defense Evasion,APT41|Kimsuky|Silence|The White Company|TEMP.Veles|APT32|APT38|Honeybee|Patchwork|Dragonfly 2.0|menuPass|Cobalt Group|FIN8|OilRig|FIN5|BRONZE BUTLER|Magic Hound|APT3|FIN10|Threat Group-3390|APT28|Group5|Lazarus Group|APT18|APT29 +T1107,File Deletion,Defense Evasion,APT41|Kimsuky|Silence|The White Company|TEMP.Veles|APT32|APT38|Honeybee|Patchwork|menuPass|Cobalt Group|Dragonfly 2.0|FIN8|OilRig|FIN5|Magic Hound|BRONZE BUTLER|APT3|FIN10|APT28|Threat Group-3390|Group5|Lazarus Group|APT18|APT29 T1106,Execution through API,Execution,Turla|Silence|APT37|Gorgon Group -T1105,Remote File Copy,Command And Control|Lateral Movement,Soft Cell|TA505|WIRTE|APT33|MuddyWater|APT18|APT38|Turla|Rancor|Gorgon Group|Cobalt Group|Dragonfly 2.0|OilRig|APT37|FIN8|PLATINUM|Leviathan|Elderwood|Magic Hound|APT3|APT32|BRONZE BUTLER|FIN7|FIN10|menuPass|Gamaredon Group|Patchwork|Lazarus Group|Threat Group-3390|APT28 +T1105,Remote File Copy,Command And Control|Lateral Movement,Soft Cell|TA505|WIRTE|APT33|MuddyWater|APT18|APT38|Gorgon Group|Cobalt Group|Rancor|Turla|Dragonfly 2.0|OilRig|APT37|FIN8|PLATINUM|Leviathan|Elderwood|APT3|Magic Hound|APT32|BRONZE BUTLER|FIN7|FIN10|menuPass|Gamaredon Group|Patchwork|Lazarus Group|Threat Group-3390|APT28 T1104,Multi-Stage Channels,Command And Control,MuddyWater|APT3 T1103,AppInit DLLs,Persistence|Privilege Escalation,no T1102,Web Service,Command And Control|Defense Evasion,APT41|APT12|FIN6|Turla|FIN7|BRONZE BUTLER|Leviathan|APT37|Magic Hound|RTM|Patchwork|Carbanak @@ -179,43 +186,43 @@ T1090,Connection Proxy,Command And Control|Defense Evasion,APT41|Soft Cell|Turla T1089,Disabling Security Tools,Defense Evasion,Kimsuky|Turla|Night Dragon|Dragonfly 2.0|Gorgon Group|Threat Group-3390|Lazarus Group|Putter Panda|Carbanak T1088,Bypass User Account Control,Defense Evasion|Privilege Escalation,APT37|MuddyWater|Honeybee|Threat Group-3390|Cobalt Group|BRONZE BUTLER|Patchwork|APT29 T1087,Account Discovery,Discovery,APT32|APT1|Dragonfly 2.0|BRONZE BUTLER|OilRig|Threat Group-3390|menuPass|FIN6|Poseidon Group|APT3|admin@338|Ke3chang -T1086,PowerShell,Execution,APT41|Kimsuky|Soft Cell|TA505|WIRTE|TEMP.Veles|APT33|Gallmaker|Turla|DarkHydrus|APT19|APT28|Thrip|Dragonfly 2.0|Cobalt Group|Gorgon Group|Leviathan|TA459|MuddyWater|FIN8|CopyKittens|OilRig|Magic Hound|BRONZE BUTLER|APT32|FIN10|FIN7|Threat Group-3390|menuPass|Patchwork|Stealth Falcon|FIN6|Poseidon Group|APT3|APT29|Deep Panda +T1086,PowerShell,Execution,APT41|Kimsuky|Soft Cell|TA505|WIRTE|TEMP.Veles|APT33|Gallmaker|Turla|Dragonfly 2.0|APT19|Thrip|DarkHydrus|APT28|Cobalt Group|Gorgon Group|Leviathan|TA459|MuddyWater|FIN8|CopyKittens|OilRig|BRONZE BUTLER|Magic Hound|APT32|FIN10|FIN7|Threat Group-3390|menuPass|Patchwork|Stealth Falcon|FIN6|Poseidon Group|APT3|APT29|Deep Panda T1085,Rundll32,Defense Evasion|Execution,TA505|MuddyWater|APT29|APT19|CopyKittens|APT3|Carbanak|APT28 T1084,Windows Management Instrumentation Event Subscription,Persistence,Turla|Leviathan|APT29 -T1083,File and Directory Discovery,Discovery,Kimsuky|APT32|MuddyWater|APT18|Leafminer|Dragonfly 2.0|Honeybee|Dark Caracal|Magic Hound|APT3|Sowbug|BRONZE BUTLER|APT28|Patchwork|Lazarus Group|Dust Storm|admin@338|Turla|Ke3chang -T1082,System Information Discovery,Discovery,Kimsuky|Tropic Trooper|Darkhotel|MuddyWater|APT18|APT37|Honeybee|APT19|APT32|Magic Hound|OilRig|APT3|Sowbug|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|admin@338|Turla|Ke3chang +T1083,File and Directory Discovery,Discovery,Kimsuky|APT32|MuddyWater|APT18|Dragonfly 2.0|Dark Caracal|Leafminer|Honeybee|Magic Hound|BRONZE BUTLER|APT3|Sowbug|APT28|Patchwork|Lazarus Group|Dust Storm|admin@338|Turla|Ke3chang +T1082,System Information Discovery,Discovery,Kimsuky|Tropic Trooper|Darkhotel|MuddyWater|APT18|APT37|Honeybee|APT19|APT32|APT3|OilRig|Magic Hound|Sowbug|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|admin@338|Turla|Ke3chang T1081,Credentials in Files,Credential Access,OilRig|Kimsuky|Turla|TA505|Stolen Pencil|MuddyWater|APT3 T1080,Taint Shared Content,Lateral Movement,Darkhotel T1079,Multilayer Encryption,Command And Control,no -T1078,Valid Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,APT41|Soft Cell|TEMP.Veles|APT39|Stolen Pencil|FIN4|Night Dragon|Dragonfly 2.0|FIN8|Leviathan|APT33|APT3|FIN5|OilRig|menuPass|APT28|FIN10|APT32|Suckfly|FIN6|Threat Group-1314|Threat Group-3390|APT18|PittyTiger|Carbanak +T1078,Valid Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,APT41|Soft Cell|TEMP.Veles|APT39|Stolen Pencil|FIN4|Night Dragon|Dragonfly 2.0|FIN8|APT33|Leviathan|APT3|FIN5|OilRig|menuPass|APT28|FIN10|APT32|Suckfly|FIN6|Threat Group-1314|Threat Group-3390|APT18|PittyTiger|Carbanak T1077,Windows Admin Shares,Lateral Movement,APT32|Orangeworm|FIN8|APT3|Lazarus Group|Threat Group-1314|Turla|Deep Panda|Ke3chang T1076,Remote Desktop Protocol,Lateral Movement,APT41|TEMP.Veles|Leviathan|APT39|Stolen Pencil|Cobalt Group|Dragonfly 2.0|FIN8|APT3|OilRig|menuPass|FIN10|Patchwork|FIN6|Lazarus Group|APT1|Axiom T1075,Pass the Hash,Lateral Movement,Soft Cell|APT32|Night Dragon|APT28|APT1 -T1074,Data Staged,Collection,Machete|Soft Cell|TEMP.Veles|Night Dragon|Honeybee|Patchwork|Dragonfly 2.0|Leviathan|FIN8|APT3|FIN5|menuPass|FIN6|Lazarus Group|Threat Group-3390|APT28 +T1074,Data Staged,Collection,Machete|Soft Cell|TEMP.Veles|Night Dragon|Patchwork|Honeybee|Dragonfly 2.0|Leviathan|FIN8|APT3|FIN5|menuPass|FIN6|Lazarus Group|Threat Group-3390|APT28 T1073,DLL Side-Loading,Defense Evasion,APT41|Soft Cell|Tropic Trooper|Patchwork|APT19|APT32|APT3|menuPass|Threat Group-3390 T1072,Third-party Software,Execution|Lateral Movement,Threat Group-1314 -T1071,Standard Application Layer Protocol,Command And Control,APT41|Machete|WIRTE|APT33|FIN4|Night Dragon|APT18|SilverTerrier|APT38|Dragonfly 2.0|APT19|Cobalt Group|FIN7|Threat Group-3390|Turla|APT37|Rancor|Orangeworm|Honeybee|Ke3chang|Dark Caracal|Lazarus Group|BRONZE BUTLER|APT32|OilRig|Magic Hound|Gamaredon Group|Stealth Falcon|FIN6|APT28 +T1071,Standard Application Layer Protocol,Command And Control,APT41|Machete|WIRTE|APT33|FIN4|Night Dragon|APT18|SilverTerrier|APT38|APT19|Dragonfly 2.0|Cobalt Group|Threat Group-3390|FIN7|Turla|Honeybee|APT37|Rancor|Orangeworm|Ke3chang|Dark Caracal|Lazarus Group|BRONZE BUTLER|Magic Hound|OilRig|APT32|Gamaredon Group|Stealth Falcon|FIN6|APT28 T1070,Indicator Removal on Host,Defense Evasion,APT41|APT29|APT38|Dragonfly 2.0|APT32|FIN8|FIN5|APT28 T1069,Permission Groups Discovery,Discovery,FIN6|Dragonfly 2.0|OilRig|APT3|admin@338|Ke3chang T1068,Exploitation for Privilege Escalation,Privilege Escalation,APT33|Cobalt Group|PLATINUM|FIN8|APT32|Threat Group-3390|FIN6|APT28 T1067,Bootkit,Persistence,APT41|Lazarus Group|APT28 T1066,Indicator Removal from Tools,Defense Evasion,Soft Cell|TEMP.Veles|Patchwork|APT3|Turla|OilRig|Deep Panda T1065,Uncommonly Used Port,Command And Control,TEMP.Veles|APT33|APT32|Gorgon Group|Magic Hound|Group5|Lazarus Group|APT3 -T1064,Scripting,Defense Evasion|Execution,Machete|Turla|TA505|Silence|WIRTE|APT39|FIN4|APT32|Darkhotel|Gallmaker|Dark Caracal|Lazarus Group|menuPass|APT19|Dragonfly 2.0|Leafminer|Rancor|Honeybee|APT37|Ke3chang|Cobalt Group|Patchwork|FIN7|Gorgon Group|MuddyWater|Leviathan|FIN8|TA459|APT28|Magic Hound|OilRig|FIN5|BRONZE BUTLER|FIN10|Gamaredon Group|Stealth Falcon|FIN6|APT3|APT29|Deep Panda|APT1 +T1064,Scripting,Defense Evasion|Execution,Machete|Turla|TA505|Silence|WIRTE|APT39|FIN4|APT32|Darkhotel|Gallmaker|Dark Caracal|Lazarus Group|menuPass|Leafminer|Rancor|Cobalt Group|APT19|Dragonfly 2.0|Honeybee|Ke3chang|Patchwork|FIN7|APT37|Gorgon Group|MuddyWater|Leviathan|TA459|FIN8|APT28|Magic Hound|OilRig|BRONZE BUTLER|FIN5|FIN10|Gamaredon Group|Stealth Falcon|FIN6|APT3|APT29|Deep Panda|APT1 T1063,Security Software Discovery,Discovery,The White Company|Cobalt Group|Darkhotel|MuddyWater|Tropic Trooper|FIN8|Patchwork|Naikon T1062,Hypervisor,Persistence,no T1061,Graphical User Interface,Execution,APT3 -T1060,Registry Run Keys / Startup Folder,Persistence,APT41|Machete|Kimsuky|APT33|APT39|APT32|APT18|Turla|APT19|Dark Caracal|Ke3chang|Dragonfly 2.0|Cobalt Group|Honeybee|Threat Group-3390|Gorgon Group|MuddyWater|APT37|Leviathan|BRONZE BUTLER|APT3|Magic Hound|FIN10|FIN7|Patchwork|FIN6|Lazarus Group|Putter Panda|APT29|Darkhotel -T1059,Command-Line Interface,Execution,APT41|Soft Cell|Turla|Silence|APT32|Cobalt Group|MuddyWater|APT18|APT38|Dragonfly 2.0|Gorgon Group|APT28|FIN7|Rancor|Honeybee|APT37|Leviathan|FIN8|Magic Hound|Sowbug|OilRig|BRONZE BUTLER|menuPass|Threat Group-3390|Suckfly|Patchwork|Lazarus Group|Threat Group-1314|APT3|admin@338|APT1|Ke3chang +T1060,Registry Run Keys / Startup Folder,Persistence,APT41|Machete|Kimsuky|APT33|APT39|APT32|APT18|Turla|Threat Group-3390|APT19|Cobalt Group|Honeybee|Dark Caracal|Ke3chang|Dragonfly 2.0|Gorgon Group|MuddyWater|APT37|Leviathan|APT3|BRONZE BUTLER|Magic Hound|FIN10|FIN7|Patchwork|FIN6|Lazarus Group|Putter Panda|APT29|Darkhotel +T1059,Command-Line Interface,Execution,APT41|Soft Cell|Turla|Silence|APT32|Cobalt Group|MuddyWater|APT18|APT38|Dragonfly 2.0|APT28|Gorgon Group|Honeybee|FIN7|Rancor|APT37|Leviathan|FIN8|Magic Hound|BRONZE BUTLER|Sowbug|OilRig|menuPass|Threat Group-3390|Suckfly|Patchwork|Lazarus Group|Threat Group-1314|APT3|admin@338|APT1|Ke3chang T1058,Service Registry Permissions Weakness,Persistence|Privilege Escalation,no T1057,Process Discovery,Discovery,Darkhotel|MuddyWater|APT1|APT38|Tropic Trooper|APT37|Honeybee|OilRig|APT3|Magic Hound|APT28|Winnti Group|Stealth Falcon|Poseidon Group|Lazarus Group|Molerats|Turla|Deep Panda|Ke3chang -T1056,Input Capture,Collection|Credential Access,APT41|Kimsuky|menuPass|Stolen Pencil|FIN4|APT38|OilRig|Ke3chang|PLATINUM|Sowbug|Magic Hound|Group5|Lazarus Group|Threat Group-3390|APT3|Darkhotel|APT28 -T1055,Process Injection,Defense Evasion|Privilege Escalation,APT41|Kimsuky|Tropic Trooper|Gorgon Group|Turla|Threat Group-3390|APT37|Cobalt Group|Honeybee|Lazarus Group|PLATINUM|Putter Panda +T1056,Input Capture,Collection|Credential Access,APT41|Kimsuky|menuPass|Stolen Pencil|FIN4|APT38|OilRig|Ke3chang|PLATINUM|Magic Hound|Sowbug|Group5|Lazarus Group|Threat Group-3390|APT3|Darkhotel|APT28 +T1055,Process Injection,Defense Evasion|Privilege Escalation,APT41|Kimsuky|Tropic Trooper|Threat Group-3390|APT37|Gorgon Group|Turla|Cobalt Group|Honeybee|Lazarus Group|PLATINUM|Putter Panda T1054,Indicator Blocking,Defense Evasion,no -T1053,Scheduled Task,Execution|Persistence|Privilege Escalation,APT41|Machete|Soft Cell|Silence|TEMP.Veles|APT33|APT39|Cobalt Group|Dragonfly 2.0|OilRig|Rancor|Patchwork|FIN8|BRONZE BUTLER|menuPass|FIN10|APT32|FIN7|Stealth Falcon|FIN6|Threat Group-3390|APT18|APT3|APT29 +T1053,Scheduled Task,Execution|Persistence|Privilege Escalation,APT41|Machete|Soft Cell|Silence|TEMP.Veles|APT33|APT39|Cobalt Group|Dragonfly 2.0|Patchwork|OilRig|Rancor|FIN8|BRONZE BUTLER|menuPass|FIN10|APT32|FIN7|Stealth Falcon|FIN6|Threat Group-3390|APT18|APT3|APT29 T1052,Exfiltration Over Physical Medium,Exfiltration,no T1051,Shared Webroot,Lateral Movement,no -T1050,New Service,Persistence|Privilege Escalation,Kimsuky|Tropic Trooper|Cobalt Group|Ke3chang|FIN7|APT32|Threat Group-3390|APT3|Lazarus Group|Carbanak +T1050,New Service,Persistence|Privilege Escalation,Kimsuky|Tropic Trooper|Cobalt Group|Threat Group-3390|Ke3chang|FIN7|APT32|APT3|Lazarus Group|Carbanak T1049,System Network Connections Discovery,Discovery,APT41|APT38|Soft Cell|APT32|APT1|OilRig|APT3|menuPass|Threat Group-3390|Poseidon Group|admin@338|Turla|Ke3chang T1048,Exfiltration Over Alternative Protocol,Exfiltration,Turla|APT33|Thrip|FIN8|OilRig|Lazarus Group T1047,Windows Management Instrumentation,Execution,APT41|FIN6|Soft Cell|APT32|MuddyWater|OilRig|Threat Group-3390|Leviathan|FIN8|menuPass|Stealth Falcon|Lazarus Group|APT29|Deep Panda @@ -238,16 +245,16 @@ T1031,Modify Existing Service,Persistence,APT41|APT32|Honeybee|APT19 T1030,Data Transfer Size Limits,Exfiltration,Threat Group-3390 T1029,Scheduled Transfer,Exfiltration,no T1028,Windows Remote Management,Execution|Lateral Movement,Threat Group-3390 -T1027,Obfuscated Files or Information,Defense Evasion,Machete|Soft Cell|Turla|TA505|Silence|APT33|Night Dragon|Darkhotel|Gallmaker|APT29|APT18|Tropic Trooper|menuPass|Patchwork|Leafminer|Cobalt Group|APT37|Threat Group-3390|Honeybee|Dark Caracal|APT19|FIN8|BlackOasis|Elderwood|Leviathan|MuddyWater|FIN7|Magic Hound|APT3|OilRig|APT32|Group5|Dust Storm|Lazarus Group|Putter Panda|APT28 +T1027,Obfuscated Files or Information,Defense Evasion,Machete|Soft Cell|Turla|TA505|Silence|APT33|Night Dragon|Darkhotel|Gallmaker|APT29|APT18|Tropic Trooper|menuPass|Patchwork|Leafminer|Honeybee|Cobalt Group|APT37|Threat Group-3390|Dark Caracal|APT19|FIN8|BlackOasis|Leviathan|Elderwood|FIN7|MuddyWater|Magic Hound|APT3|OilRig|APT32|Group5|Dust Storm|Lazarus Group|Putter Panda|APT28 T1026,Multiband Communication,Command And Control,Lazarus Group T1025,Data from Removable Media,Collection,Machete|Turla|Gamaredon Group|APT28 T1024,Custom Cryptographic Protocol,Command And Control,APT28|BRONZE BUTLER|Lazarus Group -T1023,Shortcut Modification,Persistence,APT39|Darkhotel|APT29|FIN7|Gorgon Group|Dragonfly 2.0|Leviathan|Lazarus Group +T1023,Shortcut Modification,Persistence,APT39|Darkhotel|APT29|Gorgon Group|FIN7|Dragonfly 2.0|Leviathan|Lazarus Group T1022,Data Encrypted,Exfiltration,Kimsuky|Soft Cell|Turla|menuPass|APT32|Patchwork|Honeybee|CopyKittens|BRONZE BUTLER|FIN6|Lazarus Group|Threat Group-3390|Ke3chang T1021,Remote Services,Lateral Movement,TEMP.Veles|Leviathan|APT39|OilRig|menuPass|GCMAN T1020,Automated Exfiltration,Exfiltration,Honeybee T1019,System Firmware,Persistence,no -T1018,Remote System Discovery,Discovery,Soft Cell|APT32|Threat Group-3390|Dragonfly 2.0|Deep Panda|Ke3chang|Leafminer|FIN8|APT3|FIN5|BRONZE BUTLER|menuPass|FIN6|Turla +T1018,Remote System Discovery,Discovery,Soft Cell|APT32|Threat Group-3390|Dragonfly 2.0|Deep Panda|Ke3chang|Leafminer|FIN8|FIN5|APT3|BRONZE BUTLER|menuPass|FIN6|Turla T1017,Application Deployment Software,Lateral Movement,APT32 T1016,System Network Configuration Discovery,Discovery,APT41|Soft Cell|APT39|APT32|Darkhotel|MuddyWater|APT1|APT19|Dragonfly 2.0|OilRig|Magic Hound|menuPass|Threat Group-3390|Stealth Falcon|Lazarus Group|APT3|Naikon|admin@338|Turla|Ke3chang T1015,Accessibility Features,Persistence|Privilege Escalation,APT41|APT3|APT29|Deep Panda|Axiom @@ -260,8 +267,8 @@ T1009,Binary Padding,Defense Evasion,Patchwork|APT32|Leviathan|BRONZE BUTLER|Moa T1008,Fallback Channels,Command And Control,APT41|OilRig|Lazarus Group T1007,System Service Discovery,Discovery,APT1|OilRig|Poseidon Group|admin@338|Turla|Ke3chang T1006,File System Logical Offsets,Defense Evasion,no -T1005,Data from Local System,Collection,Kimsuky|Soft Cell|Turla|menuPass|Dragonfly 2.0|Dark Caracal|Honeybee|APT37|APT28|APT3|BRONZE BUTLER|Patchwork|Stealth Falcon|Lazarus Group|Dust Storm|Threat Group-3390|APT1|Ke3chang +T1005,Data from Local System,Collection,Kimsuky|Soft Cell|Turla|menuPass|Dark Caracal|Dragonfly 2.0|Honeybee|APT37|APT28|APT3|BRONZE BUTLER|Patchwork|Stealth Falcon|Lazarus Group|Dust Storm|Threat Group-3390|APT1|Ke3chang T1004,Winlogon Helper DLL,Persistence,Tropic Trooper|Turla -T1003,Credential Dumping,Credential Access,APT41|Soft Cell|TEMP.Veles|APT33|Leviathan|APT39|Stolen Pencil|APT32|Night Dragon|Dragonfly 2.0|Leafminer|Lazarus Group|Magic Hound|APT37|MuddyWater|PLATINUM|FIN8|Sowbug|BRONZE BUTLER|FIN5|OilRig|menuPass|Strider|Patchwork|Stealth Falcon|Suckfly|FIN6|Poseidon Group|Threat Group-3390|APT3|Molerats|APT28|APT1|Ke3chang|Cleaver|Axiom -T1002,Data Compressed,Exfiltration,APT41|Soft Cell|Gallmaker|APT33|APT32|APT39|MuddyWater|Honeybee|Magic Hound|APT28|Dragonfly 2.0|FIN8|BRONZE BUTLER|CopyKittens|Sowbug|APT3|menuPass|FIN6|Lazarus Group|Threat Group-3390|APT1|Ke3chang +T1003,Credential Dumping,Credential Access,APT41|Soft Cell|TEMP.Veles|APT33|Leviathan|APT39|Stolen Pencil|APT32|Night Dragon|Dragonfly 2.0|Leafminer|Lazarus Group|Magic Hound|APT37|PLATINUM|MuddyWater|FIN8|Sowbug|BRONZE BUTLER|OilRig|FIN5|menuPass|Strider|Patchwork|Stealth Falcon|Suckfly|FIN6|Poseidon Group|Threat Group-3390|APT3|Molerats|APT28|APT1|Ke3chang|Cleaver|Axiom +T1002,Data Compressed,Exfiltration,APT41|Soft Cell|Gallmaker|APT33|APT32|APT39|MuddyWater|Honeybee|APT28|Magic Hound|Dragonfly 2.0|FIN8|BRONZE BUTLER|CopyKittens|Sowbug|APT3|menuPass|FIN6|Lazarus Group|Threat Group-3390|APT1|Ke3chang T1001,Data Obfuscation,Command And Control,APT28|Axiom diff --git a/lookups/prohibited_apps_launching_cmd.csv b/lookups/prohibited_apps_launching_cmd.csv index 01edda483b..5e4a2cf651 100644 --- a/lookups/prohibited_apps_launching_cmd.csv +++ b/lookups/prohibited_apps_launching_cmd.csv @@ -14,3 +14,4 @@ firefox.exe,prohibited java.exe,prohibited powershell.exe,prohibited mshta.exe, prohibited +zoom.exe,prohibitied diff --git a/lookups/zoom_first_time_child_process.yml b/lookups/zoom_first_time_child_process.yml new file mode 100644 index 0000000000..a55dc842f0 --- /dev/null +++ b/lookups/zoom_first_time_child_process.yml @@ -0,0 +1,4 @@ +description: A list of suspicious file names +collection: zoom_first_time_child_process +name: zoom_first_time_child_process +fields_list: _key, dest, process_name, firstTimeSeen, lastTimeSeen diff --git a/macros/kubernetes_azure.yml b/macros/kubernetes_azure.yml new file mode 100644 index 0000000000..2a23d362e5 --- /dev/null +++ b/macros/kubernetes_azure.yml @@ -0,0 +1,3 @@ +definition: sourcetype=mscs:storage:blob:json +description: customer specific splunk configurations(eg- index, source, sourcetype) for Kubernetes data from Azure. Replace the macro definition with configurations for your Splunk Environmnent. +name: kubernetes_azure diff --git a/macros/kubernetes_azure_detect_RBAC_authorization_by_account.yml b/macros/kubernetes_azure_detect_RBAC_authorization_by_account.yml new file mode 100644 index 0000000000..964e8e2e04 --- /dev/null +++ b/macros/kubernetes_azure_detect_RBAC_authorization_by_account.yml @@ -0,0 +1,3 @@ +definition: search * +description: Use this macro to add additional filters +name: kubernetes_azure_detect_rbac_authorization_by_account_filter diff --git a/macros/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml b/macros/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml new file mode 100644 index 0000000000..86245d47a6 --- /dev/null +++ b/macros/kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace.yml @@ -0,0 +1,3 @@ +definition: search * +description: Use this macro to add additional filters +name: kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter diff --git a/macros/kubernetes_azure_detect_sensitive_object_access.yml b/macros/kubernetes_azure_detect_sensitive_object_access.yml new file mode 100644 index 0000000000..dce2bd7be8 --- /dev/null +++ b/macros/kubernetes_azure_detect_sensitive_object_access.yml @@ -0,0 +1,3 @@ +definition: search * +description: Use this macro to add additional filters +name: kubernetes_azure_detect_sensitive_object_access_filter diff --git a/macros/kubernetes_azure_detect_sensitive_role_access.yml b/macros/kubernetes_azure_detect_sensitive_role_access.yml new file mode 100644 index 0000000000..54bf94d705 --- /dev/null +++ b/macros/kubernetes_azure_detect_sensitive_role_access.yml @@ -0,0 +1,3 @@ +definition: search * +description: Use this macro to add additional filters +name: kubernetes_azure_detect_sensitive_role_access_filter diff --git a/macros/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml b/macros/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml new file mode 100644 index 0000000000..0483b01baa --- /dev/null +++ b/macros/kubernetes_azure_detect_service_accounts_forbidden_failure_access.yml @@ -0,0 +1,3 @@ +definition: search * +description: Use this macro to add additional filters +name: kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter diff --git a/macros/kubernetes_azure_detect_suspicious_kubectl_calls.yml b/macros/kubernetes_azure_detect_suspicious_kubectl_calls.yml new file mode 100644 index 0000000000..9f57d71281 --- /dev/null +++ b/macros/kubernetes_azure_detect_suspicious_kubectl_calls.yml @@ -0,0 +1,3 @@ +definition: search * +description: Use this macro to add additional filters +name: kubernetes_azure_detect_suspicious_kubectl_calls_filter diff --git a/macros/kubernetes_azure_pod_scan_fingerprint_detection_filter.yml b/macros/kubernetes_azure_pod_scan_fingerprint_detection_filter.yml new file mode 100644 index 0000000000..0b2ca96cbb --- /dev/null +++ b/macros/kubernetes_azure_pod_scan_fingerprint_detection_filter.yml @@ -0,0 +1,3 @@ +definition: search * +description: Use this macro to add additional filters +name: kubernetes_azure_pod_scan_fingerprint_detection_filter diff --git a/macros/kubernetes_azure_scan_fingerprint_filter.yml b/macros/kubernetes_azure_scan_fingerprint_filter.yml new file mode 100644 index 0000000000..caed65bae0 --- /dev/null +++ b/macros/kubernetes_azure_scan_fingerprint_filter.yml @@ -0,0 +1,3 @@ +definition: search * +description: Use this macro to add additional filters +name: kubernetes_azure_scan_fingerprint_filter diff --git a/macros/previously_seen_zoom_child_processes_forget_window.yml b/macros/previously_seen_zoom_child_processes_forget_window.yml new file mode 100644 index 0000000000..59896edb25 --- /dev/null +++ b/macros/previously_seen_zoom_child_processes_forget_window.yml @@ -0,0 +1,3 @@ +description: Use this macro to determine how long to keep track of zoom child processes +definition: -90d@d +name: previously_seen_zoom_child_processes_forget_window \ No newline at end of file diff --git a/macros/previously_seen_zoom_child_processes_window.yml b/macros/previously_seen_zoom_child_processes_window.yml new file mode 100644 index 0000000000..b740f1e4be --- /dev/null +++ b/macros/previously_seen_zoom_child_processes_window.yml @@ -0,0 +1,3 @@ +description: Use this macro to determine how far back you should be checking for new zoom child processes +definition: -90d@d +name: previously_seen_zoom_child_processes_window \ No newline at end of file diff --git a/package/app.manifest b/package/app.manifest index b8cecfff26..c1699659fe 100644 --- a/package/app.manifest +++ b/package/app.manifest @@ -5,7 +5,7 @@ "id": { "group": null, "name": "DA-ESS-ContentUpdate", - "version": "1.0.54" + "version": "3.0.1" }, "author": [ { diff --git a/package/default/analytic_stories.conf b/package/default/analytic_stories.conf index c53d061c85..a3ae627c54 100644 --- a/package/default/analytic_stories.conf +++ b/package/default/analytic_stories.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-25T14:45:46 UTC +# On Date: 2020-06-04T22:46:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -16,7 +16,7 @@ version = 1 reference = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/"] detection_searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule"] mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By AccessKeyId"] +investigative_searches = ["ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User"] support_searches = ["ESCU - Previously Seen AWS Cross Account Activity"] data_models = [] providing_technologies = none @@ -34,8 +34,8 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule"] mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] -support_searches = ["ESCU - Previously Seen EC2 Launches By User", "ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 Instance Types", "ESCU - Previously Seen EC2 AMIs"] +investigative_searches = ["ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN"] +support_searches = ["ESCU - Previously Seen EC2 Instance Types", "ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 Launches By User", "ESCU - Previously Seen EC2 AMIs"] data_models = [] providing_technologies = none description = Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. @@ -53,8 +53,8 @@ version = 2 reference = ["https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html", "https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/"] detection_searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule"] mappings = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC"]} -investigative_searches = ["ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] -support_searches = ["ESCU - Baseline of blocked outbound traffic from AWS", "ESCU - Baseline of Network ACL Activity by ARN"] +investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN"] +support_searches = ["ESCU - Baseline of Network ACL Activity by ARN", "ESCU - Baseline of blocked outbound traffic from AWS"] data_models = [] providing_technologies = none description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. @@ -69,7 +69,7 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule"] mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -investigative_searches = ["ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From City"] +investigative_searches = ["ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From IP Address"] support_searches = ["ESCU - Previously Seen AWS Provisioning Activity Sources"] data_models = [] providing_technologies = none @@ -86,8 +86,8 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"] detection_searches = ["ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect new API calls from user roles - Rule"] mappings = {"cis20": ["CIS 1", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM", "DE.DP", "ID.AM", "PR.AC"]} -investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Investigate AWS User Activities by user field"] -support_searches = ["ESCU - Baseline of API Calls per User ARN", "ESCU - Baseline of Security Group Activity by ARN", "ESCU - Previously seen API call per user roles in CloudTrail", "ESCU - Create a list of approved AWS service accounts"] +investigative_searches = ["ESCU - Investigate AWS User Activities by user field", "ESCU - Get Notable History", "ESCU - Get Notable Info"] +support_searches = ["ESCU - Baseline of Security Group Activity by ARN", "ESCU - Previously seen API call per user roles in CloudTrail", "ESCU - Baseline of API Calls per User ARN", "ESCU - Create a list of approved AWS service accounts"] data_models = [] providing_technologies = none description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. @@ -105,7 +105,7 @@ version = 1 reference = [] detection_searches = ["ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule"] mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078", "T1136"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Logon Rights Modifications For Endpoint"] support_searches = [] data_models = ["Change"] providing_technologies = none @@ -121,7 +121,7 @@ version = 1 reference = ["https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf"] detection_searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule"] mappings = {"cis20": ["CIS 12", "CIS 18", "CIS 3", "CIS 4"], "kill_chain_phases": ["Actions on Objectives", "Delivery", "Exploitation"], "mitre_attack": ["T1082"], "nist": ["DE.AE", "DE.CM", "ID.RA", "PR.IP", "PR.MA", "PR.PT", "RS.MI"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web POSTs From src", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Investigate Web POSTs From src", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -150,7 +150,7 @@ version = 1 reference = ["https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/"] detection_searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule"] mappings = {"cis20": ["CIS 1"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS"]} -investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address"] +investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address"] support_searches = ["ESCU - Count of assets by category"] data_models = ["Network_Sessions"] providing_technologies = none @@ -166,7 +166,7 @@ version = 1 reference = ["https://www.zerofox.com/blog/what-is-digital-risk-monitoring/", "https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/", "https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/"] detection_searches = ["ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule"] mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives", "Delivery"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Emails From Specific Sender", "ESCU - Get Email Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] support_searches = ["ESCU - DNSTwist Domain Names"] data_models = ["Email", "Network_Resolution", "Web"] providing_technologies = none @@ -184,8 +184,8 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule"] mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Get Notable History", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] -support_searches = ["ESCU - Previously Seen Cloud Compute Images", "ESCU - Previously Seen Cloud Regions", "ESCU - Previously Seen Cloud Compute Creations By User", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen Cloud Compute Instance Types"] +investigative_searches = ["ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Investigate User Activities In Single Cloud Region"] +support_searches = ["ESCU - Previously Seen Cloud Regions", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen Cloud Compute Images", "ESCU - Previously Seen Cloud Compute Creations By User", "ESCU - Previously Seen Cloud Compute Instance Types"] data_models = ["Cloud_Infrastructure"] providing_technologies = none description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. @@ -203,7 +203,7 @@ version = 1 reference = ["https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/", "https://objective-see.com/blog/blog_0x2A.html", "https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/"] detection_searches = ["ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule"] mappings = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP", "PR.PT"]} -investigative_searches = ["ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From src ip"] support_searches = [] data_models = [] providing_technologies = none @@ -221,7 +221,7 @@ version = 1 reference = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"] detection_searches = ["ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Suspicious writes to windows Recycle Bin - Rule"] mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043", "T1074", "T1114"], "nist": ["DE.AE", "DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none @@ -239,8 +239,8 @@ version = 1 reference = ["https://attack.mitre.org/wiki/Command_and_Control", "https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware"] detection_searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule"] mappings = {"cis20": ["CIS 1", "CIS 11", "CIS 12", "CIS 13", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["T1043", "T1048", "T1095"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] -support_searches = ["ESCU - Baseline of blocked outbound traffic from AWS", "ESCU - Baseline of DNS Query Length - MLTK"] +investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN"] +support_searches = ["ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseline of blocked outbound traffic from AWS"] data_models = ["Network_Resolution", "Network_Traffic"] providing_technologies = none description = Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. @@ -289,7 +289,7 @@ version = 3 reference = ["https://attack.mitre.org/wiki/Technique/T1003", "https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html"] detection_searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule"] mappings = {"cis20": ["CIS 16", "CIS 3", "CIS 5", "CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1003", "T1064", "T1086"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP"]} -investigative_searches = ["ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Pass the Ticket Attempts"] +investigative_searches = ["ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Failed Logins for Multiple Destinations"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -307,8 +307,8 @@ version = 2 reference = ["https://www.us-cert.gov/ncas/alerts/TA18-074A"] detection_searches = ["ESCU - Create local admin accounts using net exe - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg exe Process - Rule"] mappings = {"cis20": ["CIS 12", "CIS 16", "CIS 2", "CIS 3", "CIS 5", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["T1031", "T1043", "T1050", "T1053", "T1059", "T1064", "T1078", "T1086", "T1089", "T1103", "T1112", "T1131"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.AT", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Registry Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process File Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] -support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Process Registry Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process File Activity"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Baseline of SMB Traffic - MLTK"] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none description = Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. @@ -326,7 +326,7 @@ version = 1 reference = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.imperva.com/learn/application-security/dns-amplification/"] detection_searches = ["ESCU - Large Volume of DNS ANY Queries - Rule"] mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] support_searches = [] data_models = ["Network_Resolution"] providing_technologies = none @@ -367,7 +367,7 @@ version = 1 reference = ["https://www.cisecurity.org/controls/data-protection/", "https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/"] detection_searches = ["ESCU - Detect USB device insertion - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule"] mappings = {"cis20": ["CIS 12", "CIS 13", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE", "DE.CM", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host"] support_searches = [] data_models = ["Change_Analysis", "Network_Resolution"] providing_technologies = none @@ -383,8 +383,8 @@ version = 2 reference = ["https://attack.mitre.org/wiki/Technique/T1089", "https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf"] detection_searches = ["ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Unload Sysmon Filter Driver - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1031", "T1050", "T1059", "T1089", "T1112"], "nist": ["DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] -support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Baseline of SMB Traffic - MLTK"] data_models = ["Endpoint"] providing_technologies = none description = Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. @@ -399,7 +399,7 @@ version = 2 reference = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] detection_searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule"] mappings = {"cis20": ["CIS 13", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1041"], "nist": ["DE.CM", "DE.DP", "PR.IP"]} -investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - Investigate Web Activity From src ip"] support_searches = [] data_models = ["Network_Resolution", "Web"] providing_technologies = none @@ -415,8 +415,8 @@ version = 1 reference = ["https://www.us-cert.gov/ncas/alerts/TA18-201A", "https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf", "https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html"] detection_searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule"] mappings = {"cis20": ["CIS 12", "CIS 2", "CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery", "Exploitation", "Installation"], "mitre_attack": ["T1043", "T1059", "T1072", "T1087", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] -support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Add Prohibited Processes to Enterprise Security"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get Vulnerability Logs For Endpoint"] +support_searches = ["ESCU - Add Prohibited Processes to Enterprise Security", "ESCU - Baseline of SMB Traffic - MLTK"] data_models = ["Email", "Endpoint", "Network_Traffic"] providing_technologies = none description = Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. @@ -433,8 +433,8 @@ version = 2 reference = ["https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf"] detection_searches = ["ESCU - Create or delete windows shares using net exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious File Write - Rule"] mappings = {"cis20": ["CIS 12", "CIS 16", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043", "T1059", "T1064", "T1076"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] -support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Previously seen command line arguments"] +investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - Investigate Successful Remote Desktop Authentications"] +support_searches = ["ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Previously seen command line arguments", "ESCU - Baseline of SMB Traffic - MLTK"] data_models = ["Endpoint", "Network_Resolution", "Network_Traffic"] providing_technologies = none description = Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. @@ -452,7 +452,7 @@ version = 1 reference = ["https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/"] detection_searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule"] mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13", "CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host"] support_searches = [] data_models = ["Network_Resolution"] providing_technologies = none @@ -468,7 +468,7 @@ version = 1 reference = ["http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html"] detection_searches = ["ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule"] mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery", "Reconnaissance"], "mitre_attack": ["T1082"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint"] support_searches = [] data_models = ["Web"] providing_technologies = none @@ -496,15 +496,47 @@ modification_date = 2020-04-15 id = a9ef59cf-e981-4e66-9eef-bb049f695c09 version = 1 reference = ["https://github.com/splunk/cloud-datamodel-security-research"] -detection_searches = ["ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule"] +detection_searches = ["ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Kubernetes Azure pod scan fingerprint - Rule", "ESCU - Kubernetes Azure scan fingerprint - Rule"] mappings = {"kill_chain_phases": ["Reconnaissance"]} -investigative_searches = ["ESCU - GCP Kubernetes activity by src ip", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - GCP Kubernetes activity by src ip"] support_searches = [] data_models = [] providing_technologies = none description = This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. +[Kubernetes Sensitive Object Access Activity] +category = Cloud Security +creation_date = 2020-05-20 +modification_date = 2020-05-20 +id = 2574e6d9-7254-4751-8925-0447deeec8ea +version = 1 +reference = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +detection_searches = ["ESCU - Kubernetes Azure detect sensitive object access - Rule", "ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule", "ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule"] +mappings = {"kill_chain_phases": ["Lateral Movement"]} +investigative_searches = ["ESCU - Get Notable Info"] +support_searches = [] +data_models = [] +providing_technologies = none +description = This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. + +[Kubernetes Sensitive Role Activity] +category = Cloud Security +creation_date = 2020-05-20 +modification_date = 2020-05-20 +id = 2574e6d9-7254-4751-8925-0447deeec8ew +version = 1 +reference = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +detection_searches = ["ESCU - Kubernetes Azure detect RBAC authorization by account - Rule", "ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule", "ESCU - Kubernetes Azure detect sensitive role access - Rule"] +mappings = {"kill_chain_phases": ["Lateral Movement"]} +investigative_searches = ["ESCU - Get Notable Info"] +support_searches = [] +data_models = [] +providing_technologies = none +description = This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities + [Lateral Movement] category = Adversary Tactics creation_date = 2020-02-04 @@ -514,7 +546,7 @@ version = 2 reference = ["https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] detection_searches = ["ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Schtasks scheduling job on remote system - Rule"] mappings = {"cis20": ["CIS 16", "CIS 3", "CIS 9"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053", "T1075", "T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} -investigative_searches = ["ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate Successful Remote Desktop Authentications"] support_searches = [] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none @@ -534,7 +566,7 @@ version = 4 reference = ["https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/", "https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] detection_searches = ["ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule"] mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1064", "T1086"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -559,7 +591,7 @@ version = 1 reference = ["https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/"] detection_searches = ["ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Unsuccessful Netbackup backups - Rule"] mappings = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - All backup logs for host"] +investigative_searches = ["ESCU - All backup logs for host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint"] support_searches = ["ESCU - Monitor Successful Backups", "ESCU - Monitor Unsuccessful Backups"] data_models = [] providing_technologies = none @@ -575,7 +607,7 @@ version = 1 reference = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] detection_searches = ["ESCU - Prohibited Software On Endpoint - Rule"] mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint"] support_searches = ["ESCU - Add Prohibited Processes to Enterprise Security"] data_models = ["Endpoint"] providing_technologies = none @@ -592,7 +624,7 @@ version = 1 reference = ["https://learn.cisecurity.org/20-controls-download"] detection_searches = ["ESCU - No Windows Updates in a time frame - Rule"] mappings = {"cis20": ["CIS 18"], "nist": ["PR.MA"]} -investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] support_searches = [] data_models = ["Updates"] providing_technologies = none @@ -610,8 +642,8 @@ version = 1 reference = ["https://technet.microsoft.com/library/bb490939.aspx", "https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html", "http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html"] detection_searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule"] mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059", "T1089"], "nist": ["DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] -support_searches = ["ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] +support_searches = ["ESCU - Previously seen command line arguments", "ESCU - Baseline of SMB Traffic - MLTK"] data_models = ["Endpoint"] providing_technologies = none description = Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. @@ -627,7 +659,7 @@ version = 2 reference = ["https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia", "https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/"] detection_searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["T1031", "T1050", "T1059", "T1064", "T1089"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] support_searches = ["ESCU - Previously Seen Running Windows Services", "ESCU - Previously seen command line arguments"] data_models = ["Endpoint"] providing_technologies = none @@ -670,7 +702,7 @@ version = 1 reference = ["https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/", "http://blog.amossys.fr/badflick-is-not-so-bad.html"] detection_searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule"] mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1059", "T1064", "T1086", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] data_models = ["Endpoint"] providing_technologies = none @@ -713,7 +745,7 @@ version = 1 reference = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"] detection_searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule"] mappings = {"cis20": ["CIS 12", "CIS 13", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} -investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host"] support_searches = [] data_models = ["Network_Resolution", "Network_Traffic"] providing_technologies = none @@ -729,7 +761,7 @@ version = 1 reference = ["https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/", "https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html"] detection_searches = ["ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - TOR Traffic - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Windows Event Log Cleared - Rule"] mappings = {"cis20": ["CIS 10", "CIS 12", "CIS 3", "CIS 5", "CIS 6", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["T1036", "T1043", "T1047", "T1048", "T1053", "T1070", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Sysmon WMI Activity for Host"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Baseline of SMB Traffic - MLTK"] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none @@ -745,7 +777,7 @@ version = 1 reference = ["https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html", "https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html"] detection_searches = ["ESCU - Detect New Login Attempts to Routers - Rule"] mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] support_searches = [] data_models = ["Authentication"] providing_technologies = none @@ -762,7 +794,7 @@ version = 1 reference = ["https://capec.mitre.org/data/definitions/66.html", "https://www.incapsula.com/web-application-security/sql-injection.html"] detection_searches = ["ESCU - SQL Injection with Long URLs - Rule"] mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] support_searches = [] data_models = ["Web"] providing_technologies = none @@ -779,7 +811,7 @@ version = 1 reference = ["https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/", "https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/", "https://thehackernews.com/2018/07/samsam-ransomware-attacks.html"] detection_searches = ["ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Spike in File Writes - Rule"] mappings = {"cis20": ["CIS 10", "CIS 12", "CIS 16", "CIS 18", "CIS 2", "CIS 3", "CIS 4", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery", "Installation", "Reconnaissance"], "mitre_attack": ["T1059", "T1076", "T1082"], "nist": ["DE.AE", "DE.CM", "ID.AM", "ID.RA", "PR.AC", "PR.DS", "PR.IP", "PR.MA", "PR.PT"]} -investigative_searches = ["ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Investigate Successful Remote Desktop Authentications"] support_searches = ["ESCU - Add Prohibited Processes to Enterprise Security"] data_models = ["Endpoint", "Network_Traffic", "Web"] providing_technologies = none @@ -800,7 +832,7 @@ version = 1 reference = ["https://meltdownattack.com/"] detection_searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule"] mappings = {"cis20": ["CIS 4"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] support_searches = ["ESCU - Systems Ready for Spectre-Meltdown Windows Patch"] data_models = ["Vulnerabilities"] providing_technologies = none @@ -816,7 +848,7 @@ version = 1 reference = ["http://www.splunk.com/view/SP-CAAAPQ6#announce", "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859"] detection_searches = ["ESCU - Open Redirect in Splunk Web - Rule"] mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] support_searches = [] data_models = [] providing_technologies = none @@ -841,7 +873,7 @@ version = 1 reference = ["https://nvd.nist.gov/vuln/detail/CVE-2018-11409", "https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings", "https://www.exploit-db.com/exploits/44865/"] detection_searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule"] mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From src ip"] support_searches = [] data_models = [] providing_technologies = none @@ -860,7 +892,7 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule"] mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] +investigative_searches = ["ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN"] support_searches = ["ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK", "ESCU - Previously Seen AWS Regions", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen EC2 Launches By User"] data_models = [] providing_technologies = none @@ -892,7 +924,7 @@ version = 2 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/"] detection_searches = ["ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule"] mappings = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM", "DE.DP", "PR.AC", "PR.DS"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - AWS S3 Bucket details via bucketName"] +investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable History", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS S3 Bucket details via bucketName"] support_searches = ["ESCU - Baseline of S3 Bucket deletion activity by ARN", "ESCU - Previously seen S3 bucket access by remote IP"] data_models = [] providing_technologies = none @@ -910,7 +942,7 @@ version = 1 reference = ["https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/"] detection_searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule"] mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.AC"]} -investigative_searches = ["ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN"] support_searches = ["ESCU - Baseline of blocked outbound traffic from AWS"] data_models = [] providing_technologies = none @@ -929,7 +961,7 @@ version = 2 reference = ["https://attack.mitre.org/wiki/Technique/T1059", "https://www.microsoft.com/en-us/wdsi/threats/macro-malware", "https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf"] detection_searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule"] mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Exploitation"], "mitre_attack": ["T1036", "T1059", "T1064"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] data_models = ["Endpoint"] providing_technologies = none @@ -945,7 +977,7 @@ version = 1 reference = ["http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/", "http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680", "https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454"] detection_searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule"] mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host"] support_searches = ["ESCU - Baseline of DNS Query Length - MLTK"] data_models = ["Network_Resolution"] providing_technologies = none @@ -961,7 +993,7 @@ version = 1 reference = ["https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/"] detection_searches = ["ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule"] mappings = {"cis20": ["CIS 12", "CIS 3", "CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["DE.AE", "PR.IP"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Emails From Specific Sender", "ESCU - Get Email Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] support_searches = ["ESCU - DNSTwist Domain Names"] data_models = ["Email", "UEBA"] providing_technologies = none @@ -981,7 +1013,7 @@ version = 1 reference = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5", "https://attack.mitre.org/wiki/Technique/T1170"] detection_searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect mshta exe running scripts in command-line arguments - Rule", "ESCU - Registry Keys Used For Persistence - Rule"] mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation"], "mitre_attack": ["T1059", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] data_models = ["Endpoint"] providing_technologies = none @@ -1017,7 +1049,7 @@ version = 2 reference = ["https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf", "https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html"] detection_searches = ["ESCU - Process Execution via WMI - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Temporary Event Subscription - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Sysmon WMI Activity for Host"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1035,7 +1067,7 @@ version = 1 reference = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"] detection_searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Changes to File Associations - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015", "T1042", "T1103", "T1112", "T1131", "T1138"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1044,6 +1076,23 @@ narrative = Attackers are developing increasingly sophisticated techniques for h The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ The searches in this story are designed to help you detect behaviors associated with manipulation of the Windows registry. +[Suspicious Zoom Child Processes] +category = Adversary Tactics +creation_date = 2020-04-13 +modification_date = 2020-04-13 +id = aa3749a6-49c7-491e-a03f-4eaee5fe0258 +version = 1 +reference = ["https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/", "https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/"] +detection_searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - First Time Seen Child Process of Zoom - Rule"] +mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation"], "mitre_attack": ["T1059", "T1068"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} +investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Registry Activity", "ESCU - Get Process File Activity"] +support_searches = ["ESCU - Previously Seen Zoom Child Processes - Update", "ESCU - Previously Seen Zoom Child Processes - Initial"] +data_models = ["Endpoint"] +providing_technologies = none +description = Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. +narrative = Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ +Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. + [Unusual AWS EC2 Modifications] category = Cloud Security creation_date = 2018-04-09 @@ -1053,7 +1102,7 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule"] mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId"] +investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History"] support_searches = ["ESCU - Previously Seen EC2 Modifications By User"] data_models = [] providing_technologies = none @@ -1070,7 +1119,7 @@ version = 2 reference = ["https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html", "https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf", "https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262"] detection_searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule"] mappings = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["T1015", "T1036", "T1085"], "nist": ["DE.CM", "ID.AM", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] support_searches = ["ESCU - Baseline of Command Line Length - MLTK"] data_models = ["Endpoint"] providing_technologies = none @@ -1088,7 +1137,7 @@ version = 1 reference = ["https://www.monkey.org/~dugsong/dsniff/"] detection_searches = ["ESCU - Protocols passing authentication in cleartext - Rule"] mappings = {"cis20": ["CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] support_searches = [] data_models = ["Network_Traffic"] providing_technologies = none @@ -1104,7 +1153,7 @@ version = 1 reference = ["https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud", "https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718"] detection_searches = ["ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Password Sharing Across Accounts - Rule"] mappings = {"cis20": ["CIS 16", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078", "T1136"], "nist": ["DE.AE", "DE.CM", "DE.DP"]} -investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Emails From Specific Sender", "ESCU - Get Web Session Information via session id"] +investigative_searches = ["ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Web Session Information via session id", "ESCU - Get Notable History"] support_searches = [] data_models = [] providing_technologies = none @@ -1125,7 +1174,7 @@ version = 1 reference = ["https://attack.mitre.org/wiki/Defense_Evasion"] detection_searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Reg exe Process - Rule"] mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1089", "T1112"], "nist": ["DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1141,7 +1190,7 @@ version = 1 reference = ["https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/", "https://attack.mitre.org/wiki/Technique/T1042"] detection_searches = ["ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule"] mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1161,7 +1210,7 @@ version = 2 reference = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/", "https://zeltser.com/security-incident-log-review-checklist/", "http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html"] detection_searches = ["ESCU - Deleting Shadow Copies - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule"] mappings = {"cis20": ["CIS 10", "CIS 3", "CIS 5", "CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1178,7 +1227,7 @@ version = 2 reference = ["http://www.fuzzysecurity.com/tutorials/19.html", "https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html", "http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/", "https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html", "https://www.youtube.com/watch?v=dq2Hv7J9fvk"] detection_searches = ["ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1031", "T1050", "T1053", "T1089", "T1103", "T1131", "T1138"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1194,7 +1243,7 @@ version = 2 reference = ["https://attack.mitre.org/tactics/TA0004/"] detection_searches = ["ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule"] mappings = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation"], "mitre_attack": ["T1015", "T1068"], "nist": ["DE.CM", "ID.AM", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1210,7 +1259,7 @@ version = 3 reference = ["https://attack.mitre.org/wiki/Technique/T1050", "https://attack.mitre.org/wiki/Technique/T1031"] detection_searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1031", "T1050", "T1089"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] support_searches = ["ESCU - Previously Seen Running Windows Services"] data_models = ["Endpoint"] providing_technologies = none diff --git a/package/default/analyticstories.conf b/package/default/analyticstories.conf index ace2c52f36..6f8df9ccd3 100644 --- a/package/default/analyticstories.conf +++ b/package/default/analyticstories.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-04-17T19:06:18 UTC +# On Date: 2020-06-04T22:46:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -10,11 +10,11 @@ [analytic_story://AWS Cross Account Activity] category = Cloud Security last_updated = 2018-06-04 -version = 1.0 +version = 1 references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History", "ESCU - Previously Seen AWS Cross Account Activity"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User"] description = Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. narrative = Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ @@ -23,11 +23,11 @@ This Analytic Story includes searches that will help you monitor your AWS CloudT [analytic_story://AWS Cryptomining] category = Cloud Security last_updated = 2018-03-08 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 AMIs", "ESCU - Previously Seen EC2 Instance Types", "ESCU - Previously Seen EC2 Launches By User"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN"] description = Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS). It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ @@ -37,22 +37,22 @@ This Analytic Story is focused on detecting suspicious new instances in your EC2 [analytic_story://AWS Network ACL Activity] category = Cloud Security last_updated = 2018-05-21 -version = 2.0 +version = 2 references = ["https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html", "https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of Network ACL Activity by ARN", "ESCU - Baseline of blocked outbound traffic from AWS"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN"] description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls. [analytic_story://AWS Suspicious Provisioning Activities] category = Cloud Security last_updated = 2018-03-16 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Region", "ESCU - Previously Seen AWS Provisioning Activity Sources"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From IP Address"] description = Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. narrative = Because most enterprise AWS activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to whitelist specific IPs (because they vary).\ This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. @@ -60,11 +60,11 @@ This Analytic Story was designed to provide you with flexibility in the precisio [analytic_story://AWS User Monitoring] category = Cloud Security last_updated = 2018-03-12 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Investigate AWS User Activities by user field", "ESCU - Baseline of API Calls per User ARN", "ESCU - Baseline of Security Group Activity by ARN", "ESCU - Create a list of approved AWS service accounts", "ESCU - Previously seen API call per user roles in CloudTrail"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Investigate AWS User Activities by user field", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ @@ -74,22 +74,22 @@ The detection searches in this Analytic Story are designed to help you uncover A [analytic_story://Account Monitoring and Controls] category = Best Practices last_updated = 2017-09-06 -version = 1.0 -references = ["https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}, {"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +version = 1 +references = [] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Logon Rights Modifications For Endpoint"] description = A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. narrative = Monitoring user accounts within your enterprise is a critical analytic function that helps ensure that credential and access policies/procedures are properly implemented and are being enforced. Proactive ad-hoc hunting, as well as routine monitoring, can ensure user or system accounts are not being abused by unauthorized individuals or processes. In the event of a network event or breach, user-authentication logs are a key resource in determining if or how an account might have been compromised or co-opted, leading to suspicious or malicious activity. [analytic_story://Apache Struts Vulnerability] category = Vulnerability last_updated = 2018-12-06 -version = 1.0 +version = 1 references = ["https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf"] -maintainers = [{"company": "Splunk", "email": "jhernandez@splunk.com", "name": "Jose Hernandez"}] -spec_version = 2 -searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Investigate Web POSTs From src"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Investigate Web POSTs From src", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. narrative = In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ @@ -109,22 +109,22 @@ It can also be very helpful to examine various behaviors of the process of inter [analytic_story://Asset Tracking] category = Best Practices last_updated = 2017-09-13 -version = 1.0 +version = 1 references = ["https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Count of assets by category"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address"] description = Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. narrative = This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business context and awareness of their assets during an incident. Information derived from this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. [analytic_story://Brand Monitoring] category = Abuse last_updated = 2017-12-19 -version = 1.0 +version = 1 references = ["https://www.zerofox.com/blog/what-is-digital-risk-monitoring/", "https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/", "https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - DNSTwist Domain Names"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Emails From Specific Sender", "ESCU - Get Email Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. narrative = While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and will happily leverage it for nefarious purposes whenever possible3phishing with lookalike addresses, embedding faux command-and-control domains in malware, and hosting malicious content on domains that closely mimic your corporate servers. This is where brand monitoring comes in.\ You can use our adaptation of `DNSTwist`, together with the support searches in this Analytic Story, to generate permutations of specified brands and external domains. Splunk can monitor email, DNS requests, and web traffic for these permutations and provide you with early warnings and situational awareness--powerful elements of an effective defense.\ @@ -133,11 +133,11 @@ Notable events will include IP addresses, URLs, and user data. Drilling down can [analytic_story://Cloud Cryptomining] category = Cloud Security last_updated = 2019-10-02 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Previously Seen Cloud Compute Creations By User", "ESCU - Previously Seen Cloud Compute Images", "ESCU - Previously Seen Cloud Compute Instance Types", "ESCU - Previously Seen Cloud Regions"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Investigate User Activities In Single Cloud Region"] description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ @@ -147,11 +147,11 @@ This Analytic Story is focused on detecting suspicious new instances in your clo [analytic_story://ColdRoot MacOS RAT] category = Malware last_updated = 2019-01-09 -version = 1.0 +version = 1 references = ["https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/", "https://objective-see.com/blog/blog_0x2A.html", "https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/"] -maintainers = [{"company": "Splunk", "email": "jhernandez@splunk.com", "name": "Jose Hernandez"}] -spec_version = 2 -searches = ["ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Network Traffic From src_ip", "ESCU - Investigate Web Activity From src_ip"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Jose Hernandez"}] +spec_version = 3 +searches = ["ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From src ip"] description = Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. narrative = Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ @@ -160,11 +160,11 @@ Searches in this Analytic Story leverage the capabilities of OSquery to address [analytic_story://Collection and Staging] category = Adversary Tactics last_updated = 2020-02-03 -version = 1.1 +version = 1 references = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. narrative = A common adversary goal is to identify and exfiltrate data of value from a target organization. This data may include email conversations and addresses, confidential company information, links to network design/infrastructure, important dates, and so on.\ Attacks are composed of three activities: identification, collection, and staging data for exfiltration. Identification typically involves scanning systems and observing user activity. Collection can involve the transfer of large amounts of data from various repositories. Staging/preparation includes moving data to a central location and compressing (and optionally encoding and/or encrypting) it. All of these activities provide opportunities for defenders to identify their presence. \ @@ -173,11 +173,11 @@ Use the searches to detect and monitor suspicious behavior related to these acti [analytic_story://Command and Control] category = Adversary Tactics last_updated = 2018-06-01 -version = 1.0 +version = 1 references = ["https://attack.mitre.org/wiki/Command_and_Control", "https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseline of blocked outbound traffic from AWS"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Protocol or Port Mismatch - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - TOR Traffic - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN"] description = Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. narrative = Threat actors typically architect and implement an infrastructure to use in various ways during the course of their attack campaigns. In some cases, they leverage this infrastructure for scanning and performing reconnaissance activities. In others, they may use this infrastructure to launch actual attacks. One of the most important functions of this infrastructure is to establish servers that will communicate with implants on compromised endpoints. These servers establish a command and control channel that is used to proxy data between the compromised endpoint and the attacker. These channels relay commands from the attacker to the compromised endpoint and the output of those commands back to the attacker.\ Because this communication is so critical for an adversary, they often use techniques designed to hide the true nature of the communications. There are many different techniques used to establish and communicate over these channels. This Analytic Story provides searches that look for a variety of the techniques used for these channels, as well as indications that these channels are active, by examining logs associated with border control devices and network-access control lists. @@ -185,22 +185,22 @@ Because this communication is so critical for an adversary, they often use techn [analytic_story://Common Phishing Frameworks] category = Adversary Tactics last_updated = 2019-04-29 -version = 1.0 +version = 1 references = ["https://github.com/kgretzky/evilginx2", "https://attack.mitre.org/techniques/T1192/", "https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/"] -maintainers = [{"company": "Splunk", "email": "research@splunk.com", "name": "Splunk Research Team"}] -spec_version = 2 +maintainers = [{"company": "Splunk", "email": "-", "name": "Splunk Research Team"}] +spec_version = 3 searches = ["ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule", "ESCU - Get Certificate logs for a domain"] description = Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. narrative = As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Because phishing is a technique that relies on human psychology, you will never be able to eliminate this vulnerability 100%. But you can use automated detection to significantly reduce the risks.\ This Analytic Story focuses on detecting signs of MiTM attacks enabled by [EvilGinx2](https://github.com/kgretzky/evilginx2), a toolkit that sets up a transparent proxy between the targeted site and the user. In this way, the attacker is able to intercept credentials and two-factor identification tokens. It employs a proxy template to allow a registered domain to impersonate targeted sites, such as Linkedin, Amazon, Okta, Github, Twitter, Instagram, Reddit, Office 365, and others. It can even register SSL certificates and camouflage them via a URL shortener, making them difficult to detect. Searches in this story look for signs of MiTM attacks enabled by EvilGinx2. -[analytic_story://Container Implantation Monitoring & Investigation] +[analytic_story://Container Implantation Monitoring and Investigation] category = Cloud Security -last_updated = -version = 1.0 +last_updated = 2020-02-20 +version = 1 references = ["https://github.com/splunk/cloud-datamodel-security-research"] -maintainers = [{"company": "Splunk", "email": "rsoto@splunk.com, rvaldez@splunk.com", "name": "Rod Soto, Rico Valdez"}] -spec_version = 2 +maintainers = [{"company": "Rico Valdez, Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 searches = ["ESCU - GCP GCR container uploaded - Rule", "ESCU - New container uploaded to AWS ECR - Rule", "ESCU - Investigate AWS ECR container listing activity"] description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. narrative = Container Registrys provide a way for organizations to keep customized images of their development and infrastructure environment in private. However if these repositories are misconfigured or priviledge users credentials are compromise, attackers can potentially upload implanted containers which can be deployed across the organization. These searches allow operator to monitor who, when and what was uploaded to container registry. @@ -208,11 +208,11 @@ narrative = Container Registrys provide a way for organizations to keep customiz [analytic_story://Credential Dumping] category = Adversary Tactics last_updated = 2020-02-04 -version = 3.0 +version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1003", "https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}, {"company": "Splunk", "email": "pbareiss@splunk.com", "name": "Patrick Bareiss"}] -spec_version = 2 -searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Attempted Credential Dump From Registry via Reg.exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Previous Unseen User"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Failed Logins for Multiple Destinations"] description = Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. narrative = Credential dumping—gathering credentials from a target system, often hashed or encrypted—is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems. The threat actors target a variety of sources to extract them, including the Security Accounts Manager (SAM), Local Security Authority (LSA), NTDS from Domain Controllers, or the Group Policy Preference (GPP) files.\ Once attackers obtain valid credentials, they use them to move throughout a target network with ease, discovering new systems and identifying assets of interest. Credentials obtained in this manner typically include those of privileged users, which may provide access to more sensitive information and system operations.\ @@ -221,11 +221,11 @@ The detection searches in this Analytic Story monitor access to the Local Securi [analytic_story://DHS Report TA18-074A] category = Malware last_updated = 2020-01-22 -version = 2.0 +version = 2 references = ["https://www.us-cert.gov/ncas/alerts/TA18-074A"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Create local admin accounts using net.exe - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg.exe Process - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process File Activity", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Registry Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Create local admin accounts using net exe - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Process Registry Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process File Activity"] description = Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. narrative = The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ @@ -235,11 +235,11 @@ Suspicious activities--spikes in SMB traffic, processes that launch netsh (to mo [analytic_story://DNS Amplification Attacks] category = Abuse last_updated = 2016-09-13 -version = 1.0 +version = 1 references = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.imperva.com/learn/application-security/dns-amplification/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. narrative = The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. @@ -247,11 +247,11 @@ The search in this story can help you to detect if attackers are abusing your co [analytic_story://DNS Hijacking] category = Adversary Tactics last_updated = 2020-02-04 -version = 1.0 +version = 1 references = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS record changed - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Discover DNS records"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS record changed - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Get DNS Server History for a host"] description = Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. narrative = Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.\ The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous. Not only can hackers bring down an entire business, they can intercept confidential information, emails, and login credentials, as well. \ @@ -266,44 +266,44 @@ The searches in this Analytic Story help you detect and investigate activities t [analytic_story://Data Protection] category = Abuse last_updated = 2017-09-14 -version = 1.0 +version = 1 references = ["https://www.cisecurity.org/controls/data-protection/", "https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect USB device insertion - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect USB device insertion - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host"] description = Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. narrative = Attackers can leverage a variety of resources to compromise or exfiltrate enterprise data. Common exfiltration techniques include remote-access channels via low-risk, high-payoff active-collections operations and close-access operations using insiders and removable media. While this Analytic Story is not a comprehensive listing of all the methods by which attackers can exfiltrate data, it provides a useful starting point. [analytic_story://Disabling Security Tools] category = Adversary Tactics last_updated = 2020-02-04 -version = 2.0 +version = 2 references = ["https://attack.mitre.org/wiki/Technique/T1089", "https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Suspicious Reg.exe Process - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Suspicious Reg exe Process - Rule", "ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. narrative = Attackers employ a variety of tactics in order to avoid detection and operate without barriers. This often involves modifying the configuration of security tools to get around them or explicitly disabling them to prevent them from running. This Analytic Story includes searches that look for activity consistent with attackers attempting to disable various security mechanisms. Such activity may involve monitoring for suspicious registry activity, as this is where much of the configuration for Windows and various other programs reside, or explicitly attempting to shut down security-related services. Other times, attackers attempt various tricks to prevent specific programs from running, such as adding the certificates with which the security tools are signed to a blacklist (which would prevent them from running). [analytic_story://Dynamic DNS] category = Malware last_updated = 2018-09-06 -version = 2.0 +version = 2 references = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From src_ip"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - Investigate Web Activity From src ip"] description = Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. narrative = Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and blacklists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. -[analytic_story://Emotet Malware (DHS Report TA18-201A)] +[analytic_story://Emotet Malware DHS Report TA18-201A ] category = Malware last_updated = 2020-01-27 -version = 1.0 +version = 1 references = ["https://www.us-cert.gov/ncas/alerts/TA18-201A", "https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf", "https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Add Prohibited Processes to Enterprise Security", "ESCU - Baseline of SMB Traffic - MLTK"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detection of tools built by NirSoft - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get Vulnerability Logs For Endpoint"] description = Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. narrative = The trojan downloader known as Emotet first surfaced in 2014, when it was discovered targeting the banking industry to steal credentials. However, according to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), Emotet has evolved far beyond those beginnings to become what a ThreatPost article called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For example, in early 2018, Emotet was found to be using its loader function to spread the Quakbot and Ransomware variants. \ According to the TA, the the malware continues to be among the most costly and destructive malware affecting the private and public sectors. Researchers have linked it to the threat group Mealybug, which has also been on the security communitys radar since 2014.\ @@ -312,11 +312,11 @@ The searches in this Analytic Story will help you find executables that are rare [analytic_story://Hidden Cobra Malware] category = Malware last_updated = 2020-01-22 -version = 2.0 +version = 2 references = ["https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Create or delete windows shares using net.exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of DNS Query Length - MLTK", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Create or delete windows shares using net exe - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - Investigate Successful Remote Desktop Authentications"] description = Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. narrative = North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ @@ -326,22 +326,22 @@ Among other searches in this Analytic Story is a detection search that looks for [analytic_story://Host Redirection] category = Abuse last_updated = 2017-09-14 -version = 1.0 +version = 1 references = ["https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host"] description = Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. narrative = Attackers will often attempt to manipulate client communications for nefarious purposes. In some cases, an attacker may endeavor to modify a local host file to redirect communications with resources (such as antivirus or system-update services) to prevent clients from receiving patches or updates. In other cases, an attacker might use this tactic to have the client connect to a site that looks like the intended site, but instead installs malware or collects information from the victim. Additionally, an attacker may redirect a victim in order to execute a MITM attack and observe communications. [analytic_story://JBoss Vulnerability] category = Vulnerability last_updated = 2017-09-14 -version = 1.0 +version = 1 references = ["http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint"] description = In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. narrative = This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with the Samsam ransomware variant. Incidents involving this ransomware are unique, in that they begin with attacks against vulnerable services, rather than the phishing or drive-by attacks more common with ransomware. In this case, vulnerable JBoss applications appear to be the target of choice.\ It is helpful to understand how often a notable event generated by this story occurs, as well as the commonalities between some of these events, both of which may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. It may also help to understand whether the issue is restricted to a single user/system or whether it is broader in scope.\ @@ -362,22 +362,44 @@ It can also be helpful to examine various behaviors of and the parent of the pro [analytic_story://Kubernetes Scanning Activity] category = Cloud Security last_updated = 2020-04-15 -version = 1.0 +version = 1 references = ["https://github.com/splunk/cloud-datamodel-security-research"] -maintainers = [{"company": "Splunk", "email": "rsoto@splunk.com", "name": "Rod Soto"}] -spec_version = 2 -searches = ["ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes activity by src_ip", "ESCU - GCP Kubernetes activity by src_ip", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +searches = ["ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Kubernetes Azure pod scan fingerprint - Rule", "ESCU - Kubernetes Azure scan fingerprint - Rule", "ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - GCP Kubernetes activity by src ip"] description = This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. +[analytic_story://Kubernetes Sensitive Object Access Activity] +category = Cloud Security +last_updated = 2020-05-20 +version = 1 +references = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +searches = ["ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule", "ESCU - Kubernetes Azure detect sensitive object access - Rule", "ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule", "ESCU - Get Notable Info"] +description = This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. + +[analytic_story://Kubernetes Sensitive Role Activity] +category = Cloud Security +last_updated = 2020-05-20 +version = 1 +references = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +searches = ["ESCU - Kubernetes Azure detect sensitive role access - Rule", "ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule", "ESCU - Kubernetes Azure detect RBAC authorization by account - Rule", "ESCU - Get Notable Info"] +description = This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities + [analytic_story://Lateral Movement] category = Adversary Tactics last_updated = 2020-02-04 -version = 2.0 -references = ["https://blog.binarydefense.com/reliably-detecting-pass-the-hash-through-event-log-analysis", "https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Successful Remote Desktop Authentications"] +version = 2 +references = ["https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate Successful Remote Desktop Authentications"] description = Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. narrative = Once attackers gain a foothold within an enterprise, they will seek to expand their accesses and leverage techniques that facilitate lateral movement. Attackers will often spend quite a bit of time and effort moving laterally. Because lateral movement renders an attacker the most vulnerable to detection, it's an excellent focus for detection and investigation.\ Indications of lateral movement can include the abuse of system utilities (such as `psexec.exe`), unauthorized use of remote desktop services, `file/admin$` shares, WMI, PowerShell, pass-the-hash, or the abuse of scheduled tasks. Organizations must be extra vigilant in detecting lateral movement techniques and look for suspicious activity in and around high-value strategic network assets, such as Active Directory, which are often considered the primary target or "crown jewels" to a persistent threat actor.\ @@ -388,11 +410,11 @@ If there is evidence of lateral movement, it is imperative for analysts to colle [analytic_story://Malicious PowerShell] category = Adversary Tactics last_updated = 2017-08-23 -version = 4.0 +version = 4 references = ["https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/", "https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. narrative = The searches in this Analytic Story monitor for parameters often used for malicious purposes. It is helpful to understand how often the notable events generated by this story occur, as well as the commonalities between some of these events. These factors may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. Likewise, it is important to determine whether the issue is restricted to a single user/system or is broader in scope.\ The following factors may assist you in determining whether the event is malicious: \ @@ -408,22 +430,22 @@ In the event a system is suspected of having been compromised via a malicious we [analytic_story://Monitor Backup Solution] category = Best Practices last_updated = 2017-09-12 -version = 1.0 +version = 1 references = ["https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - All backup logs for host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Monitor Successful Backups", "ESCU - Monitor Unsuccessful Backups"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - All backup logs for host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint"] description = Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. narrative = Having backups is a standard best practice that helps ensure continuity of business operations. Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. The detection searches in this Analytic Story will help you identify systems that have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint. [analytic_story://Monitor for Unauthorized Software] category = Best Practices last_updated = 2017-09-15 -version = 1.0 +version = 1 references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Add Prohibited Processes to Enterprise Security"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint"] description = Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. narrative = It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within Enterprise Security with prohibited processes. An included support search will augment this data, adding information on processes thought to be malicious. This search requires data from endpoint detection-and-response solutions, endpoint data sources (such as Sysmon), or Windows Event Logs--assuming that the Active Directory administrator has enabled process tracking within the System Event Audit Logs.\ It is important to investigate any software identified as suspicious, in order to understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. @@ -431,10 +453,10 @@ It is important to investigate any software identified as suspicious, in order t [analytic_story://Monitor for Updates] category = Best Practices last_updated = 2017-09-15 -version = 1.0 +version = 1 references = ["https://learn.cisecurity.org/20-controls-download"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] description = Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. narrative = It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important to eliminate known vulnerabilities that may be exploited by various threat actors.\ @@ -444,11 +466,11 @@ Microsoft releases updates for Windows systems on a monthly cadence. They should [analytic_story://Netsh Abuse] category = Abuse last_updated = 2017-01-05 -version = 1.0 +version = 1 references = ["https://technet.microsoft.com/library/bb490939.aspx", "https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html", "http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of SMB Traffic - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. narrative = It is a common practice for attackers of all types to leverage native Windows tools and functionality to execute commands for malicious reasons. One such tool on Windows OS is `netsh.exe`,a command-line scripting utility that allows you to--either locally or remotely--display or modify the network configuration of a computer that is currently running. `Netsh.exe` can be used to discover and disable local firewall settings. It can also be used to set up a remote connection to a host from an infected system.\ To get started, run the detection search to identify parent processes of `netsh.exe`. @@ -456,11 +478,11 @@ To get started, run the detection search to identify parent processes of `netsh. [analytic_story://Orangeworm Attack Group] category = Malware last_updated = 2020-01-22 -version = 2.0 +version = 2 references = ["https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia", "https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Previously Seen Running Windows Services", "ESCU - Previously seen command line arguments"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. narrative = In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with remote access to the target system, decrypting and extracting a copy of its main DLL payload from its resource section. Before writing the payload to disk, it inserts a randomly generated string into the middle of the decrypted payload in an attempt to evade hash-based detections.\ Awareness of the Orangeworm group first surfaced in January, 2015. It has conducted targeted attacks against related industries, as well, such as pharmaceuticals and healthcare IT solution providers.\ @@ -471,11 +493,11 @@ This Analytic Story is designed to help you detect and investigate suspicious ac [analytic_story://Phishing Payloads] category = Adversary Tactics last_updated = 2019-04-29 -version = 1.0 +version = 1 references = ["https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html"] -maintainers = [{"company": "Splunk", "email": "research@splunk.com", "name": "Splunk Research Team"}] -spec_version = 2 -searches = ["ESCU - Detect Oulook.exe writing a .zip file - Rule", "ESCU - Suspicious LNK file launching a process - Rule", "ESCU - Get Parent Process Info"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Splunk Research Team"}] +spec_version = 3 +searches = ["ESCU - Detect Oulook exe writing a zip file - Rule", "ESCU - Suspicious LNK file launching a process - Rule", "ESCU - Get Parent Process Info"] description = Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. narrative = Despite its simplicity, phishing remains the most pervasive and dangerous cyberthreat. In fact, research shows that as many as [91% of all successful attacks](https://digitalguardian.com/blog/91-percent-cyber-attacks-start-phishing-email-heres-how-protect-against-phishing) are initiated via a phishing email. \ As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Worse, because its success relies on the gullibility of humans, it's impossible to completely "automate" it out of your environment. However, you can use ES and ESCU to detect and investigate potentially malicious payloads injected into your environment subsequent to a phishing attack. \ @@ -489,11 +511,11 @@ This Analytic Story focuses on detecting signs that a malicious payload has been [analytic_story://Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns] category = Adversary Tactics last_updated = 2020-01-22 -version = 1.0 +version = 1 references = ["https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/", "http://blog.amossys.fr/badflick-is-not-so-bad.html"] -maintainers = [{"company": "iDefense", "email": "iDefense.IntelOps@accenture.com", "name": "iDefense Cyber Espionage Team"}] -spec_version = 2 -searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = [{"company": "iDefense", "email": "-", "name": "iDefense Cyber Espionage Team"}] +spec_version = 3 +searches = ["ESCU - Unusually Long Command Line - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. narrative = This story was created as a joint effort between iDefense and Splunk.\ iDefense analysts have recently discovered a Windows executable file that, upon execution, spoofs a decryption tool and then drops a file that appears to be the custom-built javascript backdoor, "Orz," which is associated with the threat actors known as MUDCARP (as well as "temp.Periscope" and "Leviathan"). The file is executed using Wscript.\ @@ -527,33 +549,33 @@ If behavioral searches included in this story yield positive hits, iDefense reco [analytic_story://Prohibited Traffic Allowed or Protocol Mismatch] category = Best Practices last_updated = 2017-09-11 -version = 1.0 +version = 1 references = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host"] description = Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. narrative = A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. The combined effect allows both network defenders and security controls to focus and not be mired in superfluous traffic or data types. Looking for deviations to policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. [analytic_story://Ransomware] category = Malware last_updated = 2020-02-04 -version = 1.1 +version = 1 references = ["https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/", "https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - TOR Traffic - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Baseline of SMB Traffic - MLTK"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Sysmon WMI Activity for Host"] description = Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. narrative = Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise. Attackers can deploy ransomware to enterprises through spearphishing campaigns and driveby downloads, as well as through traditional remote service-based exploitation. In the case of the WannaCry campaign, there was self-propagating wormable functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. -[analytic_story://Router & Infrastructure Security] +[analytic_story://Router and Infrastructure Security] category = Best Practices last_updated = 2017-09-12 -version = 1.0 +version = 1 references = ["https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html", "https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. narrative = Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. @@ -561,11 +583,11 @@ This Analytic Story helps you gain a better understanding of how your network de [analytic_story://SQL Injection] category = Adversary Tactics last_updated = 2017-09-19 -version = 1.0 +version = 1 references = ["https://capec.mitre.org/data/definitions/66.html", "https://www.incapsula.com/web-application-security/sql-injection.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] description = Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. narrative = It is very common for attackers to inject SQL parameters into vulnerable web applications, which then interpret the malicious SQL statements.\ This Analytic Story contains a search designed to identify attempts by attackers to leverage this technique to compromise a host and gain a foothold in the target environment. @@ -573,11 +595,11 @@ This Analytic Story contains a search designed to identify attempts by attackers [analytic_story://SamSam Ransomware] category = Malware last_updated = 2018-12-13 -version = 1.0 +version = 1 references = ["https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/", "https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/", "https://thehackernews.com/2018/07/samsam-ransomware-attacks.html"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Update Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Investigate Web Activity From Host", "ESCU - Add Prohibited Processes to Enterprise Security"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Samsam Test File Write - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Investigate Successful Remote Desktop Authentications"] description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. narrative = The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ @@ -589,21 +611,21 @@ This Analytic Story includes searches designed to help detect and investigate si [analytic_story://Spectre And Meltdown Vulnerabilities] category = Vulnerability last_updated = 2018-01-08 -version = 1.0 +version = 1 references = ["https://meltdownattack.com/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Systems Ready for Spectre-Meltdown Windows Patch"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. narrative = Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched. [analytic_story://Splunk Enterprise Vulnerability] category = Vulnerability last_updated = 2017-09-19 -version = 1.0 +version = 1 references = ["http://www.splunk.com/view/SP-CAAAPQ6#announce", "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] description = Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. narrative = This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\ @@ -620,11 +642,11 @@ It is important to ensure that your Splunk deployment is being kept up to date a [analytic_story://Splunk Enterprise Vulnerability CVE-2018-11409] category = Vulnerability last_updated = 2018-06-14 -version = 1.0 +version = 1 references = ["https://nvd.nist.gov/vuln/detail/CVE-2018-11409", "https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings", "https://www.exploit-db.com/exploits/44865/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Network Traffic From src_ip", "ESCU - Investigate Web Activity From src_ip"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From src ip"] description = Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. narrative = Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). NIST has included it in its vulnerability database (read more here: https://nvd.nist.gov/vuln/detail/CVE-2018-11409). The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Customers should upgrade to the latest version to reduce the risk of this vulnerability.\ Splunk Enterprise exposes partial information about the host operating system, hardware, and Splunk license. Splunk Enterprise before 6.6.0 exposes this information without authentication. Splunk Enterprise 6.6.0 and later exposes this information only to authenticated Splunk users. Based on the information exposure, Splunk characterizes this issue as a low severity impact.\ @@ -634,33 +656,33 @@ A detection search within this Analytic Story looks for vulnerabilities describe [analytic_story://Suspicious AWS EC2 Activities] category = Cloud Security last_updated = 2018-02-09 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK", "ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK", "ESCU - Previously Seen AWS Regions", "ESCU - Previously Seen EC2 Launches By User"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN"] description = Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and risk auditing within your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Console, AWS command-line interface, and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your AWS EC2 instances and helps you respond and investigate those activities. [analytic_story://Suspicious AWS Login Activities] category = Cloud Security last_updated = 2019-05-01 -version = 1.0 +version = 1 references = ["https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}, {"company": "Splunk", "email": "jbrewer@splunk.com", "name": "Jason Brewer"}] -spec_version = 2 -searches = ["ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect new user AWS Console Login - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Previously seen users in CloudTrail", "ESCU - Update previously seen users in CloudTrail"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect new user AWS Console Login - Rule", "ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - AWS Investigate User Activities By ARN"] description = Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. narrative = It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker. [analytic_story://Suspicious AWS S3 Activities] category = Cloud Security last_updated = 2018-07-24 -version = 2.0 +version = 2 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - Baseline of S3 Bucket deletion activity by ARN", "ESCU - Previously seen S3 bucket access by remote IP"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable History", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS S3 Bucket details via bucketName"] description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. narrative = As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ @@ -669,11 +691,11 @@ Among things to look out for are S3 access from unfamiliar locations and by unfa [analytic_story://Suspicious AWS Traffic] category = Cloud Security last_updated = 2018-05-07 -version = 1.0 +version = 1 references = ["https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network ACL Details from ID", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of blocked outbound traffic from AWS"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN"] description = Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). narrative = A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ @@ -683,33 +705,33 @@ The searches in this Analytic Story will monitor your AWS network traffic for ev [analytic_story://Suspicious Command-Line Executions] category = Adversary Tactics last_updated = 2020-02-03 -version = 2.1 +version = 2 references = ["https://attack.mitre.org/wiki/Technique/T1059", "https://www.microsoft.com/en-us/wdsi/threats/macro-malware", "https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule", "ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. narrative = The ability to execute arbitrary commands via the Windows CLI is a primary goal for the adversary. With access to the shell, an attacker can easily run scripts and interact with the target system. Often, attackers may only have limited access to the shell or may obtain access in unusual ways. In addition, malware may execute and interact with the CLI in ways that would be considered unusual and inconsistent with typical user activity. This provides defenders with opportunities to identify suspicious use and investigate, as appropriate. This Analytic Story contains various searches to help identify this suspicious activity, as well as others to aid you in deeper investigation. [analytic_story://Suspicious DNS Traffic] category = Adversary Tactics last_updated = 2017-09-18 -version = 1.0 +version = 1 references = ["http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/", "http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680", "https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Baseline of DNS Query Length - MLTK"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host"] description = Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. narrative = Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. [analytic_story://Suspicious Emails] category = Adversary Tactics last_updated = 2020-01-27 -version = 1.0 +version = 1 references = ["https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - DNSTwist Domain Names"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Get Emails From Specific Sender", "ESCU - Get Email Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. narrative = It is a common practice for attackers of all types to leverage targeted spearphishing campaigns and mass mailers to deliver weaponized email messages and attachments. Fortunately, there are a number of ways to monitor email data in Splunk to detect suspicious content.\ Once a phishing message has been detected, the next steps are to answer the following questions: \ @@ -720,11 +742,11 @@ Once a phishing message has been detected, the next steps are to answer the foll [analytic_story://Suspicious MSHTA Activity] category = Adversary Tactics last_updated = 2020-02-03 -version = 1.1 +version = 1 references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5", "https://attack.mitre.org/wiki/Technique/T1170"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule", "ESCU - Detect mshta.exe running scripts in command-line arguments - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK", "ESCU - Previously seen command line arguments"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Detect mshta exe running scripts in command-line arguments - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. narrative = One common adversary tactic is to bypass application white-listing solutions via the mshta.exe process, which executes Microsoft HTML applications with the .hta suffix. In these cases, attackers use the trusted Windows utility to eproxy execution of malicious files, whether an .hta application, javascript, or VBScript.\ One example of a notable mshta.exe attack was the Kovter malware (https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5) that was implicated in ransomware and click-fraud attacks. Kovter utilized .hta to execute a series of javascript commands, each progressively more dangerous. According to the Mitre Parternship Network (https://attack.mitre.org/wiki/Technique/T1170), FIN7 has leveraged mshta.exe, as has the MuddyWater group, who used it to execute its POWERSTATS payload (which then used the utility to execute additional payloads).\ @@ -733,11 +755,11 @@ The searches in this story help you detect and investigate suspicious activity t [analytic_story://Suspicious Okta Activity] category = Adversary Tactics last_updated = 2020-04-02 -version = 1.0 +version = 1 references = ["https://attack.mitre.org/wiki/Technique/T1078", "https://owasp.org/www-community/attacks/Credential_stuffing", "https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate User Activities In Okta"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Investigate User Activities In Okta", "ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate Okta Activity by app"] description = Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. narrative = Okta is the leading single sign on (SSO) provider, allowing users to authenticate once to Okta, and from there access a variety of web-based applications. These applications are assigned to users and allow administrators to centrally manage which users are allowed to access which applications. It also provides centralized logging to help understand how the applications are used and by whom. \ While SSO is a major convenience for users, it also provides attackers with an opportunity. If the attacker can gain access to Okta, they can access a variety of applications. As such monitoring the environment is important. \ @@ -746,11 +768,11 @@ With people moving quickly to adopt web-based applications and ways to manage th [analytic_story://Suspicious WMI Use] category = Adversary Tactics last_updated = 2018-10-23 -version = 2.0 +version = 2 references = ["https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf", "https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Process Execution via WMI - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get User Information from Identity Table"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Script Execution via WMI - Rule", "ESCU - Process Execution via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Sysmon WMI Activity for Host"] description = Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. narrative = WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning to WMI abuse in their efforts to conduct nefarious tasks, such as reconnaissance, detection of antivirus and virtual machines, code execution, lateral movement, persistence, and data exfiltration. \ The detection searches included in this Analytic Story are used to look for suspicious use of WMI commands that attackers may leverage to interact with remote systems. The searches specifically look for the use of WMI to run processes on remote systems.\ @@ -759,24 +781,36 @@ In the event that unauthorized WMI execution occurs, it will be important for an [analytic_story://Suspicious Windows Registry Activities] category = Adversary Tactics last_updated = 2018-05-31 -version = 1.0 +version = 1 references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. narrative = Attackers are developing increasingly sophisticated techniques for hijacking target servers, while evading detection. One such technique that has become progressively more common is registry modification.\ The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ The searches in this story are designed to help you detect behaviors associated with manipulation of the Windows registry. +[analytic_story://Suspicious Zoom Child Processes] +category = Adversary Tactics +last_updated = 2020-04-13 +version = 1 +references = ["https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/", "https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - First Time Seen Child Process of Zoom - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Registry Activity", "ESCU - Get Process File Activity"] +description = Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. +narrative = Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ +Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. + [analytic_story://Unusual AWS EC2 Modifications] category = Cloud Security last_updated = 2018-04-09 -version = 1.0 +version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History", "ESCU - Previously Seen EC2 Modifications By User"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History"] description = Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. narrative = A common attack technique is to infiltrate a cloud instance and make modifications. The adversary can then secure access to your infrastructure or hide their activities. So it's important to stay alert to changes that may indicate that your environment has been compromised. \ Searches within this Analytic Story can help you detect the presence of a threat by monitoring for EC2 instances that have been created or changed--either by users that have never previously performed these activities or by known users who modify or create instances in a way that have not been done before. This story also provides investigative searches that help you go deeper once you detect suspicious behavior. @@ -784,11 +818,11 @@ narrative = A common attack technique is to infiltrate a cloud instance and make [analytic_story://Unusual Processes] category = Malware last_updated = 2020-02-04 -version = 2.1 +version = 2 references = ["https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html", "https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf", "https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Baseline of Command Line Length - MLTK"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. narrative = Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ @@ -797,22 +831,22 @@ In the event an unusual process is identified, it is imperative to better unders [analytic_story://Use of Cleartext Protocols] category = Best Practices last_updated = 2017-09-15 -version = 1.0 +version = 1 references = ["https://www.monkey.org/~dugsong/dsniff/"] -maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. narrative = Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information could be highly sensitive, or could allow for session hijacking. In addition, these protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. [analytic_story://Web Fraud Detection] category = Abuse last_updated = 2018-10-08 -version = 1.0 +version = 1 references = ["https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud", "https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718"] -maintainers = [{"company": "Splunk", "email": "Mayhem@splunk.com", "name": "Jim Apger"}] -spec_version = 2 -searches = ["ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Web Session Information via session_id"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Jim Apger"}] +spec_version = 3 +searches = ["ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Web Session Information via session id", "ESCU - Get Notable History"] description = Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. narrative = The Federal Bureau of Investigations (FBI) defines Internet fraud as the use of Internet services or software with Internet access to defraud victims or to otherwise take advantage of them. According to the Bureau, Internet crime schemes are used to steal millions of dollars each year from victims and continue to plague the Internet through various methods. The agency includes phishing scams, data breaches, Denial of Service (DOS) attacks, email account compromise, malware, spoofing, and ransomware in this category.\ These crimes are not the fraud itself, but rather the attack techniques commonly employed by fraudsters in their pursuit of data that enables them to commit malicious actssuch as obtaining and using stolen credit cards. They represent a serious problem that is steadily increasing and not likely to go away anytime soon.\ @@ -824,22 +858,22 @@ Another search detects incidents wherein a single password is used across multip [analytic_story://Windows Defense Evasion Tactics] category = Adversary Tactics last_updated = 2018-05-31 -version = 1.0 +version = 1 references = ["https://attack.mitre.org/wiki/Defense_Evasion"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Hiding Files And Directories With Attrib.exe - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Reg.exe Process - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Suspicious Reg exe Process - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others narrative = Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. [analytic_story://Windows File Extension and Association Abuse] category = Malware last_updated = 2018-01-26 -version = 1.0 +version = 1 references = ["https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/", "https://attack.mitre.org/wiki/Technique/T1042"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. narrative = Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ @@ -850,11 +884,11 @@ Run the searches in this story to detect and investigate suspicious behavior tha [analytic_story://Windows Log Manipulation] category = Adversary Tactics last_updated = 2017-09-12 -version = 2.0 +version = 2 references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/", "https://zeltser.com/security-incident-log-review-checklist/", "http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - Deleting Shadow Copies - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Deleting Shadow Copies - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint"] description = Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. narrative = Because attackers often modify system logs to cover their tracks and/or to thwart the investigative process, log monitoring is an industry-recognized best practice. While there are legitimate reasons to manipulate system logs, it is still worthwhile to keep track of who manipulated the logs, when they manipulated them, and in what way they manipulated them (determining which accesses, tools, or utilities were employed). Even if no malicious activity is detected, the knowledge of an attempt to manipulate system logs may be indicative of a broader security risk that should be thoroughly investigated.\ The Analytic Story gives users two different ways to detect manipulation of Windows Event Logs and one way to detect deletion of the Update Sequence Number (USN) Change Journal. The story helps determine the history of the host and the users who have accessed it. Finally, the story aides in investigation by retrieving all the information on the process that caused these events (if the process has been identified). @@ -862,33 +896,33 @@ The Analytic Story gives users two different ways to detect manipulation of Wind [analytic_story://Windows Persistence Techniques] category = Adversary Tactics last_updated = 2018-05-31 -version = 2.0 +version = 2 references = ["http://www.fuzzysecurity.com/tutorials/19.html", "https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html", "http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/", "https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html", "https://www.youtube.com/watch?v=dq2Hv7J9fvk"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}, {"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -spec_version = 2 -searches = ["ESCU - Detect Path Interception By Creation Of program.exe - Rule", "ESCU - Hiding Files And Directories With Attrib.exe - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Reg.exe used to hide files/directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] +spec_version = 3 +searches = ["ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. narrative = Maintaining persistence is one of the first steps taken by attackers after the initial compromise. Attackers leverage various custom and built-in tools to ensure survivability and persistent access within a compromised enterprise. This Analytic Story provides searches to help you identify various behaviors used by attackers to maintain persistent access to a Windows environment. [analytic_story://Windows Privilege Escalation] category = Adversary Tactics last_updated = 2020-02-04 -version = 2.0 +version = 2 references = ["https://attack.mitre.org/tactics/TA0004/"] -maintainers = [{"company": "Splunk", "email": "davidd@splunk.com", "name": "David Dorsey"}] -spec_version = 2 -searches = ["ESCU - Child Processes of Spoolsv.exe - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Registry Activities", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. narrative = Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. [analytic_story://Windows Service Abuse] category = Malware last_updated = 2017-11-02 -version = 3.0 +version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1050", "https://attack.mitre.org/wiki/Technique/T1031"] -maintainers = [{"company": "Splunk", "email": "rvaldez@splunk.com", "name": "Rico Valdez"}] -spec_version = 2 -searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc.exe Manipulating Windows Services - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Previously Seen Running Windows Services"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] +spec_version = 3 +searches = ["ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. narrative = The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to run privileged code that can interact with the kernel. In many cases, attackers will create a new service to host their malicious code. Attackers have also been observed modifying unnecessary or unused services to point to their own code, as opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. @@ -900,366 +934,357 @@ narrative = The Windows operating system uses a services architecture to allow f type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from cities that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +explanation = This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each country. It returns only those events from countries that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +explanation = This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from IP addresses that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +explanation = This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from regions that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +explanation = This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search\ -1. Retrieves the **AssumeRole** event\ -1. Verifies that the log entry contains a value for the account ID of the requesting account\ -1. Ensures that the requesting account ID does not match the account ID of the requested account\ -1. Pulls in the previously seen requesting and requested account IDs\ -1. Splits up and executes multiple search paths at the same.\ -1. The first path determines the **firstTime** and **lastTime** entries for the cache file\ -1. Outputs the data to the cache file.\ -1. Creates a conditional statement that is always false (both because we don't want these values to exit the search pipeline and because we think we're clever).The second pipeline adds the **firstTime** and **lastTime** entries to search results. Next, it filters out any account pairs that haven't been seen for the first time within the last hour. The `isnotnull(_time)` will remove the entries from the cache file.\ -The search finishes by gathering the data that it will display to the user. +explanation = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - AWS Network Access Control List Created with All Open Ports - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = A network access control list (ACL) is a layer of security for your VPC that acts as a firewall for controlling traffic in and out of one or more subnets. Network ACLs with all open ports have a larger attack surface. This search looks for events within your CloudTrail logs to check if there were any Network ACLs created with ports ranging from 1024 to 65525. This search will create a table comprised of AWS account id, src, user and all parameters of the request made by the user and the server response. +explanation = The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. -annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - AWS Network Access Control List Deleted - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The search looks for CloudTrail events to detect whether any network ACLs have been deleted and gives you values of error messages and error codes (if any), user details, user source IP, the user who initiated this request, and the name of the event. +explanation = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has legitimately deleted a network ACL. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Launched by User - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances launched by a particular user, as well as the average and standard deviation values. Assign a `threshold_value` in the search. Start with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. For your reference, we then keep only the outliers and calculate the number of standard deviations away the value is from the average. +explanation = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then compare the total number of instances launched by a particular user against the saved baseline data in the model ec2_excessive_runinstances_v1. +explanation = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances terminated by a particular user, as well as the average- and standard-deviation values. Assign a `threshold_value` in the search. Try starting with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier to 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. We then filter out outliers with a value of 1 and show only those instance-termination events that happened within the previous 10 minutes. +explanation = This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then compare the total number of instances terminated by a particular user against the saved baseline data in the model ec2_excessive_terminateinstances_v1. +explanation = This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Access LSASS Memory for Dump Creation - Rule] type = detection asset_type = Windows -confidence = high -explanation = dbgcore.dll is a specifc DLL for Windows core debugging. It is used to obtain a memory dump of a process. This search detects the usage of this DLL for creating a memory dump of LSASS process. Memory dumps of the LSASS process can be created with tools such as Windows Task Manager or procdump. +confidence = medium +explanation = Detect memory dumping of the LSASS process. how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Amazon EKS Kubernetes Pod scan detection - Rule] type = detection asset_type = Amazon EKS Kubernetes cluster Pod confidence = medium -explanation = In this search we can detect unauthenticated web requests against an EKS cluster Pod, by looking at k8s authentication data, user agent and source IPs and API direct request. +explanation = This search provides detection information on unauthenticated requests against Kubernetes' Pods API how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. -annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery"], "mitre_technique_id": ["T1190"]} +annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Amazon EKS Kubernetes cluster scan detection - Rule] type = detection asset_type = Amazon EKS Kubernetes cluster -confidence = high -explanation = In this search we can detect unauthenticated web requests against an EKS cluster, by looking at k8s authentication data, user agent and source IPs. +confidence = medium +explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. -annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery"], "mitre_technique_id": ["T1190"]} +annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Attempt To Add Certificate To Untrusted Store - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = Attackers will often attempt to disable security tools in order to evade detection. It is also possible for end users to attempt to disable anti-virus or other security tools to circumvent restrictions they encounter while trying to execute other programs. One way malware may accomplish this is by adding the legitimate certificate used to sign the security software to the untrusted certificate store. This will cause the system to no longer trust the software signed with this certificate and disallow it from executing. This search simply looks for the execution of **certutil.exe** with the parameters `-addcert` and `disallowed`, which add a certification to the "untrusted" certificate store. +confidence = medium +explanation = Attempt to add a certificate to the untrusted certificate store how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for changes of the ExecutionPolicy in the registry. The ExecutionPolicy is a safety feature that controls the conditions under which PowerShell loads configuration files and runs scripts. Usually, the ExecutionPolicy is "Restricted" for Windows clients and "RemoteSigned" for Windows Servers, allowing only certain scripts to run. This search detects when an attacker sets the ExecutionPolicy to "Unrestricted" or "Bypass." +confidence = medium +explanation = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. -annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "mitre_technique_id": ["T1086", "T1064"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["DE.CM"]} known_false_positives = Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Attempt To Stop Security Service - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for the processes **net.exe** and **sc.exe** with a parameter of `"stop"`. It then searches a list of security-related services included in a lookup file for matches on the command line. Results are subsequently returned in table format. The included lookup file can be modified to update the services to monitor. +confidence = medium +explanation = This search looks for attempts to stop security-related services on the endpoint. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] -[savedsearch://ESCU - Attempted Credential Dump From Registry via Reg.exe - Rule] +[savedsearch://ESCU - Attempted Credential Dump From Registry via Reg exe - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for the process reg.exe with the "save" parameter, which specifies a binary export from the registry. In addition, it looks for the keys that contain the hashed credentials, which attackers may retrieve and use for brute-force attacks in order to harvest legitimate credentials. +confidence = medium +explanation = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = None identified. -providing_technologies = ["Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Batch File Write to System32 - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications across your hosts, as well as for evidence of batch files being written to paths that include "system32." This activity is consistent with some SamSam attacks and is, in general, suspicious. +confidence = medium +explanation = The search looks for a batch file (.bat) written to the Windows system directory tree. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] -[savedsearch://ESCU - Child Processes of Spoolsv.exe - Rule] +[savedsearch://ESCU - Child Processes of Spoolsv exe - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for child processes of spoolsv.exe, which is associated with the Print Spooler service on Windows. Children of this process typically run under the SYSTEM context. This search should address the POC developed for the Windows local-privilege-escalation exploit announced in September of 2018. The associated vulnerability was assigned CVE-2018-8440. More information is available at https://doublepulsar.com/task-scheduler-alpc-exploit-high-level-analysis-ff08cda6ad4f. +explanation = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. -annotations = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Privilege Escalation", "Exploitation for Privilege Escalation"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} known_false_positives = Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Clients Connecting to Multiple DNS Servers - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = DNS Queries with multiple DNS servers from a single client is unusual and may be indicative of malicious activity. This search works by performing a count by the source of the distinct destinations for the DNS traffic. The search uses the `Network_Resolution` data model. +explanation = This search allows you to identify the endpoints that have connected to more than five DNS servers and made DNS Queries over the time frame of the search. how_to_implement = This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Exfiltration Over Alternative Protocol"], "nist": ["PR.PT", "DE.AE", "PR.DS"]} +annotations = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048"], "nist": ["PR.PT", "DE.AE", "PR.DS"]} known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule] type = detection asset_type = Cloud Compute Instance confidence = medium -explanation = For each user, the search returns the first time seen, last time seen, and the systems. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each user back to the lookup file and then clears out any output. The other part of the search limits the results to when the user was seen for the first time within the previous 70 minutes. It then displays the new user, the instances created by that user, and the associated times. +explanation = This search looks for cloud compute instances created by users who have not created them before. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. -providing_technologies = ["AWS", "Azure", "GCP"] +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule] type = detection asset_type = Cloud Compute Instance confidence = medium -explanation = For each image ID and user, the search returns the first time seen, last time seen, and the systems. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each image back to the lookup file and clears out any output. The other part of the search limits the results to when the image was seen for the first time within the previous 70 minutes. It then displays the new image, the instances created using it, the user who created it, and the associated times. +explanation = This search looks for cloud compute instances being created with previously unseen image IDs. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Images" support search to create a baseline of previously seen images. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. -providing_technologies = ["AWS", "Azure", "GCP"] +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule] type = detection asset_type = Cloud Compute Instance confidence = medium -explanation = For each instance type and user, the search returns the first time seen, last time seen, and the system. It then appends the historical data and merges it into the data. The search then splits and outputs the updated times for each instance type back to the lookup file and clears out any output. The other part of the search limits the results to when the instance type was seen for the first time within the previous 70 minutes. It then displays the new instance type, the instances created using it, the user who created them, and the times associated. +explanation = Find EC2 instances being created with previously unseen instance types. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the " Previously Seen Cloud Compute Instance Types" support search to create a baseline of previously seen regions. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. -providing_technologies = ["AWS", "Azure", "GCP"] +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule] type = detection asset_type = Cloud Compute Instance confidence = medium -explanation = In this search, we query cloud infrastructure compute logs to look for events that indicate that an instance was started in a particular region. Using the \"previously_seen_cloud_regions\" lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The \"eval\" and \"if\" functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with \"Instance Started in a New Region.\" However, this region will be added to the list in \"previously_seen_cloud_regions.\" +explanation = This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the \"Previously Seen Cloud Compute Instance Types\" support search to create a baseline of previously seen regions. -annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. -providing_technologies = ["AWS", "Azure", "GCP"] +providing_technologies = [] [savedsearch://ESCU - Common Ransomware Extensions - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications across your hosts and identifies files with extensions that are commonly associated with the encrypted files generated by ransomware. +confidence = medium +explanation = The search looks for file modifications with extensions commonly used by Ransomware how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\ This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\ 1. \ 1. **Label:** File Extension, **Field:** file_extension\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Common Ransomware Notes - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications in the Change Analysis data model. It checks modified file names against an included lookup file, which contains the names of note files left behind by ransomware (to inform the victim how they can pay the ransom and retrieve their files). The search returns a list of files with matching names. +confidence = medium +explanation = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Create Remote Thread into LSASS - Rule] type = detection asset_type = Windows -confidence = high -explanation = This search detects the creation of a remote thread into LSASS (Local Security Authority Subsystem Service). This technique can be used by attackers to inject code into LSASS and dump the memory in order to obtain credentials. +confidence = medium +explanation = Detect remote thread creation into LSASS consistent with credential dumping. how_to_implement = This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] -[savedsearch://ESCU - Create local admin accounts using net.exe - Rule] +[savedsearch://ESCU - Create local admin accounts using net exe - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Net.exe is a built-in Windows command-line tool that can be used to add, display, or modify user accounts. While Microsoft administrators use this tool to manage user groups, threat actors often leverage it to create local admin accounts to maintain persistence. In this search, we are looking for the execution of process net.exe with command-line parameters such as `localgroup`, `add`, or `user` that may correspond to the creation of local admin accounts or setting user/group properties. +explanation = This search looks for the creation of local administrator accounts using net.exe. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create admin accounts. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] -[savedsearch://ESCU - Create or delete windows shares using net.exe - Rule] +[savedsearch://ESCU - Create or delete windows shares using net exe - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we are looking for the command-line execution of net.exe with command-line parameters such as `net`, `share`, or `delete` that may correspond to the creation/deletion of windows drive shares. Net.exe is a built-in command-line tool on Windows that can be used to create, delete, and manage shared resources on the computer, both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, attackers also leverage it to create and delete (hidden) file shares by appending "$" after the name of the share. Since the creation/deletion of hidden shares is a special case of detecting share creation/deletion we have commented out the regex that adds that additional matching criteria. If only hidden share detection is desired add `| regex process="\S+[$]"` before the last pipe in the search. +explanation = This search looks for the creation or deletion of hidden shares using net.exe. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement"], "mitre_technique_id": ["T1077", "T1126"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Creation of Shadow Copy - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = The ntds.dit file contains the Active Directory (AD) database. This file can't be copied directly. That's why attackers will first create a shadow copy before exfiltrating the file. This search detects the creation of a shadow copy using Ntdsutil, Vssadmin, or Wmic. +confidence = medium +explanation = Monitor for signs that Ntdsutil, Vssadmin, or Wmic has been used to create a shadow copy. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Legtimate administrator usage of Ntdsutil, Vssadmin, or Wmic will create false positives. -providing_technologies = ["Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Creation of Shadow Copy with wmic and powershell - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The ntds.dit file contains the Active Directory (AD) database. This file can't be copied directly. That's why attackers create a shadow copy before exfiltrating the file. This search detects the creation of a shadow copy using wmic, which is executed by Powershell. +explanation = This search detects the use of wmic and Powershell to create a shadow copy. how_to_implement = You must enable Powershell scriptblock logging in order to detect this attack.This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Legtimate administrator usage of wmic to create a shadow copy. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = The file system, security, sam and ntds.dit containing sensitive credentials. Normally, the files can't be easily copied. But it is possible by creating first a shadow copy and then copy it from the shadow copy. This search will detect this attack of credential dumping. +confidence = medium +explanation = This search detects credential dumping using copy command from a shadow copy. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = unknown -providing_technologies = ["Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Credential Dumping via Symlink to Shadow Copy - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = The file system, security, sam, and ntds.dit containing sensitive credentials. Normally, the files can't be easily copied, but it can be done by creating shadow copy and then create a symlink to the shadow copy. This search will detect this attack of credential dumping. +confidence = medium +explanation = This search detects the creation of a symlink to a shadow copy. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = unknown -providing_technologies = ["Sysmon"] +providing_technologies = [] [savedsearch://ESCU - DNS Query Length Outliers - MLTK - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers often use random, long domain names for components of their attack infrastructure. This search leverages the probability distribution function algorithm provided by the Machine Learning Toolkit (MLTK) to identify outliers in the length of the DNS query for each record type observed. The companion search "Baseline of DNS Query Length - MLTK" creates a machine-learning (ML) model built over the historical data used by this search. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +explanation = This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment. how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ 1. \ @@ -1267,59 +1292,59 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su 1. \ 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "mitre_technique_id": ["T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - DNS Query Length With High Standard Deviation - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers often use random, long domain names for their attack infrastructure. This search looks at all the queries observed over the search time frame, and identifies any domains being resolved with names that are greater that 2 times the standard deviation. +explanation = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. -annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "mitre_technique_id": ["T1071"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It's possible there can be long domain names that are legitimate. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Clients should be resolving their DNS requests via a trusted DNS server. This search will identify DNS queries being sent to unauthorized DNS servers by comparing the destination and source of the traffic with assets marked as DNS servers. +explanation = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. -annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Exfiltration", "Defense Evasion"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - DNS record changed - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Using a lookup `discover_dns_records` generated by support search "Discover DNS records" we check previous network traffic and make sure the responses have not changed. +explanation = The search takes the DNS records and their answers results of the discovered_dns_records lookup and finds if any records have changed by searching DNS response from the Network_Resolution datamodel across the last day. how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the `Network_Resolution` data model. It also requires that the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ **Splunk>Phantom Playbook Integration**\ If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ -annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - Deleting Shadow Copies - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for execution of vssadmin or wmic with both the "delete" and "shadows" parameters passed on the command-line. The two arguments are searched for separately because we can't predict the number of spaces between the words on the command-line. The search will return the number of times this activity was observed, and the times of the first and last event. +explanation = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Impact"], "mitre_technique_id": ["T1490"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Detect API activity from users without MFA - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs and specifically look for events where the multi factor authentication context of the user's session is false which basically means, that the user does not have MFA enabled on AWS. We then filter out all the known AWS service accounts since service accounts typically do not have MFA enabled. The search then creates a table of the first and last time a user without MFA was detected, the values and count of the API calls made, the type of user identity, ARN and the name of the user. +explanation = This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them.\ This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ @@ -1327,15 +1352,15 @@ This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) 1. \ 1. **Label:** AWS User Type, **Field:** userIdentity.type\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Execution"], "nist": ["DE.DP", "PR.AC"]} +annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect AWS API Activities From Unapproved Accounts - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we are looking for successful API calls via CloudTrail. We filter out events triggered by known users listed in the `identity_lookup_expanded` lookup file and the service accounts. Once filtered out, we output a table with the event names and count, as well as the first and last time a specific user or service is detected. +explanation = This search looks for successful CloudTrail activity by user accounts that are not listed in the identity table or `aws_service_accounts.csv`. It returns event names and count, as well as the first and last time a specific user or service is detected, grouped by users. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also populate the `identity_lookup_expanded` lookup shipped with the Asset and Identity framework to be able to look up users in your identity table in Enterprise Security (ES). Leverage the support search called "Create a list of approved AWS service accounts": run it once every 30 days to create and validate a list of service accounts.\ This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ @@ -1343,143 +1368,143 @@ This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not ye 1. \ 1. **Label:** Last Time, **Field:** lastTime\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect AWS Console Login by User from New City - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new city and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and city combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect AWS Console Login by User from New Country - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new country and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and country combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect AWS Console Login by User from New Region - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console from a new region and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen ARN and region combinations logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = To detect pass the hash activity, we look at all events with event code 4624 that specify a logon type 3 (network logons) for remote pass the hash attacks and logon type 9 for local pass the hash attacks. The search also filters out events with an account name of 'Anonymous' to help reduce false positives. +confidence = medium +explanation = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Pass the Hash"], "mitre_technique_id": ["T1075"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1075"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Detect Credential Dumping through LSASS access - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search looks for LSASS access using Credential Dumping tools by detecting Process access with Sysmon logs (EventCode 10), TargetImage lsass.exe and GrantedAccess 0x1410 or 0x1010. This will for example detect the use of sekurlsa::logonpasswords in Mimikatz. +explanation = This search looks for reading lsass memory consistent with credential dumping. how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search gathers all the answers to each system's DNS query, then filters for queries that have subdomains extracted from the EvilGinx toolkit. It will then run a regex to extract `legit_domains` from the query and remove that from the detection if it is listed in the `legit_domains.csv` +confidence = medium +explanation = This search looks for DNS requests for phishing domains that are leveraging EvilGinx tools to mimic websites. how_to_implement = You need to ingest data from your DNS logs in the Network_Resolution datamodel. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You will have to add legitimate domain names to the `legit_domains.csv` file shipped with the app. \ **Splunk>Phantom Playbook Integration**\ If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ -annotations = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["Spearphishing Link", "Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1192"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - Detect Excessive Account Lockouts From Endpoint - Rule] type = detection asset_type = Windows -confidence = low -explanation = This search queries the `Change.All_Changes` datamodel under the nodename is `Account_Management` , where the result is "lockout", which indicates that an account has been locked out. It then counts the number of times an endpoint has caused an account lockout within a four hour window and displays those hosts with a count greater than or equal to five. +confidence = medium +explanation = This search identifies endpoints that have caused a relatively high number of account lockouts in a short period. how_to_implement = You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \ **Splunk>Phantom Playbook Integration**\ If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Initial Access", "Valid Accounts"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["PR.IP"]} known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Detect Excessive User Account Lockouts - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search queries the `Change.All_Changes` datamodel under the nodename is `Account_Management` , where the result is "lockout", which indicates that an account has been locked out. It then counts the number of times a user has caused an account lockout within a four hour window and displays those users with a count greater than or equal to five. +explanation = This search detects user accounts that have been locked out a relatively high number of times in a short period. how_to_implement = ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Initial Access", "Valid Accounts"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["PR.IP"]} known_false_positives = It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Detect Large Outbound ICMP Packets - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search works by looking at fields in the Network_Traffic data model, which is populated by various firewalls and passive networking monitoring technologies. Specifically, the search looks for ICMP packets larger than 1,000 bytes with a destination that is external to your organization. +explanation = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model -annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Standard Non-Application Layer Protocol"], "nist": ["DE.AE"]} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1095"], "nist": ["DE.AE"]} known_false_positives = ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the search to adjust the byte threshold or whitelist specific IP addresses, as necessary. -providing_technologies = ["Bro", "Splunk Stream", "Palo Alto Firewall"] +providing_technologies = [] [savedsearch://ESCU - Detect Long DNS TXT Record Response - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search uses the Network_Resolution data model and gathers all the answers to DNS queries for TXT records. The query then looks at the answer section and calculates the length of the answer. The search will then return information for those responses that exceed 100 characters in length. +explanation = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. -annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - Detect Mimikatz Using Loaded Images - Rule] type = detection asset_type = Windows -confidence = high -explanation = This search looks for loaded images (dll) unique for Mimikatz using Sysmon EventCode 7 logs. +confidence = medium +explanation = This search looks for reading loaded Images unique to credential dumping with Mimikatz. how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.AE", "DE.CM"]} known_false_positives = Other tools can import the same DLLs. These tools should be part of a whtelist. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search looks for Windows Event Code(signature_id) 4703 (token right adjusted), where the process requesting the token change is PowerShell.exe and the requested privilege is "SeDebugPrivilege". This is consistent with the use of PowerShell to execute Mimikatz using sekurlsa::logonpasswords. It will return the host where the activity occurred, the process and associated id, the enabled privilege, and the message in the event. +explanation = This search looks for PowerShell requesting privileges consistent with credential dumping. how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Detect New Local Admin account - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search looks for Windows Event Code 4720 (account creation) and 4732 (account added to a security-enabled local group), where the group name is "Administrators", and determines whether they are generated for the same user's Security ID within three hours of each other. It will return the user account that was added, the Security ID, the group name to which the user was added, the account name of the user who initiated the action, and the subsequent message returned. +explanation = This search looks for newly created accounts that have been elevated to local administrators. how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here:http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes.\ This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Security ID, **Field:** Security_ID\ 1. \ @@ -1487,116 +1512,105 @@ This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not 1. \ 1. **Label:** Message, **Field:** Message\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Valid Accounts", "Defense Evasion", "Persistence"], "nist": ["PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1078"], "nist": ["PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Detect New Login Attempts to Routers - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers will often attempt to compromise network devices such as routers for a variety of nefarious purposes, including modifying VPN settings or re-routing network traffic. Typically, only a relatively small number of user accounts log into these devices on a regular basis. This search identifies 'new' connections to your routers by checking to see if a similar login was made in the last 30 days. Routers are identified by checking the IP address against those categorized as a "router" in the ES assets and identity framework. +explanation = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]} known_false_positives = Legitimate router connections may appear as new connections -providing_technologies = ["Active Directory", "Palo Alto Firewall"] +providing_technologies = [] [savedsearch://ESCU - Detect New Open S3 buckets - Rule] type = detection asset_type = S3 Bucket confidence = medium -explanation = This search queries CloudTrail logs for events with S3 bucket access controls given to the "All Users" group, which allows anyone in the world access to the resource. This search generates a table displaying the time when the bucket was made public, the permission of the S3 bucket, the bucket name, and the ARN of the user who created the bucket. +explanation = This search looks for CloudTrail events where a user has created an open/public S3 bucket. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. -providing_technologies = ["AWS"] +providing_technologies = [] -[savedsearch://ESCU - Detect Oulook.exe writing a .zip file - Rule] +[savedsearch://ESCU - Detect Oulook exe writing a zip file - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = In this search, we are essentially trying to detect if outlook.exe is writing a `.zip` file to the disk. The way this search would run is, it will execute the the subsearch first which looks for all .zip files being written to the disk and outputs a crucial field "process_id", that we use the main search to check if that process\_id belongs to a process_name of outlook.exe. The search uses a join command to essentially give you an end result of the first and last time that zip file was written by outlook.exe, the dest and user logged on the system, the hash value and the complete path to the zip file on disk +confidence = medium +explanation = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. -annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Initial Access", "Spearphishing Attachment"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = It is not uncommon for outlook to write legitimate zip files to the disk. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Detect Outbound SMB Traffic - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we are looking for the network connections that were not blocked by the firewall and that are destined for destination port 139 or 445. We then filter out events that have Classless Inter-Domain Routing (CIDR) blocks categorized as internal in the `assets_by_cidr.csv` lookup file which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. Since we are only looking for outbound traffic from the hosts made to the Internet, we filter out traffic whose destination IP address is private. +explanation = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model -annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Commonly Used Port", "Credential Access", "Lateral Movement"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = [] -[savedsearch://ESCU - Detect Path Interception By Creation Of program.exe - Rule] +[savedsearch://ESCU - Detect Path Interception By Creation Of program exe - Rule] type = detection asset_type = confidence = medium -explanation = This search queries the Endpoint file-system data model node to list out all the values of destination machines, as well as the values of file hashes and file paths that have the file "program.exe" in the C: drive. Path interception occurs when an executable is placed in a specific path so that it is executed by an application instead of by the intended target. In this case, applications vulnerable to path interception (because of unquoted service paths with spaces in Windows registry) allow attackers to execute maliciously crafted program.exes. +explanation = The search is looking for the creation of program.exe in the C: drive. The creation of this file in that location may be driven by a motive to perform path interception. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Privilege Escalation", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is unlikely that a normal user may create and place this file in the C: drive. Confirm with the user. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Tanium", "Ziften"] +providing_technologies = [] -[savedsearch://ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] +[savedsearch://ESCU - Detect Prohibited Applications Spawning cmd exe - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Obtaining access to the Command-Line Interface (CLI) is typically a primary attacker goal. Once an attacker has obtained the ability to execute code on a target system, they will often further manipulate the system via commands passed to the CLI. It is also unusual for many applications to spawn a command shell during normal operation, while it is often observed if an application has been compromised in some way. As such, it is often beneficial to look for cmd.exe being executed by processes that are often targeted for exploitation, or that would not spawn cmd.exe in any other circumstances. A lookup file is provided to easily modify the processes that are being watched for execution of cmd.exe. +explanation = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution", "Command-Line Interface"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Detect PsExec With accepteula Flag - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we are looking for the PsExec process with `accepteula` on the command line. +explanation = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine -providing_technologies = ["Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Detect Rare Executables - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search first executes the subsearch and counts all of your processes to determine the 10 most rare (the limit set is 10). It then filters out whitelisted processes and outputs the first and last time a rare process was encountered, the destination where the process is running, the count of occurrences, and the users who initiated the processes. +explanation = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_whitelist` searches two lookup files to whitelist your processes. These consist of `rare_process_whitelist_default.csv` and `rare_process_whitelist_local.csv`. To add your own processes to the whitelist, add them to `rare_process_whitelist_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the whitelist value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. -annotations = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]} +annotations = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]} known_false_positives = Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_whitelist_local.csv` to filter them out of your search results. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Detect S3 access from a new IP - Rule] type = detection asset_type = S3 Bucket -confidence = low -explanation = Here the subsearch executes first and returns all successful S3 bucket-access attempts (HTTP code "200") within the last hour. It groups the results by the earliest and latest times it has seen a remote IP accessing a particular bucket. It appends this information to the historical data from the lookup file and then recalculates the `firstTime` and `lastTime` field for each remote IP accessing an S3 bucket. Next, it returns only those remote IP addresses that have first been seen accessing a specific bucket within the past hour. This is combined with the main search to return the time, bucket name, source IP, city, and country operations performed, as well as the requested URI of the resource +confidence = medium +explanation = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. -annotations = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Exfiltration"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +annotations = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect Spike in AWS API Activity - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search and its corresponding subsearch run through a series of steps, as per the following: \ -1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls.\ -1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -1. Counts the number of API calls per ARN.\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -1. Renames `apiCalls` as `latestCount`.\ -1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\ -1. Updates the cache file with the latest results.\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +explanation = This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.\ This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** AWS Event Name, **Field:** eventName\ 1. \ @@ -1604,137 +1618,95 @@ This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) 1. \ 1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect Spike in Network ACL Activity - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search and its corresponding subsearch run through the following series of steps: \ -1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for creating/modifying/replacing network Access Control Lists (ACLs).\ -1. Kick off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -1. Count the number of API calls per Amazon Resource Name (ARN).\ -1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drop the count from the latest hour, since it is not necessary, and merge the rest of the data with the results of the stats command. \ -1. Rename `apiCalls` as `latestCount`.\ -1. Calculate the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. They do the same for the standard deviation--weighting the past more heavily than the current.\ -1. Update the cache file with the latest results.\ -1. Set the minimum threshold for the number of data points and set the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filter out anything that it determines is not a spike and return the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +explanation = This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. -annotations = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Exfiltration"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect Spike in S3 Bucket deletion - Rule] type = detection asset_type = S3 Bucket confidence = medium -explanation = This search and its corresponding subsearch run through the following series of steps: \ -1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for deletion of S3 buckets.\ -1. Kick off a subsearch that retrieves the same data and pulls out and converts the ARN into a more friendly format.\ -1. Count the number of API calls per ARN.\ -1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drop the count from the latest hour, since it is unnecessary, and merge the rest of the data with the results of the `stats` command. \ -1. Rename `apiCalls` as `latestCount`.\ -1. Calculate the new average value for each ARN with the latest count, weighting the past more heavily than the current hour. It does the same for the standard deviation—weighting the past more heavily than the current.\ -1. Update the cache file with the latest results.\ -1. Set the minimum threshold for the number of data points and the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and if the count is a sufficient number of standard deviations away from the average.\ -1. Filter out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of the deleted S3 buckets, the number of unique API calls, and the total number of API calls for each of these user ARNs. +explanation = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect Spike in Security Group Activity - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search and its corresponding subsearch run through the following series of steps: \ -1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls specifically for security groups.\ -1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -1. Counts the number of API calls per ARN.\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -1. Renames `apiCalls` as `latestCount`.\ -1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\ -1. Updates the cache file with the latest results.\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +explanation = This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Execution"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search retrieves all the VPC Flow log entries that have recorded a blocked outbound network connection originating from your AWS environment. Then it kicks off a subsearch, which looks at the same data and performs the following series of steps: \ -1. Counts the number of blocked outbound connections by each source IP\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the average blocked connections, and the standard deviation for each source IP.\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -1. Renames `numberOfBlockedConnections` as `latestCount`.\ -1. Calculates the new average value for each source IP with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation, weighting the past more heavily than the current.\ -1. Updates the cache file with the latest results.\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -1. Filters out anything that it determines is not a spike and returns the list of source IPs to the main search. The main search subsequently gets the list of all destination IPs for which the traffic was blocked, the network interface ID, the number of unique destination IP, and the total number of blocked connections for each of these source IP addresses. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +explanation = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. -annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["Exfiltration", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect USB device insertion - Rule] type = detection asset_type = Endpoint -confidence = low -explanation = USB is a common attack vector for delivering or propagating malicious code, or the exfiltration of data. Your corporation may have a policy of not allowing removable media at all, or may only allow approved media to be used on specific hosts by specific users. By logging USB activity from Windows and other endpoints gathered using the Universal Forwarder, you can gain an understanding of what systems might be vulnerable to attack via removable media, or what users might need additional security training. This search is looking for event_id 4656 for failure and 4663 for successful USB read/write attempts from Windows Security Event logs, which is the event code generated when a files are read from and written to a removable storage device +confidence = medium +explanation = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Exfiltration"], "nist": ["PR.PT", "PR.DS"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"]} known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Detect Unauthorized Assets by MAC address - Rule] type = detection asset_type = Infrastructure confidence = medium -explanation = This search requires you to leverage the Enterprise Security Assets and Identity framework to populate assets_by_str.csv. Once the assets_by_str.csv is populated, we then query your DHCP logs to detect unknown systems connecting to your network. More documentation is available at: http://docs.splunk.com/Documentation/ES/4.7.1/Admin/Verifyassetandidentitydata. +explanation = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. how_to_implement = This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. -annotations = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] -[savedsearch://ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule] +[savedsearch://ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers often leverage various scripting languages to execute their attacks. In a Windows environment, the Windows Script Host is the tool that interprets the scripts and is included in all modern versions of Windows. The Windows Script Host is available as a command-line tool called "cscript.exe" or "wscript.exe." To detect this behavior, the search looks for process-creation events for cscript.exe or wscript.exe with a parent process of cmd.exe. The search will return the count, the first and last times this behavior was seen on a destination machine, and user and process information. +explanation = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution", "Command-Line Interface"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications may exhibit this behavior. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] type = detection asset_type = Web Server confidence = medium -explanation = This search returns the number of times a URL associated with this type of JexBoss probe is observed. +explanation = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. -annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery", "System Information Discovery"]} +annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1082"]} known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. -providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +providing_technologies = [] [savedsearch://ESCU - Detect hosts connecting to dynamic domain providers - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search leverages an accelerated `Network_Resolution` data model to count and list the values of resolved domains for each DNS query. It checks the results against the list of Dynamic DNS providers in the lookup `dynamic_dns_providers` by each host (DNS.src). +explanation = Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, blacklists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains. how_to_implement = First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\ This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\ 1. \ @@ -1742,1204 +1714,1294 @@ This search produces fields (query, answer, isDynDNS) that are not yet supported 1. \ 1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Exfiltration", "Defense Evasion"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} known_false_positives = Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - Rule] type = detection asset_type = Web Server -confidence = high -explanation = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers. +confidence = medium +explanation = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model -annotations = {"cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} +annotations = {"cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} known_false_positives = No known false positives for this detection. -providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +providing_technologies = [] -[savedsearch://ESCU - Detect mshta.exe running scripts in command-line arguments - Rule] +[savedsearch://ESCU - Detect mshta exe running scripts in command-line arguments - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Mshta.exe is a built-in Windows utility that can launch HTML files with .hta extensions (HTML applications), javascript, or VBScript. The search detects this behavior by looking for events where the process mshta.exe is executed with command-line arguments that indicate that a script is invoked +explanation = This search looks for the execution of "mshta.exe" with command-line arguments that launch a script. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Detect new API calls from user roles - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch will execute first and return the user roles and names of the API calls completed within the last hour, where the type of user identity is `AssumedRole`. It then appends the historical data to those results in the lookup file. Next, it recalculates the `earliest` and `latest` fields for each user role, as well as the name of the API call, and returns only those roles and API calls that have first been seen in the past hour. This is combined with the main search to return the values of API calls, name of the user role, and the earliest and latest time of this activity. It is worth noting that the name of the role of a particular user is parsed as "userName" in the CloudTrail logs. +explanation = This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect new user AWS Console Login - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen users logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Detect processes used for System Network Configuration Discovery - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = Attackers have a range of built-in Windows tools they leverage to ascertain the topography of a network from the point of view of a compromised machine. It is uncommon to see these commands execute quickly within short periods of time. This search returns the number of times, as well as the first time and last times, that every process has run for each endpoint. It then executes the macro `system_network_configuration_discovery_tools`, which looks for processes that are typically used for network configuration discovery. Once you have a list of suspicious process launches for each destination, you can leverage the transaction command to see what processes are fired within a five-minute span on an endpoint and detect only those events where the count of these processes is greater than five. +confidence = medium +explanation = This search looks for fast execution of processes used for system network configuration discovery on the endpoint. how_to_implement = You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. -annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Detect web traffic to dynamic domain providers - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for hosts in your environment that may be communicating with a dynamic DNS provider. It checks each URL an endpoint is connecting to against a list of dynamic DNS providers. It returns the source and destination IP address of the web request, the URL requested, and the first time the event occurred. +confidence = medium +explanation = This search looks for web connections to dynamic DNS providers. how_to_implement = This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Command and Control", "Web Service", "Exfiltration Over Command and Control Channel", "Defense Evasion"], "nist": ["PR.IP", "DE.DP"]} +annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1102", "T1041"], "nist": ["PR.IP", "DE.DP"]} known_false_positives = It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +providing_technologies = [] [savedsearch://ESCU - Detection of DNS Tunnels - Rule] type = detection asset_type = Endpoint -confidence = low -explanation = The search will calculate the distinct count and sum of the length of DNS queries made and DNS answers received by a particular host to alert the analyst if the combined length is greater than 10000, which is not typical behavior. +confidence = medium +explanation = This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. how_to_implement = To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. -annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "nist": ["PR.PT", "PR.DS"]} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "PR.DS"]} known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - Detection of tools built by NirSoft - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for process-creation events accompanied by specific command-line arguments ("scomma" and "stext"). These parameters may be leveraged by a set of free, legitimate tools built by NirSoft. Attackers have been seen abusing the tools' capabilities to steal passwords, set up key loggers, recover account information from mail clients, and conduct other nefarious activities. The search will identify the count, the first and last times a process is executed, the command-line arguments, and the parent process. +explanation = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Discovery", "Execution", "Lateral Movement", "Third-party Software", "Account Discovery"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072", "T1087"], "nist": ["PR.IP"]} known_false_positives = While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Disabling Remote User Account Control - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search checks to see if the registry key SOFTWARE\Microsoft\Windows\CurrentVersion\Policies\System\LocalAccountTokenFilterPolicy was modified. This registry key can be used to disable remote User Account Control. The search returns the count, the first time activity was seen, last time activity was seen, the registry path that was modified, the host where the modification took place and the user that performed the modification. +explanation = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Modify Registry"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Dump LSASS via comsvcs DLL - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = LSASS is the Local Security Authority Subsystem Service, which is responsible for storing the user credentials. There are multiple ways to attack LSASS. This search detects the usage of comsvcs.dll for dumping the LSASS process. +confidence = medium +explanation = Detect the usage of comsvcs.dll for dumping the lsass process. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = None identified. -providing_technologies = ["Sysmon"] +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Modified With Previously Unseen User - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns the ARNs of all successful EC2 instance modifications within the last hour and then appends the historical data in the lookup file to those results. EC2 modification APIs are defined by the macro `ec2_modification_api_calls`. The search then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance ID of those systems. +explanation = This search looks for EC2 instances being modified by users who have not previously modified them. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started In Previously Unseen Region - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that an instance was started in a particular region. Using the `previously_seen_aws_regions.csv` lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The `eval` and `if` functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with "Instance Started in a New Region". However, this region will be added to the list of `previously_seen_aws_regions.csv`. Please maintain `previously_seen_aws_regions.csv` +explanation = This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. -annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion"], "nist": ["DE.DP", "DE.AE"]} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen AMI - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns the AMI image ID of all successful EC2 instance launches within the last hour and then appends the historical data from the lookup file to those results. It then recalculates the earliest and latest seen time field for each AMI image ID and returns only those AMI image IDs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +explanation = This search looks for EC2 instances being created with previously unseen AMIs. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns the instance types of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the earliest seen time field for each instance type and returns only those instance types that has first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +explanation = This search looks for EC2 instances being created with previously unseen instance types. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen User - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = The subsearch returns the ARNs of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +explanation = This search looks for EC2 instances being created by users who have not created them before. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - Email Attachments With Lots Of Spaces - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at any emails with file attachment names that contain many spaces, relative to the length of the file name. Specifically, it checks to see whether spaces make up more than 10% of the number of characters in the file name. This percentage can be tuned for each environment. The search will output the message ID of the email, the count, the sender and recipient addresses, the first and last time this event was seen, and the space ratio of the file attachment name. +confidence = medium +explanation = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names. how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ **Splunk Phantom Playbook Integration**\ If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. -annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = ["Microsoft Exchange"] +providing_technologies = [] [savedsearch://ESCU - Email files written outside of the Outlook directory - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we are looking for activities consistent with an adversary collecting email data from local machines. The search will detect email files (files with .pst or .ost extensions) created in directories other than the standard Outlook directory (c:\users\username\My Documents\Outlook Files\. +explanation = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Collection", "Email Collection"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114"]} known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Email servers sending high volume traffic to hosts - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search may look complex, but it's a neat representation of how statistics can help you understand your dataset to bubble up events that are not normal compared to its behavior. The search consists of three parts. The first part of the SPL fetches the data you want to work on. In this search, we calculate the sum of bytes sent and bytes_out from systems categorized as email_server to each host. We then calculate the average and standard deviation for the bytes sent to all the hosts combined and on a per-host basis. Then we set threshold values to deviation_threshold and minimum_data_samples using eval statements. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. We then check for byte transfers that are statistically significantly higher than normal. The search then gives IP address of the host, the time of the increased byte transfer, how much data was transferred, and the average amount of data transfer the email server normally sends to all hosts and to this specific host. Finally, it includes the number of standard deviations away the byte count was from these averages. +explanation = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. -annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Collection", "Email Collection", "Commonly Used Port"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114", "T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - Excessive DNS Failures - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks at DNS traffic with a reply code that is NOT indicative of a successful response. Numerous unsuccessful replies may be indicative of DNS protocol tampering or other malicious activity. If more than 50 of these unsuccessful responses are observed over the time frame of the search, a notable event will be generated. +explanation = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. -annotations = {"cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Exfiltration", "Exfiltration Over Alternative Protocol", "Command and Control", "Commonly Used Port"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048", "T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - Execution of File With Spaces Before Extension - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search uses the endpoint data model to look for process names with at least five spaces between the file name and its extension. +explanation = This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Execution of File with Multiple Extensions - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search uses the "Application State" data model to look for process names with specific combinations of double extensions. Relatively straightforward, the search looks for strings in the "process" field that match what you're looking for. +confidence = medium +explanation = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. -annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Extended Period Without Successful Netbackup Backups - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search finds all the successful backup messages in your logs, and then looks for the most recent backup time for each system. It then identifies those systems where the most recent successful backup time is over a week ago, and reports on them. +confidence = medium +explanation = This search returns a list of hosts that have not successfully completed a backup in over a week. how_to_implement = To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} known_false_positives = None identified -providing_technologies = ["Netbackup"] +providing_technologies = [] [savedsearch://ESCU - File with Samsam Extension - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications across your hosts and creates notable events when it identifies files with extensions associated with the SamSam ransomware, including `.stubbin`, `.berkshire`, `.satoshi`, `.sophos`, or `.keyxml`. Files with these extensions have been observed in SamSam attacks consisting of payload data or keying material. +confidence = medium +explanation = The search looks for file writes with extensions consistent with a SamSam ransomware attack. how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Because these extensions are not typically used in normal operations, you should investigate all results. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] + +[savedsearch://ESCU - First Time Seen Child Process of Zoom - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +providing_technologies = [] [savedsearch://ESCU - First Time Seen Running Windows Service - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for a change in the status of a Windows service and extracts the name of the service and the action taken by the service. Then the cache file of previously seen Windows services is added to the search. At this point, the search takes two different paths: the first updates the cache file with the latest information and the second searches for services that have never before been seen. It returns the time, the Windows host name, and the service name. +explanation = This search looks for the first time a Windows service is seen running in your environment. how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs in order for this search to execute successfully. The support search, `Previously Seen Running Windows Services`, should be run before this search to create the baseline of known Windows services. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. -annotations = {"cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Execution", "New Service"], "mitre_technique_id": ["T1050"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]} +annotations = {"cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1050"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]} known_false_positives = A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - First time seen command line argument - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The subsearch returns all events where `cmd.exe` was used with a `/c` parameter in the command-line arguments to execute other commands/programs. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for command-line execution and outputs this data to the lookup file to update the local cache. It returns only those events that have first been seen in the past one hour. This is combined with the main search to return the time, user, destination, process, parent process, and value of the command-line argument. +explanation = This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. -annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "Scripting", "Persistence", "Command-Line Interface"], "mitre_technique_id": ["T1059", "T1117", "T1202"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1064", "T1059"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - GCP GCR container uploaded - Rule] type = detection asset_type = GCP GCR Container confidence = medium -explanation = In this search we can detect if a new container has been uploaded to Google Container Registry, operator can monitor users uploading containers, object paths of new uploaded containers. +explanation = This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. -annotations = {"mitre_attack": ["Persistence"], "mitre_technique_id": ["T1525"]} +annotations = {} known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. -providing_technologies = ["GCP"] +providing_technologies = [] [savedsearch://ESCU - GCP Kubernetes cluster scan detection - Rule] type = detection asset_type = GCP Kubernetes cluster -confidence = high -explanation = In this search we can detect unauthenticated web requests and possible attack against a GCP cluster, by looking at k8s authentication data, user agent, source IPs and destionation +confidence = medium +explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. -annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["Discovery"], "mitre_technique_id": ["T1190"]} +annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. -providing_technologies = ["GCP"] +providing_technologies = [] -[savedsearch://ESCU - Hiding Files And Directories With Attrib.exe - Rule] +[savedsearch://ESCU - Hiding Files And Directories With Attrib exe - Rule] type = detection asset_type = confidence = medium -explanation = This search is looking to detect command-line execution with of attrib.exe binary with the +h flag set. The +h flag is used to hide a file. +explanation = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Persistence"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Hosts receiving high volume of network traffic from email server - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search may look complex, but it's a neat representation of how statistics can help you understand your dataset to bubble up events that are not normal compared to its behavior. The search consists of three parts. The first part of the SPL fetches the data you want to work on. In this search, we calculate the sum of bytes sent and bytes_out from systems categorized as email_server to each host. We then calculate the average and standard deviation for the bytes sent to all the hosts combined and on a per-host basis. Then we set threshold values to deviation_threshold and minimum_data_samples using eval statements. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. We then check for byte transfers that are statistically significantly higher than normal. The search then gives IP address of the host, the time of the increased byte transfer, how much data was transferred, and the average amount of data transfer the email server normally sends to all hosts and to this specific host. Finally, it includes the number of standard deviations away the byte count was from these averages. +explanation = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. -annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Collection", "Commonly Used Port"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - Identify New User Accounts - Rule] type = detection asset_type = Domain Server confidence = medium -explanation = Adversaries will often seek to create new user accounts as a means of maintaining access to a target environment. Using this search, we identify accounts created in the last week by comparing the start date in the Identity_Management data model against the current time. +explanation = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Persistence", "Create Account"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1136"], "nist": ["PR.IP"]} known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. -providing_technologies = ["Active Directory"] +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect RBAC authorization by account - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect sensitive object access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect sensitive role access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts with failure or forbidden access status +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubectl calls with IP, verb namespace and object access context +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure pod scan fingerprint - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Reconnaissance"]} +known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure scan fingerprint - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Reconnaissance"]} +known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +providing_technologies = [] [savedsearch://ESCU - Large Volume of DNS ANY Queries - Rule] type = detection asset_type = DNS Servers -confidence = high -explanation = This search counts the number of DNS ANY queries received in 5 minutes, and generates a Notable Event if the count exceeds a predefined threshold. The search returns the count, the first time, and the last time a DNS packet was observed with the ANY flag set. +confidence = medium +explanation = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. annotations = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.IP"]} known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - MacOS - Re-opened Applications - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks at the Endpoint data model to identify any MacOS process events referencing a property list file which determines which applications are "re-opened" during startup. This could indicate a malicious attempt to establish persistence on the system. +explanation = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. how_to_implement = In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "mitre_attack": ["Persistence"], "mitre_technique_id": ["T1164"], "nist": ["DE.DP", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM"]} known_false_positives = At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be whitelisted. -providing_technologies = ["OSquery"] +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes running with specific command-line arguments that indicate that the process will download a file from the Internet without display anything to the user. The search for "*-Exec*" is to check and see if the default execution policy for PowerShell is being overridden on the command-line. The search for "*-WindowStyle*" and "*hidden*" are to see if the window that would normally be displayed will be hidden from the user instead. Finally, the search for "*New-Object*" and "*System.Net.WebClient*" are there to check to see if a PowerShell object that can be used to download files will be created. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +explanation = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes that are passing encoded commands on the command-line. The flags "-EncodedCommand" and "-enc" are two different possible flags that can be used to pass base64 encoded commands to PowerShell. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +explanation = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = System administrators may use this option, but it's not common. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes that were launched using a parameter designed to bypass the local PowerShell execution policy. By default, the policy is set to "Restricted," which disables the execution of PowerShell scripts. In environments that make heavy use of PowerShell, the policy can be set to allow only scripts signed by a trusted publisher. Malicious PowerShell use almost always includes the parameter `-ExecutionPolicy bypass`. PowerShell is very liberal when it comes to interpreting command-line parameters passed to it. For example, the parameter we look for, `-ExecutionPolicy`, can be abbreviated to `-Execution`, `-Exec`, or even `-ex`. As such, we look for `* -ex*`, which should catch all variations of this parameter, followed by the keyword `bypass`. This search will return the host, the user the process ran under, the process and its command-line arguments, the number of times it has seen this process, and the first and last times it saw this process. +explanation = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes that have a number of suspicious flags on the command-line. It is looking for flags are passing encoded commands on the command-line. The flags `-EncodedCommand` and `-enc` are two different possible flags that can be used to pass base64 encoded commands to PowerShell. The `*-Exec*` flag looks to see it the default execution policy of PowerShell is being overridden, while the `*-NonI*` flag tells the PowerShell process that this will be a noninteractive process, so the user doesn't know about the process. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +explanation = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for PowerShell processes that are passing command-line arguments with unusual characters (backticks and carets) that are PowerShell specific escape characters. Attackers use this obfuscation technique since it does not affect the functionality of PowerShell and it will bypass standard security controls that look for straight up malicious strings and commands. The search counts the occurrence of these obfuscation characters and lists out destination IPs running these PowerShell commands. +explanation = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution", "PowerShell", "Scripting"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = These characters might be legitimately on the command-line, but it is not common. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Monitor DNS For Brand Abuse - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search gathers all the answers to each system's DNS query, then filters out all queries that do not appear on the list of faux "look-a-like" domains that have been generated from the brand abuse domains you are monitoring. +confidence = medium +explanation = This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. annotations = {"kill_chain_phases": ["Delivery", "Actions on Objectives"]} known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - Monitor Email For Brand Abuse - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at the sender address in email headers, and identifies those with a sender address using a domain name that matches the list of permutations generated for the domain you want to monitor. +confidence = medium +explanation = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = ["Microsoft Exchange", "Bro", "Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - Monitor Registry Keys for Print Monitors - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we look for modifications to registry keys used for adding print-monitor entries on Microsoft platforms via the `registry_path` field in the endpoint data model. It then provides the destination, command used to initiate the change, the user who conducted this activity, the resource affected (registry_key_name), and the entire path of the registry. +explanation = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. -annotations = {"cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Privilege Escalation", "Local Port Monitor"], "nist": ["PR.PT", "DE.CM", "PR.AC"]} +annotations = {"cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.AC"]} known_false_positives = You will encounter noise from legitimate print-monitor registry entries. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Monitor Web Traffic For Brand Abuse - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at all the URLs an endpoint is connecting to and then checks the URL against a list of faux domains that could be indicative of brand abuse. +confidence = medium +explanation = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. -annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +providing_technologies = [] [savedsearch://ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule] type = detection asset_type = Infrastructure -confidence = high -explanation = This search detects instances when there are more than 5 distinct users failing Okta logins due to invalid credentails from the same IP address. This may be indicative of attack techniques such as credential stuffing or password spraying, where an attacker attempts to login using common or found passwords and attempts to authenticate with them. +confidence = medium +explanation = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro XXXXXXXXXXXXX to raise the threshold or except specific IP adresses from triggering this search. -providing_technologies = ["Okta"] +providing_technologies = [] [savedsearch://ESCU - New container uploaded to AWS ECR - Rule] type = detection asset_type = AWS ECR container confidence = medium -explanation = In this search we can detect if a new container has been uploaded to Amazon Elastic Container Registry, operator can monitor users uploading containers, image ids of new uploaded containers. +explanation = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. -annotations = {"mitre_attack": ["Persistence"], "mitre_technique_id": ["T1525"]} +annotations = {} known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. -providing_technologies = ["AWS"] +providing_technologies = [] [savedsearch://ESCU - No Windows Updates in a time frame - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Keeping your systems up-to-date with the latest patches is an important step in keeping your systems secured. For Windows endpoints, Microsoft typically releases patches on the second Tuesday of every month. These patches contain fixes for vulnerabilities in the system that could potentially be exploited by malicious actors. This search checks for messages regarding Windows updates in the 'Update' data model. If a message indicating a successful update has not been observed in 60 days, a notable event will be generated. These systems should be checked to determine why it has not been updated in that time frame. +explanation = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. annotations = {"cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"]} known_false_positives = None identified -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Okta Account Lockout Events - Rule] type = detection asset_type = Infrastructure -confidence = high -explanation = This search detects when a user exceeds the maximum configured Okta login attempts and the account is subsequently locked out. This is often indicative of brtue force attempts against a user account. +confidence = medium +explanation = Detect Okta user lockout events how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. -providing_technologies = ["Okta"] +providing_technologies = [] [savedsearch://ESCU - Okta Failed SSO Attempts - Rule] type = detection asset_type = Infrastructure -confidence = high -explanation = This search looks for events that indicate a user attempted to access an app they did not have permissions to access. This could indicate attempts to access prohibited applications. Please leverage the `okta_failed_sso_attempt_filter` macro to filter out false positives +confidence = medium +explanation = Detect failed Okta SSO events how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = There may be a faulty config preventing legitmate users from accessing apps they should have access to. -providing_technologies = ["Okta"] +providing_technologies = [] [savedsearch://ESCU - Okta User Logins From Multiple Cities - Rule] type = detection asset_type = Infrastructure -confidence = high -explanation = This search detects users logging in from multiple states in the last 24 hours. This can be indicative of an attacker using compromised credentials to log in to Okta. The efficacy of this search is highly dependant on the mobility of the users using Okta. It is particularly useful in situations where users should explicitly *not* be travelling, such as during the COVID-19 pandemic. +confidence = medium +explanation = This search detects logins from the same user from different states in a 24 hour period. how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Valid Accounts"], "mitre_technique_id": ["T1078"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. -providing_technologies = ["Okta"] +providing_technologies = [] [savedsearch://ESCU - Open Redirect in Splunk Web - Rule] type = detection asset_type = Splunk Server confidence = medium -explanation = This search looks within Splunk's internal logs for evidence of CVE-2016-4859 open redirect exploitation attempts. +explanation = This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. how_to_implement = No extra steps needed to implement this search. -annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} known_false_positives = None identified -providing_technologies = ["Splunk Enterprise"] +providing_technologies = [] [savedsearch://ESCU - Osquery pack - ColdRoot detection - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks at the Alerts data model to identify those generated from the osquery osx-attacks.conf pack, which search for the ColdRoot RAT. +explanation = This search looks for ColdRoot events from the osx-attacks osquery pack. how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model -annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "mitre_attack": ["Execution", "Persistence", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]} +annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]} known_false_positives = There are no known false positives. -providing_technologies = ["OSquery"] +providing_technologies = [] [savedsearch://ESCU - Overwriting Accessibility Binaries - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search returns all the different accessibility binaries that have been modified for each Windows host. +confidence = medium +explanation = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Accessibility Features"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Process Execution via WMI - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for processes launched via WMI, either remotely or locally, by looking for processes launched by WmiPrvSE.exe, which is the process WMI uses to execute new processes and commands. +explanation = This search looks for processes launched via WMI. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes. -providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Processes Tapping Keyboard Events - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search leverages Alerts generated from the osquery osx-attacks.conf pack search `Keyboard_Event_Taps` to detect when a process is monitoring the keystrokes of a machine, This is a common technique used by macOS remote access trojans to log keystrokes from a machine +explanation = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. -annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Collection"], "nist": ["DE.DP"]} +annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP"]} known_false_positives = There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. -providing_technologies = ["OSquery"] +providing_technologies = [] [savedsearch://ESCU - Processes created by netsh - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for all processes with the parent process "c:\Windows\System32\netsh.exe" and returns the process, the command line used to execute it, the host name, and the user context under which it ran. +explanation = This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. how_to_implement = To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Processes launching netsh - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for all the parent processes of netsh.exe and returns that process, the command-line used to execute it, the host name, and the user context under which it ran. +explanation = This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Command-Line Interface", "Persistence", "Defense Evasion", "Disabling Security Tools"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059", "T1089"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Prohibited Network Traffic Allowed - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for traffic marked 'is_prohibited' in the Enterprise Security lookup table 'interesting_ports_lookup', and then determines if any network devices have an associated 'allow' action on that traffic by checking the Network_Traffic data model. +explanation = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. -annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "nist": ["DE.AE", "PR.AC"]} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified -providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - Prohibited Software On Endpoint - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search returns the number of times, as well as the first and last time, every process has run for each endpoint and user. It then displays only those processes that you have marked as "prohibited" in the Enterprise Security "Interesting Processes" table. +confidence = medium +explanation = This search looks for applications on the endpoint that you have marked as prohibited. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response products, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. -annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Protocol or Port Mismatch - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for instances in which the protocol observed is not consistent with the port and transport protocol typically used for that protocol. For example, looking for network traffic other than HTTP running over TCP port 80. Such behavior could indicate a misconfiguration or a custom command and control protocol that has been designed to look like ordinary web traffic. The search will also identify if HTTP traffic is observed running on unexpected ports. This can be common in many environments. +explanation = This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. how_to_implement = Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. -annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Commonly Used Port"], "nist": ["DE.AE", "PR.AC"]} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified -providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - Protocols passing authentication in cleartext - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search is checking for traffic on well-known ports that are associated with protocols that pass authentication in cleartext. +explanation = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. -annotations = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "mitre_attack": ["Credential Access", "Lateral Movement", "Collection"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} +annotations = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} known_false_positives = Some networks may use kerberized FTP or telnet servers, however, this is rare. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] -[savedsearch://ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule] +[savedsearch://ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for modifications to registry paths that specify the definition and configuration of Windows services by reg.exe. Reg.exe is a Windows utility that allows for manipulation of the registry via the command line. Malware often uses the Windows services architecture to persist, hide in plain sight, and gain the ability to interact with the Windows kernel. While it is common to modify the configuration of Windows services (and new services may be created with software installs), the use of reg.exe to create or modify a service configuration is unusual and a technique commonly used by attackers. The search returns the count, the first time the activity was seen, the last time activity was seen, the registry path that was modified, the host where the modification took place, and the user that performed the modification. +confidence = medium +explanation = The search looks for reg.exe modifying registry keys that define Windows services and their configurations. how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["Persistence", "Privilege Escalation", "New Service", "Modify Existing Service", "Defense Evasion", "Disabling Security Tools"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} known_false_positives = It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] -[savedsearch://ESCU - Reg.exe used to hide files/directories via registry keys - Rule] +[savedsearch://ESCU - Reg exe used to hide files directories via registry keys - Rule] type = detection asset_type = confidence = medium -explanation = Reg.exe is a binary native to Windows platform used to edit the registry hives of the system. Attackers can leverage this binary to hide files by passing in arguments that are used to hide the files. In the search, we first gather results with keywords, add, Hidden, and REG_DWORD, that will be in the raw event and filter by process and the command-line. We then leverage regular expressions on the command-line field to look for /d value as 2 which is responsible for hiding a file or directory. +explanation = The search looks for command-line arguments used to hide a file or directory using the reg add command. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Persistence"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = None at the moment -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Registry Keys Used For Persistence - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for specific registry paths that malware often uses to ensure survivability and persistence on system startup. The search returns the count, the first time the activity was seen, the last time the activity was seen, the registry path that was modified, the host where the modification took place and the user that performed the modification. +explanation = The search looks for modifications to registry keys that can be used to launch an application or service at system startup. how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Registry Run Keys / Start Folder", "AppInit DLLs", "Authentication Package"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1103", "T1131"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Registry Keys Used For Privilege Escalation - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for specific registry paths that malware often uses to elevate privileges. The search returns the count, the first time the activity was seen, the last time the activity was seen, the registry path that was modified, the host where the modification took place, and the user who performed the modification. +explanation = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Privilege Escalation", "Persistence", "Accessibility Features"], "mitre_technique_id": ["T1183"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Registry Keys for Creating SHIM Databases - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we look for modifications to registry keys used for shim databases on Microsoft platforms via the object_category and object_path field in the Change_Analysis data model and give you the destination, command used to initiate the change, the user who conducted this activity, the resource affected(object), and the whole path of the object. An application compatibility shim is a small library that transparently intercepts an API (via hooking), changes the parameters passed, handles the operation itself, or redirects the operation elsewhere, such as additional code stored on a system. This capability can be also leveraged by attackers to create and store malicious files in a shim database as observed in CARBANAK backdoor. +explanation = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Application Shimming"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Remote Desktop Network Bruteforce - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search monitors for abnormal amounts of remote-desktop (RDP) traffic from a source to a destination that may be indicative of a brute-force attack. It does this by filtering out RDP traffic from the Network_Traffic.All_Traffic data model, using twice the standard deviation of all source-to-destination connections. If any tuple is within more than two standard deviations of all other usual RDP traffic flows, it is indicative of a brute-force attack. +explanation = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. -annotations = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - Remote Desktop Network Traffic - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search finds systems that do not commonly communicate use remote desktop. It does this by filtering out all systems that have the "common_rdp_source" or "common_rdp_destination" category applied to that system. Categories are applied to systems using the Assets and Identity framework. +explanation = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search allows for whitelisting both source and destination hosts to remove them from the output of the search so you can focus on the uncommon uses of remote desktop on your network. how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. -annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Remote Desktop Protocol"], "mitre_technique_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - Remote Desktop Process Running On System - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search finds systems that do not commonly use remote desktop, but which begin using it. It filters out all systems that have the "common_rdp_source" category applied. Categories are applied to systems using the Assets and Identity framework. +explanation = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. -annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Remote Desktop Protocol"], "mitre_technique_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Remote Process Instantiation via WMI - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing native Windows utilities such as wmic.exe as a means to "live off the land", and avoid introducing new executables to the target system. In this search, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators. +explanation = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. -providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Remote Registry Key modifications - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for modifications made to the Windows registry from remote locations using reg.exe—a tool used to create/update/delete/modify Windows registry keys. It is accomplished through specifying the machine names in the registry path, by entering double backslashes, followed by a computer name. In this search, we look for registry changes where the registry path contains the name of a remote computer. The search returns the number of times the remote server has been accessed, the first and last times the activity occurred, the name of the modified registry path, the host on which the modification took place, and the name of the user that performed the modification. +explanation = This search monitors for remote modifications to registry keys. how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Persistence", "Lateral Movement"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Remote WMI Command Attempt - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Many a times, attackers leverage native Windows utilities that are designed to help administrators better manage their systems, infrastructure, and auditing, but are instead leveraged for malicious purposes. In this case, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators. +explanation = This search looks for wmic.exe being launched with parameters to operate on remote systems. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Administrators may use this legitimately to gather info from remote systems. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - RunDLL Loading DLL By Ordinal - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for rundll32.exe being run, loading a DLL out of a directory or subdirectory of AppData, and specifying the function at ordinal 2 be run. +explanation = This search looks for DLLs under %AppData% being loaded by rundll32.exe that are calling the exported function at ordinal 2. Calling exported functions by ordinal is not as common as calling by exported name. There was a bug fixed in IDAPro on 2016-08-08 that would not display functions without names. Calling functions by ordinal would overcome the lack of name and make it harder for analyst to reverse engineer. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["Execution", "Rundll32"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1085"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - SMB Traffic Spike - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search looks for a traffic spike in SMB traffic from a particular system. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. +explanation = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Execution", "Command and Control", "Commonly Used Port"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - SMB Traffic Spike - MLTK - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers often leverage Server Message Block (SMB) traffic, a protocol used for Windows file-sharing activity. A high-profile example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search leverages Splunk's Machine Learning Toolkit (MLTK) to identify spikes in SMB traffic that are unusual for a given hour of day/day of week combination. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +explanation = This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections. how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\ This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Lateral Movement", "Execution", "Command and Control", "Commonly Used Port"], "mitre_technique_id": ["T1110", "T1135", "T1210"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results -providing_technologies = ["Bro", "Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - SQL Injection with Long URLs - Rule] type = detection asset_type = Database Server confidence = medium -explanation = This search looks only at your web servers and returns the source, the web server, the URL and its length, and the user agent associated with HTTP GET requests for extremely long URLs or user agent lengths with more than three common SQL commands found within the URL. +explanation = This search looks for long URLs that have several SQL commands visible within them. how_to_implement = To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. -annotations = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability", "Execution", "Commonly Used Port"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} +annotations = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1043"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = [] [savedsearch://ESCU - Samsam Test File Write - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications across your hosts and monitors for a file named "test.txt" written to "windows\system32". This file is copied to potential targets during SamSam ransomware attacks to test the attacker's ability to access remote systems. If the file is successfully copied to the system, the system is added to a list of targets on which to deploy ransomware. +confidence = medium +explanation = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = No false positives have been identified. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] -[savedsearch://ESCU - Sc.exe Manipulating Windows Services - Rule] +[savedsearch://ESCU - Sc exe Manipulating Windows Services - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for the execution of sc.exe with parameters that indicate the utility is being used to create a new Windows service, or modify an existing one. Attackers often create a new service to host their malicious code, or they may take a non-critical service or one that is disabled, and modify it to point to their malware and enable the service if necessary. It is unusual for a service to be created or modified using the sc.exe utility. +explanation = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["Persistence", "Privilege Escalation", "New Service", "Modify Existing Service", "Defense Evasion", "Disabling Security Tools"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} known_false_positives = Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for execution of schtasks.exe with parameters that indicate that a specific task "reset," whose name is associated with the Dragonfly threat actor--has been created or deleted. Schtasks.exe is a native Windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence. +explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate a task name associated with the Dragonfly threat actor was created or deleted. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Scheduled Task"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = No known false positives -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Scheduled tasks used in BadRabbit ransomware - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for execution of schtasks.exe with parameters that indicate that specific task names related to the Bad Rabbit ransomware were created or deleted. The specific task name used are rhaegal, drogon and viserion_. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence. +explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = No known false positives -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Schtasks scheduling job on remote system - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled on a remote host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or malicious executables on remote systems. +explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Privilege Escalation", "Execution", "Scheduled Task"], "mitre_technique_id": ["T1053"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Schtasks used for forcing a reboot - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled that would cause a forced reboot on the host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or establish persistence. This tactic is leveraged by the Bad Rabbit Ransomware. +explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. how_to_implement = To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Execution", "Scheduled Task"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Script Execution via WMI - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for scripts launched via WMI, either remotely or locally, by looking for the execution of scrcons.exe, which is the scripting host used by WMI, similar to wscript or cscript. +explanation = This search looks for scripts launched via WMI. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. -providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Shim Database File Creation - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for files being created in `Windows\AppPatch\Custom and Windows\AppPatch\Custom64`, the location where shim databases are installed. It will return all the files created, as well as the time of creation for the first and last file for each endpoint. +confidence = medium +explanation = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Application Shimming"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["DE.CM"]} known_false_positives = Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for the execution of sdbinst.exe with command-line arguments of -q and -p. The -q option performs a silent installation with no visible window, status, or warning information. The -p option allows the shim database to contain patches. It will return the count, the first time, and the last time these command-line arguments were seen on each endpoint and by each user. +explanation = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Application Shimming"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["DE.CM"]} known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Short Lived Windows Accounts - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search looks for Windows Event Logs 4720 (account creation) and 4726 (account deletion) and determines if they happen for the same user within 4 hours of each other. It will report the user and machine that reported the events and the time it first and last saw this activity. +explanation = This search detects accounts that were created and deleted in a short time period. how_to_implement = This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ -annotations = {"cis20": ["CIS 16"], "mitre_attack": ["Persistence", "Create Account"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1136"], "nist": ["PR.IP"]} known_false_positives = It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Single Letter Process On Endpoint - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search returns all the processes for each endpoint and user and filters out any process that isn't 5 characters long and ends with .exe. +confidence = medium +explanation = This search looks for process names that consist only of a single letter. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Spectre and Meltdown Vulnerable Systems - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks for the three CVEs associated with the Spectre and Meltdown vulnerabilities. +confidence = medium +explanation = The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. how_to_implement = The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. annotations = {"cis20": ["CIS 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]} known_false_positives = It is possible that your vulnerability scanner is not detecting that the patches have been applied. -providing_technologies = ["Nessus", "Qualys"] +providing_technologies = [] [savedsearch://ESCU - Spike in File Writes - Rule] type = detection asset_type = Endpoint -confidence = low -explanation = This search calculates counts the number of file modification events per hour per host in your environment. It then takes the average and standard deviations of those numbers and displays any hosts with more than 20 events that have over four times the standard deviation more than the average number of file modifications. +confidence = medium +explanation = The search looks for a sharp increase in the number of files written to a particular host how_to_implement = In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response products, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Splunk Enterprise Information Disclosure - Rule] type = detection asset_type = Splunk Server confidence = medium -explanation = This search searches Splunk's internal logs for evidence of CVE-2018-11409 exploitation attempts. +explanation = This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. how_to_implement = The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives. -annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} known_false_positives = Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information. -providing_technologies = ["Splunk Enterprise"] +providing_technologies = [] [savedsearch://ESCU - Suspicious Changes to File Associations - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for changes made to the registry that control Windows file associations. It is typical for users to change the file association to open certain types of files with specific applications. However, when these changes are legitimately performed, they are typically done via the processes explorer.exe or openwith.exe. The search first executes the subsearch that looks at the Registry node, which specifies setting a value in the registry and creates a table of process_id and dest. It then uses those arguments to find out what process and parent process were responsible for making those registry changes. +explanation = This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. -annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Change Default File Association"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Suspicious Email - UBA Anomaly - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This detection monitors for emails that are suspicious because of their sender, domain rareness, or behavior differences, as determined by Splunk UBA. In this search, we query the "UEBA" data model to look for anomalies that are raised by the "SuspiciousEmailDetectionModel" and will output the count, description of the anomaly, signature, the type of event in UBA, the severity, and the user who received a potentially suspicious email from a newly seen domain. It will also output all the categories associated with that anomaly. +explanation = This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. -annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "mitre_attack": [], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and whitelist the URL if you determine that it is a legitimate sender. -providing_technologies = ["Microsoft Exchange"] +providing_technologies = [] [savedsearch://ESCU - Suspicious Email Attachment Extensions - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at any email messages with attachments and checks the file names of those attachments against an included lookup file to see if it has a suspicious file extension. +confidence = medium +explanation = This search looks for emails that have attachments with suspicious file extensions. how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ **Splunk Phantom Playbook Integration**\ If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. -annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Execution", "Defense Evasion"], "mitre_technique_id": ["T1193"], "nist": ["DE.AE", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["DE.AE", "PR.IP"]} known_false_positives = None identified -providing_technologies = ["Microsoft Exchange"] +providing_technologies = [] [savedsearch://ESCU - Suspicious File Write - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at files being created or modified in the Endpoint file-system data model. The names of those files are checked against an included lookup file, which contains the names of files associated with malware or attack activity. The search returns any files with matching names, along with a note (also specified in the lookup file) that gives or points to more information about the files. +confidence = medium +explanation = The search looks for files created with names that have been linked to malicious activity. how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": [], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Suspicious Java Classes - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search leverages HTTP form data from typically POST events that can be captured with Splunk streams or similar wire data capture tools. The search looks for java classes like `processbuilder` and `runtime` are used to create a new process and execute commands inside java, and are synonymous with spawning a shell. There are very exceptional reasons to ever these classes in Java via an HTTP API and hence when seen are highly suspicious. Also, this is a common vectors leverage to exploit Apache Struts. +explanation = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. how_to_implement = In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. -annotations = {"cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["Execution"], "nist": ["DE.AE"]} +annotations = {"cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "nist": ["DE.AE"]} known_false_positives = There are no known false positives. -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Apache"] +providing_technologies = [] [savedsearch://ESCU - Suspicious LNK file launching a process - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = In this search, we are essentially trying to detect if a LNK file created under the C:\User* or *\Local\Temp\* directory structures is launching a process with in 1 hour of its creation. LNK files or also known as Windows shortcut files are commonly associated with phishing and are a [preferred method used for exploitation](https://www.fireeye.com/blog/threat-research/2017/04/fin7-phishing-lnk.html). +confidence = medium +explanation = This search looks for a ``*.lnk` file under `C:\User*` or `*\Local\Temp\*` executing a process. This is common behavior used by various spear phishing tools. how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. -annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["Initial Access", "Spearphishing Attachment"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This detection should yield little or no false positive results. It is uncommon for LNK files to execute process from temporary or user directories. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] -[savedsearch://ESCU - Suspicious Reg.exe Process - Rule] +[savedsearch://ESCU - Suspicious Reg exe Process - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for the execution of reg.exe with a parent process of cmd.exe. It then executes a subsearch looking for those cmd.exe processes with a parent that is not explorer.exe. It then joins those two searches to make sure that the reg.exe process is a grandchild of the non explorer.exe process. The search will return the number of such instances and the first and last time this activity has been seen on each endpoint and user. +explanation = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Modify Registry", "Disabling Security Tools"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112", "T1089"], "nist": ["DE.CM"]} known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Suspicious wevtutil Usage - Rule] type = detection asset_type = confidence = medium -explanation = This search looks for execution of wevtutil.exe with command-line arguments that indicate that it has been used to delete the setup, application, security, or system event logs. The search returns the number of times the behavior was observed, the first and last time it was seen, the host exhibiting the behavior and the user context of the process execution. +explanation = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Indicator Removal on Host"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]} known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Suspicious writes to System Volume Information - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search uses data on file writes captured via Sysmon to watch for writes to the "System Volume Information" folder by processes other than the system process. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. It then looks for a file created with a path that includes "System Volume Information" and a process ID (PID) other than 4. PID 4 is assigned to the System process on Windows systems. Excluding these writes allows us to filter out legitimate activity. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity. +explanation = This search detects writes to the 'System Volume Information' folder by something other than the System process. how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"cis20": ["CIS 8"], "mitre_attack": ["Collection", "Data Staged"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]} known_false_positives = It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. -providing_technologies = ["Sysmon"] +providing_technologies = [] [savedsearch://ESCU - Suspicious writes to windows Recycle Bin - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search uses data on file writes captured via Sysmon to watch for writes to the Recycle Bin by processes other than explorer.exe. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. Next, it looks for files created with a path that includes the string "$Recycle.Bin" by processes other than explorer.exe, which is the process responsible for copying files to the Recycle Bin on delete. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity. +explanation = This search detects writes to the recycle bin by a process other than explorer.exe. how_to_implement = To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. -annotations = {"cis20": ["CIS 8"], "mitre_attack": ["Collection", "Data Staged"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]} known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. -providing_technologies = ["Sysmon"] +providing_technologies = [] [savedsearch://ESCU - System Processes Run From Unexpected Locations - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search returns all the processes that are not executing out of the C:\Windows\System32 or C:\Windows\SysWOW64 directories. Next, it takes the filename and looks it up in a table `is_windows_system_file` of files that should normally run out of the C:\Windows\System32 or C:\Windows\SysWOW64 directory. Any matches are then returned. +explanation = This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. how_to_implement = To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Masquerading"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - TOR Traffic - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = The search leverages the Enterprise Security Network_Traffic data model to look for network traffic that has been identified as TOR and marked as 'allowed'. +explanation = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. -annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration"], "nist": ["DE.AE"]} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE"]} known_false_positives = None at this time -providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - USN Journal Deletion - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search looks for the execution of fsutil.exe with command-line arguments to delete the USN journal. The search returns the count of the number of times it's seen this process execution with these arguments, the first and last time it's seen this behavior, the hosts it was executed on, and the user context under which it was executed. +explanation = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Indicator Removal on Host"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} +annotations = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Uncommon Processes On Endpoint - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search returns the number of times, as well as the first and last time, it has seen every process run for each endpoint and user, and then displays only those processes that you have marked as uncommon in the `uncommon_processes_default.csv` table. +confidence = medium +explanation = This search looks for applications on the endpoint that you have marked as uncommon. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. -annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Accessibility Features"], "nist": ["ID.AM", "PR.DS"]} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Unload Sysmon Filter Driver - Rule] type = detection asset_type = confidence = medium -explanation = This search is looking to detect execution of `fltMC.exe` that specifically used for unloading the Sysmon Filter Driver +explanation = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Disabling Security Tools"], "mitre_technique_id": ["T1089"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["DE.CM"]} known_false_positives = -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Unsigned Image Loaded by LSASS - Rule] type = detection asset_type = Windows confidence = medium -explanation = This search detects unsigned images loaded by LSASS (Local Security Authrity Subsystem Service). Normally, LSASS only loads signed images. Therefore, it is a malicious indicator when unsigned images are loaded by LSASS. This can be an indicator for credential dumping using tools like Windows Credential Editor. +explanation = This search detects loading of unsigned images by LSASS. how_to_implement = This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Credential Access", "Credential Dumping"], "mitre_technique_id": ["T1003"], "nist": ["DE.CM"]} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Unsuccessful Netbackup backups - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure. +confidence = medium +explanation = This search gives you the hosts where a backup was attempted and then failed. how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} known_false_positives = None identified -providing_technologies = ["Netbackup"] +providing_technologies = [] [savedsearch://ESCU - Unusually Long Command Line - Rule] type = detection asset_type = confidence = medium -explanation = This search calculates the average and standard deviation for the length of the command lines on each of your endpoints and alerts when it detects a command line with a length over 10 times the standard deviation larger than the average command line. +explanation = Command lines that are extremely long may be indicative of malicious activity on your hosts. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications start with long command lines. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Unusually Long Command Line - MLTK - Rule] type = detection asset_type = confidence = medium -explanation = This search leverages the Machine Learning Toolkit (MLTK) to identify outliers in the length of the command lines observed to be used by a specific user. The companion search, "Baseline of Command Line Length - MLTK," creates a machine-learning (ML) model built over the historical data used by this search. The determination of what is considered an outlier may be adjusted via the threshold parameter in the search. More information on the algorithm used can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. +explanation = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. how_to_implement = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. -annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution"], "nist": ["PR.PT", "DE.CM"]} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Unusually Long Content-Type Length - Rule] type = detection asset_type = Web Server -confidence = high -explanation = This detection search uses HTTP traffic data captured with Splunk Stream. The search is constructed to use "stream:http" sourcetype and counts of the number of times an HTTP request is received by a destination which the length of the Content-Type header value the client sends the server is greater than 100 characters long. We calculate this content_type_length field and output the results. +confidence = medium +explanation = This search looks for unusually long strings in the Content-Type http header that the client sends the server. how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. -annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} known_false_positives = Very few legitimate Content-Type fields will have a length greater than 100 characters. -providing_technologies = ["Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - WMI Permanent Event Subscription - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Windows event ID 5861. +explanation = This search looks for the creation of WMI permanent event subscriptions. how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - WMI Permanent Event Subscription - Sysmon - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Sysmon event ID 21. +explanation = This search looks for the creation of WMI permanent event subscriptions. how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - WMI Temporary Event Subscription - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI temporary event subscription by watching for Windows event ID 5860. +explanation = This search looks for the creation of WMI temporary event subscriptions. how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Web Fraud - Account Harvesting - Rule] type = detection asset_type = Account confidence = medium -explanation = When a fraudster is setting the stage for a campaign, they will often create many user accounts on the website. This is a simple example of how to detect a many-account creation hosted on a Magento2 e-commerce platform, where the fraudster is using email addresses from a single email domain. +explanation = This search is used to identify the creation of multiple user accounts using the same email domain name. how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. -annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Persistence", "Create Account"], "nist": ["DE.CM", "DE.DP"]} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. -providing_technologies = ["Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - Web Fraud - Anomalous User Clickspeed - Rule] type = detection asset_type = account confidence = medium -explanation = It's suspicious when someone or something is moving throughout your website too quickly or with a perfect click cadence. Fortunately, it's easy to detect by calculating the time between clicks for each session and highlighting the anomalous behavior. +explanation = This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. how_to_implement = Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. -annotations = {"cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Initial Access", "Valid Accounts"], "nist": ["DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.AE", "DE.CM"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior. -providing_technologies = ["Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - Web Fraud - Password Sharing Across Accounts - Rule] type = detection asset_type = account confidence = medium -explanation = A common password across user accounts generally indicates that the users are choosing poor passwords or that a fraudster has a common password across multiple accounts embedded within a script. The search will extract the username and password information from the form_data field, then calculate the number and values for usernames that have the same passwords. Finally, it outputs the values where the unique usernames sharing passwords are greater than 5 +explanation = This search is used to identify user accounts that share a common password. how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. -providing_technologies = ["Splunk Stream"] +providing_technologies = [] [savedsearch://ESCU - Web Servers Executing Suspicious Processes - Rule] type = detection asset_type = Web Server confidence = medium -explanation = This detection search uses the Enterprise Security Endpoint data model. The search uses tstats to search within an accelerated data model to find suspicious applications or processes such as whoami, ping, iptables, wget, service, or curl, running on hosts which are marked as web servers in the Assets and Identity Framework of ES. +explanation = This search looks for suspicious processes on all systems labeled as web servers. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. -annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability", "Execution", "Discovery", "System Information Discovery"], "nist": ["PR.IP"]} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]} known_false_positives = Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +providing_technologies = [] [savedsearch://ESCU - Windows Event Log Cleared - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks at the Windows security and system event logs. EventCode 1002 in the security log indicates that the log has been cleared, EventCode 1000 in the security log indicates the event logging service has been shut down, and EventCode 104 in the system log indicates the application log has been cleared. If any of these events are found, a notable will be generated. +confidence = medium +explanation = This search looks for Windows events that indicate one of the Windows event logs has been purged. how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts. -annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["Defense Evasion", "Indicator Removal on Host"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]} known_false_positives = It is possible that these logs may be legitimately cleared by Administrators. -providing_technologies = ["Microsoft Windows"] +providing_technologies = [] [savedsearch://ESCU - Windows hosts file modification - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = The hosts file is present on both Windows and Linux endpoints. The purpose of the hosts file is to provide a mapping between hostnames and IP addresses, the same way DNS is used to provide such a mapping. However, the information in the hosts file takes precedence over information received via DNS and a DNS query will not be issued if the hostname of interest is found in the hosts file. As such, attackers have been observed adding entries to the host file to override any DNS resolution. For this reason, it is useful to monitor for changes to this file, which typically do not occur very often in legitimate cases. +confidence = medium +explanation = The search looks for modifications to the hosts file on all Windows endpoints across your environment. how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["Command and Control", "Exfiltration"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} +annotations = {"cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} known_false_positives = There may be legitimate reasons for system administrators to add entries to this file. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +providing_technologies = [] ### END DETECTIONS ### -### INVESTIGATIONS ### +### RESPONSE TASKS ### [savedsearch://ESCU - AWS Investigate User Activities By ARN] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time -earliest_time_offset = 72000 -latest_time_offset = 36000 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - AWS Investigate User Activities By AccessKeyId] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - AWS Investigate User Activities By Source User] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -2947,41 +3009,49 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. -known_false_positives = None at this time -earliest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - AWS Network Interface details via resourceId] type = investigation explanation = none how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS configuration inputs -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - AWS S3 Bucket details via bucketName] type = investigation explanation = none how_to_implement = To implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later) and configure your AWS inputs. -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - All backup logs for host] type = investigation explanation = none how_to_implement = The successfully implement this search you must first send your backup logs to Splunk. -known_false_positives = None at this time -earliest_time_offset = 1209600 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 -[savedsearch://ESCU - Amazon EKS Kubernetes activity by src_ip] +[savedsearch://ESCU - Amazon EKS Kubernetes activity by src ip] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Cloud Watch EKS inputs. -known_false_positives = None at this time -earliest_time_offset = -70m@m -latest_time_offset = -10m@m +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 + +[savedsearch://ESCU - Analyze Malicious File] +type = investigation +explanation = none +how_to_implement = none +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - DNS Hijack Enrichment] type = investigation @@ -2989,39 +3059,39 @@ explanation = none how_to_implement = If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ -known_false_positives = None at this time -earliest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Domain Certificate Investigation] type = investigation explanation = none how_to_implement = To successfully implement this phantom playbook, you must integrate Enterprise Security with Phantom. Configure this playbook in the correlation search `Detect DNS requests to Phishing Sites leveraging EvilGinx2` ,as an adaptive response action. -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Excessive Account Lockouts Enrichment And Response] type = investigation explanation = none how_to_implement = Import playbook into phantom -known_false_positives = None at this time -earliest_time_offset = -4h@h -latest_time_offset = -5m@m +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 -[savedsearch://ESCU - GCP Kubernetes activity by src_ip] +[savedsearch://ESCU - GCP Kubernetes activity by src ip] type = investigation explanation = none how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. -known_false_positives = None at this time -earliest_time_offset = -70m@m -latest_time_offset = -10m@m +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get All AWS Activity From City] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3029,7 +3099,7 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3037,7 +3107,7 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3045,7 +3115,7 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3053,247 +3123,247 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. -known_false_positives = None at this time -earliest_time_offset = 43200 -latest_time_offset = 1 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Backup Logs For Endpoint] type = investigation explanation = none how_to_implement = You must be ingesting your backup logs. -known_false_positives = None at this time -earliest_time_offset = 604800 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Certificate logs for a domain] type = investigation explanation = none how_to_implement = You must be ingesting your certificates or SSL logs from your network traffic into your Certificates datamodel. Please note the wildcard(*) before domain in the search syntax, we use to match for all domain and subdomain combinations -known_false_positives = None at this time -earliest_time_offset = 36000 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get DNS Server History for a host] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be ingesting your DNS traffic -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get DNS traffic ratio] type = investigation explanation = none how_to_implement = You must be ingesting your network traffic -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get EC2 Instance Details by instanceId] type = investigation explanation = none how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get EC2 Launch Details] type = investigation explanation = none how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. -known_false_positives = None at this time -earliest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Email Info] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting your email logs or capturing unencrypted network traffic which contains email communications. -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Emails From Specific Sender] type = investigation explanation = none how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model. -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get First Occurrence and Last Occurrence of a MAC Address] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server. -known_false_positives = None at this time -earliest_time_offset = 864000 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get History Of Email Sources] type = investigation explanation = none how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model. -known_false_positives = None at this time -earliest_time_offset = 172800 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Logon Rights Modifications For Endpoint] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting your Windows event logs -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Logon Rights Modifications For User] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting your Windows event logs -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Notable History] type = investigation explanation = none how_to_implement = If you are using Enterprise Security you are likely already creating notable events with your correlation rules. No additional configuration is necessary. -known_false_positives = None at this time -earliest_time_offset = 864000 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Notable Info] type = investigation explanation = none how_to_implement = If you are using Enterprise Security you are likely already creating notable events with your correlation rules. No additional configuration is necessary. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Outbound Emails to Hidden Cobra Threat Actors] type = investigation explanation = none how_to_implement = To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model. -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Parent Process Info] type = investigation explanation = none how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Process File Activity] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model. -known_false_positives = None at this time -earliest_time_offset = 7200 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Process Info] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model. -known_false_positives = None at this time -earliest_time_offset = 7200 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Process Information For Port Activity] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events and populate the Endpoint Datamodel -known_false_positives = None at this time -earliest_time_offset = 7200 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Process Registry Activity] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model. -known_false_positives = None at this time -earliest_time_offset = 7200 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Process Responsible For The DNS Traffic] type = investigation explanation = none how_to_implement = You must be ingesting endpoint data that associates processes with network events into the Endpoint datamodel. This can come from endpoint protection products such as carbon black, or endpoint data sources such as Sysmon. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Registry Activities] type = investigation explanation = none how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Risk Modifiers For Endpoint] type = investigation explanation = none how_to_implement = Enable the correlation searches included in Splunk Enterprise Security that include Risk Analysis alert actions by leveraging the Risk Analysis Framework -known_false_positives = None at this time -earliest_time_offset = 604800 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Risk Modifiers For User] type = investigation explanation = none how_to_implement = Enable the correlation searches included in Splunk Enterprise Security that include Risk Analysis alert actions by leveraging the Risk Analysis Framework -known_false_positives = None at this time -earliest_time_offset = 604800 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get Sysmon WMI Activity for Host] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate events for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. -known_false_positives = None at this time -earliest_time_offset = 7200 -latest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Update Logs For Endpoint] type = investigation explanation = none how_to_implement = You need to be ingesting the update logs from your various systems. -known_false_positives = None at this time -earliest_time_offset = 604800 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Get User Information from Identity Table] type = investigation explanation = none how_to_implement = To successfully implement this search you must have populated the identity table with information about your users. -known_false_positives = None at this time -earliest_time_offset = 864000 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get Vulnerability Logs For Endpoint] type = investigation explanation = none how_to_implement = You need to be ingesting the logs from your vulnerability scanner. -known_false_positives = None at this time -earliest_time_offset = 604800 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 -[savedsearch://ESCU - Get Web Session Information via session_id] +[savedsearch://ESCU - Get Web Session Information via session id] type = investigation explanation = none how_to_implement = This search leverages data extracted from Stream:HTTP. You must configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment server. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate AWS ECR container listing activity] type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.You must also install Cloud Infrastructure Data Model. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3301,7 +3371,7 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3309,7 +3379,7 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +known_false_positives = not defined earliest_time_offset = 14400 latest_time_offset = 0 @@ -3317,430 +3387,468 @@ latest_time_offset = 0 type = investigation explanation = none how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = None at this time -earliest_time_offset = 7200 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Investigate Failed Logins for Multiple Destinations] type = investigation explanation = none how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. -known_false_positives = None at this time -earliest_time_offset = -7d -latest_time_offset = now +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 -[savedsearch://ESCU - Investigate Network Traffic From src_ip] +[savedsearch://ESCU - Investigate Network Traffic From src ip] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Okta Activity by IP Address] type = investigation explanation = none how_to_implement = You must be ingesting Okta logs -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Okta Activity by app] type = investigation explanation = none how_to_implement = You must be ingesting Okta logs -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Pass the Hash Attempts] type = investigation explanation = none how_to_implement = To successfully implement this search you need be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -known_false_positives = None at this time -earliest_time_offset = -24h -latest_time_offset = now +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Pass the Ticket Attempts] type = investigation explanation = none how_to_implement = To successfully implement this search you need to be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -known_false_positives = None at this time -earliest_time_offset = -24h -latest_time_offset = now +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Previous Unseen User] type = investigation explanation = none how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. -known_false_positives = None at this time -earliest_time_offset = -60d -latest_time_offset = now +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Successful Remote Desktop Authentications] type = investigation explanation = none how_to_implement = You must be populating the Authentication data model with security events from your Windows event logs. -known_false_positives = None at this time -earliest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 latest_time_offset = 0 [savedsearch://ESCU - Investigate Suspicious Strings in HTTP Header] type = investigation explanation = none how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate User Activities In All Cloud Regions] type = investigation explanation = none how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate User Activities In Okta] type = investigation explanation = none how_to_implement = You must be ingesting Okta logs -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate User Activities In Single Cloud Region] type = investigation explanation = none how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = None at this time -earliest_time_offset = 86400 -latest_time_offset = 14400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Web Activity From Host] type = investigation explanation = none how_to_implement = To successfully implement this search you must be ingesting your web traffic and populating the Web data model. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 -[savedsearch://ESCU - Investigate Web Activity From src_ip] +[savedsearch://ESCU - Investigate Web Activity From src ip] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be ingesting your web traffic and populating the web data model. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Investigate Web POSTs From src] type = investigation explanation = none how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model. -known_false_positives = None at this time -earliest_time_offset = 3600 -latest_time_offset = 3600 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 + +[savedsearch://ESCU - Malware Hunt and Contain] +type = investigation +explanation = none +how_to_implement = none +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 + +[savedsearch://ESCU - Process Chain Analysis] +type = investigation +explanation = none +how_to_implement = none +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 + +[savedsearch://ESCU - Quarantaine Infected Host] +type = investigation +explanation = none +how_to_implement = none +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Suspicious Email Attachment Investigate and Delete] type = investigation explanation = none how_to_implement = Synchronize the community playbook repository in Phantom, then open the playbook and follow the deployment notes to configure it for your environment. -known_false_positives = None at this time -earliest_time_offset = 0 -latest_time_offset = 86400 +known_false_positives = not defined +earliest_time_offset = 14400 +latest_time_offset = 0 -### END INVESTIGATIONS ### +### END RESPONSE TASKS ### ### BASELINES ### [savedsearch://ESCU - Add Prohibited Processes to Enterprise Security] type = support -explanation = This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries. +explanation = This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints. how_to_implement = This search should be run on each new install of ESCU. -known_false_positives = -providing_technologies = ["Splunk Enterprise Security"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of API Calls per User ARN] type = support -explanation = This search returns all log events that are API calls, pulls out the ARN that initiated each call, and collects them in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each ARN had. This table is then stored in a lookup file. +explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of Command Line Length - MLTK] type = support -explanation = Create a machine-learning (ML) model to characterize the length of the command lines used in your environment. This can help you identify unusually long ones that may indicate that attackers are executing commands on yout systems. +explanation = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line. how_to_implement = You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -known_false_positives = -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of DNS Query Length - MLTK] type = support -explanation = Create a machine-learning (ML) model to characterize the length of DNS requests seen in your environment to help identify unusually long ones that may be indicative of attacker infrastrucutre or the use of DNS as a command-and-control channel in your environment. +explanation = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query. how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -known_false_positives = -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK] type = support -explanation = Create a machine-learning (ML) model to establish a baseline for how many RunInstances users do in the environment. This can help you identify excessive numbers of RunInstances which may warrant further investigation to determine if there is misuse or abuse. +explanation = This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\ In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK] type = support -explanation = Create a machine-learning (ML) model to establish a baseline for how many TerminateInstances users do in the environment. This can help you identify excessive numbers of TerminateInstances which may warrant further investigation to determine if there is misuse or abuse. +explanation = This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\ In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\ More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of Network ACL Activity by ARN] type = support -explanation = Use this search to create a baseline for API calls related to network ACLs for the users who initiated this activity. It returns all logged API calls for network activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per-hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. +explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of S3 Bucket deletion activity by ARN] type = support -explanation = Use this search to create a baseline for API calls related to deleting an S3 bucket, grouped by the users who initiated this activity. It returns all logged API calls for S3 bucket-deletion activity and then pulls out the ARN that initiated each call. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. +explanation = This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of SMB Traffic - MLTK] type = support -explanation = Create a machine-learning (ML) model to characterize the number of SMB connections observed in your environment. This may help identify spikes in SMB traffic that may be indicative of attackers scanning or attempting to propagate to other systems in your environment. By default, this model is built over 30 days of data and profiles the number of SMB connections in your environment by the hour of day/day of week that the connections occur. +explanation = This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week. how_to_implement = You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, you may consider adding "src" to the by clause, which will build the model for each unique source in your enviornment. However, if you have a large number of hosts in your environment, this search may be very resource intensive. In this case, you may need to raise the value of max_inputs and/or max_groups in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`. -known_false_positives = -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of Security Group Activity by ARN] type = support -explanation = Use this search to create a baseline for API calls related to security groups by the users who initiated this activity. It returns all logged API calls for all security-group-related activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. +explanation = This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Baseline of blocked outbound traffic from AWS] type = support -explanation = Use this search to create a baseline of blocked outbound network connections by each source IP in your AWS environment. This search returns all log events that correspond to a blocked outbound network connection, extracts the source IP from where the outbound connection was initiated, and collects the events in one-hour groupings. Next, it calculates the number of outbound connections blocked per hour. For each source IP, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each source IP had. This table is then stored in a lookup file. +explanation = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Count of Unique IPs Connecting to Ports] type = support -explanation = For each port being accessed on the network, this search gives the total number of connections observed, and the number of unique IP addresses making those connections. +explanation = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them. how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. -known_false_positives = -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Count of assets by category] type = support -explanation = This search gives you the number and the names of the hosts of each host in your environment by category. It will then sort them by the count. +explanation = This search shows you every asset category you have and the assets that belong to those categories. how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated. -known_false_positives = -providing_technologies = ["Splunk Enterprise Security"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Create a list of approved AWS service accounts] type = support -explanation = We first look for all successful CloudTrail API activity caused by types of user accounts and then remove all the events caused by users in the Identity table. This generates a list of accounts--typically service accounts--configured in your AWS environment. We output this list of service accounts to `aws_service_accounts.csv`. +explanation = This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the service account entires in `aws_service_accounts.csv`, which is a lookup file created as a result of running this support search. Please remove the entries of service accounts that are not legitimate. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - DNSTwist Domain Names] type = support -explanation = This search starts with the dnstwist command consuming domains from a file called domains.csv in the DA-ESS-SOC/lookups directory. This search then adds a domain\_abuse=true term to each permutation, removes all the valid domain names and stores all that information into a lookup file that is used in the associated detection search. Alternatively domain dnstwist permutations can be calculated from domains in the `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` lookups located in **Splunk\_SA\_CIM** using argument `populate_from_cim=true`. Also an individual domain can be passed using argument `domain=` +explanation = This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches. how_to_implement = To successfully implement this search you need to update the file called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**. -known_false_positives = -providing_technologies = ["Splunk Enterprise"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Discover DNS records] type = support -explanation = Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv` +explanation = The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup how_to_implement = To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation -known_false_positives = Please vet the lookup created by this baseline search -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Identify Systems Creating Remote Desktop Traffic] type = support -explanation = This search counts the numbers of times the system has tried to connect to another system on TCP/3389, the default port used for RDP traffic. +explanation = This search counts the numbers of times the system has generated remote desktop traffic. how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. -known_false_positives = -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Identify Systems Receiving Remote Desktop Traffic] type = support -explanation = This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic. +explanation = This search counts the numbers of times the system has created remote desktop traffic how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it. -known_false_positives = -providing_technologies = ["Splunk Stream", "Bro"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Identify Systems Using Remote Desktop] type = support -explanation = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. It does this by looking for the process name in the Endpoint data model. +explanation = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. -known_false_positives = -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Monitor Successful Backups] type = support -explanation = This search gives you the count and the hostname of all the systems that had a successful backup each day. +explanation = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate. how_to_implement = To successfully implement this search you must be ingesting your backup logs. -known_false_positives = -providing_technologies = ["Netbackup"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Monitor Unsuccessful Backups] type = support -explanation = This search gives you the count and hostname of all the systems that had a backup failure each day +explanation = This search is intended to give you a feel for how often backup failures happen in your environments. Fluctuations in these numbers will allow you to determine when you should investigate. how_to_implement = To successfully implement this search you must be ingesting your backup logs. -known_false_positives = -providing_technologies = ["Netbackup"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen AWS Cross Account Activity] type = support -explanation = In this support search, we look for **AssumeRole** events where the requesting account is different from the requested account. The first and last times these events are seen are written to a lookup file. +explanation = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen AWS Provisioning Activity Sources] type = support -explanation = This search includes any event name that begins with "run" or "create," and then determines the first and last time these events were seen for each IP address that initiated the action. The search then consults a **GeoIP** database to determine the physical location of this IP address. This table outputs to a file. +explanation = This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen AWS Regions] type = support -explanation = In this support search, we create a table of the first time (earliest) and most recent time (latest) that this region has been seen in our dataset, grouped by the value `awsRegion`. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_aws_regions.csv` lookup file, which will act like a baseline for detections. Please validate the entries of region names in the lookup file. +explanation = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen Cloud Compute Creations By User] type = support -explanation = In this support search, we create a table of the earliest and latest time for each user that has created a cloud compute instance. +explanation = This search builds a table of previously seen users that have launched a cloud compute instance. how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = -providing_technologies = ["AWS", "Azure", "GCP"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen Cloud Compute Images] type = support -explanation = In this support search, we create a table of the earliest and latest time for each image id that has been seen. This table is then outputted to a csv file. +explanation = This search builds a table of previously seen images used to launch cloud compute instances how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = -providing_technologies = ["AWS", "Azure", "GCP"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen Cloud Compute Instance Types] type = support -explanation = In this support search, we create a table of the first time `firstTime` and most recent time `lastTime` that the compute type has been seen in our dataset. We only look for those events where an instance has been created. All of these entries will be added to the `previously_seen_cloud_compute_instance_types` lookup file, which will act as a baseline for detections. +explanation = This search builds a table of previously seen cloud compute instance types how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = -providing_technologies = ["AWS", "Azure", "GCP"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen Cloud Regions] type = support -explanation = In this support search, we create a table of the first time `firstTime` and most recent time `lastTime` that this region has been seen in our dataset, grouped by the region. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_cloud_regions` lookup file, which will act like a baseline for detections. +explanation = This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days how_to_implement = You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed. -known_false_positives = -providing_technologies = ["AWS", "Azure", "GCP"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen EC2 AMIs] type = support -explanation = In this support search, we create a table of the earliest and latest time that a specific AMI ID has been seen. This table is then outputted to a csv file. +explanation = This search builds a table of previously seen AMIs used to launch EC2 instances how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen EC2 Instance Types] type = support -explanation = In this support search, we create a table of the earliest and latest time that a specific EC2 instance type has been seen. The instanceType request field is not required and defaults to m1.small, so any time this field is null, the search defaults the field to m1.small. This table is then outputted to a csv file. +explanation = This search builds a table of previously seen EC2 instance types how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen EC2 Launches By User] type = support -explanation = In this support search, we create a table of the earliest and latest times that an ARN has launched a EC2 instance. This table is then outputted to a csv file. +explanation = This search builds a table of previously seen ARNs that have launched a EC2 instance. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen EC2 Modifications By User] type = support -explanation = In this support search, we create a table of the earliest and latest times that an ARN has modified a EC2 instance. The list of APIs that modify an EC2 are defined in the `ec2_modification_api_calls` macro for ease of use. This table is then outputted to a file. +explanation = This search builds a table of previously seen ARNs that have launched a EC2 instance. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously Seen Running Windows Services] type = support -explanation = In this support search, we look for Windows system-event code that indicates a status change of a Windows service. In this specific log event, the `param1` field represents the "service_name" and the `param2` represents the action/status of the service. This search will create a table of the first and last time as particular Windows service was seen to be in the `running` status. +explanation = This collects the services that have been started across your entire enterprise. how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. -known_false_positives = -providing_technologies = ["Microsoft Windows"] +known_false_positives = not defined +providing_technologies = none + +[savedsearch://ESCU - Previously Seen Zoom Child Processes - Initial] +type = support +explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is outputed to disk. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. +known_false_positives = not defined +providing_technologies = none + +[savedsearch://ESCU - Previously Seen Zoom Child Processes - Update] +type = support +explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously seen API call per user roles in CloudTrail] type = support -explanation = In this support search, we are looking for successful API calls made by user roles within your AWS infrastructure. The intent is to create an initial baseline cache of names of the API calls per security role for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of user role and the name of the API call. It is also worth noting that the role of a particular user is parsed as "userName" in the CloudTrail logs. +explanation = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, which is a lookup file created as a result of running this support search. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously seen S3 bucket access by remote IP] type = support -explanation = In this support search, we are looking for successful S3 bucket-access attempts made from remote IPs. The intent is to create an initial baseline cache of remote IP addresses per bucket name for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of remote IP and the name of the S3 bucket. +explanation = This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200" how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, which is a lookup file created as a result of running this support search. -known_false_positives = -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously seen command line arguments] type = support -explanation = In this support search, we look for command-line arguments using the parameter `/c` to execute processes and create an initial baseline cache for the previous 30 days. This will include the earliest and latest times a particular command-line argument is seen in our dataset, grouped by the command-line value. +explanation = This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. -known_false_positives = -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Previously seen users in CloudTrail] type = support -explanation = In this support search, we look for console login events by a particular user and create an initial baseline cache for the previous 30 days, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values. +explanation = This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created as a result of running this support search. -known_false_positives = n/a -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Systems Ready for Spectre-Meltdown Windows Patch] type = support -explanation = This search looks to see if a registry key was created at `HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat`. It will tell you when it was created and, if possible, what process created it. +explanation = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown. how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -known_false_positives = -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Update previously seen users in CloudTrail] type = support -explanation = In this support search, we look for console login events by a particular user to update the baseline cache of users/arns making the accesses, including the earliest and latest times, City, Region, and Country a particular user ARN is seen in our dataset, grouped by the ARN value. In cases where City and Region cannot be determined, the source IP address is substituted for these values. +explanation = This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins.csv`, which is a lookup file created as a result of running this support search. -known_false_positives = n/a -providing_technologies = ["AWS"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Windows Updates Install Failures] type = support -explanation = This search gives you the count of the number of systems that attempted and failed to install a Windows update each day. +explanation = This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned. how_to_implement = You must be ingesting your Windows Update Logs -known_false_positives = -providing_technologies = ["Microsoft Windows"] +known_false_positives = not defined +providing_technologies = none [savedsearch://ESCU - Windows Updates Install Successes] type = support -explanation = This search gives you the count and name of all the systems that had a successful update applied each day +explanation = This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned. how_to_implement = You must be ingesting your Windows Update Logs -known_false_positives = -providing_technologies = ["Microsoft Windows"] +known_false_positives = not defined +providing_technologies = none ### END ESCU BASELINES ### \ No newline at end of file diff --git a/package/default/app.conf b/package/default/app.conf index ec718c8dfd..852676cd72 100644 --- a/package/default/app.conf +++ b/package/default/app.conf @@ -4,7 +4,7 @@ is_configured = false state = enabled state_change_requires_restart = false -build = 5208 +build = 5971 [triggers] reload.analytic_stories = simple @@ -20,7 +20,7 @@ reload.content-version = simple [launcher] author = Splunk -version = 1.0.54 +version = 3.0.1 description = Explore the Analytic Stories included with ES Content Updates. [ui] diff --git a/package/default/collections.conf b/package/default/collections.conf index f36b4c99f9..cf2d61486e 100644 --- a/package/default/collections.conf +++ b/package/default/collections.conf @@ -1,13 +1,11 @@ -[kvstore_process_length] -field.process_name = string -field.dest = string -field.length = number -field.stddev = number -accelerated_fields.my_acceleration = {"process_name": 1, "dest": 1} +############# +# Automatically generated by generator.py in splunk/security-content +# On Date: 2020-06-04T22:46:46 UTC +# Author: Splunk Security Research +# Contact: research@splunk.com +############# -[kvstore_process_path] -field.process_name = string -field.dest = string -field.process_path = string -accelerated_fields.my_acceleration = {"process_name": 1, "dest": 1} +[zoom_first_time_child_process] +enforceTypes = false +replicate = false diff --git a/package/default/content-version.conf b/package/default/content-version.conf index 4a2967788c..3162010369 100644 --- a/package/default/content-version.conf +++ b/package/default/content-version.conf @@ -1,2 +1,2 @@ [content-version] -version = 1.0.54 +version = 3.0.1 diff --git a/package/default/data/ui/panels/workbench_panel_get_sysmon_wmi_activity_for_host.xml b/package/default/data/ui/panels/workbench_panel_get_sysmon_wmi_activity_for_host.xml index b4642ac7a7..73fd273db3 100644 --- a/package/default/data/ui/panels/workbench_panel_get_sysmon_wmi_activity_for_host.xml +++ b/package/default/data/ui/panels/workbench_panel_get_sysmon_wmi_activity_for_host.xml @@ -1,7 +1,7 @@ - sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18 EventCode<22 host=$dest$ | rename host as dest | table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter + sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18 EventCode<22 host=$dest$ | rename host as dest | table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter diff --git a/package/default/data/ui/panels/workbench_panel_investigate_aws_ecr_container_listing_activity.xml b/package/default/data/ui/panels/workbench_panel_investigate_aws_ecr_container_listing_activity.xml index 30e3630ed5..fabf2baa48 100644 --- a/package/default/data/ui/panels/workbench_panel_investigate_aws_ecr_container_listing_activity.xml +++ b/package/default/data/ui/panels/workbench_panel_investigate_aws_ecr_container_listing_activity.xml @@ -1,7 +1,7 @@
- |tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.event_name="ListImages" by Compute.image_id Compute.src_user Compute.src Compute.http_user_agent Compute.user_type | rename "Compute.*" as * |stats values(http_user_agent) as http_user_agent distinct_count(http_user_agent) as unique_ua_count by src_user, image_id, src, user_type | where unique_ua_count > 1 + |tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.event_name="ListImages" by Compute.image_id Compute.src_user Compute.src Compute.http_user_agent Compute.user_type | rename "Compute.*" as * |stats values(http_user_agent) as http_user_agent distinct_count(http_user_agent) as unique_ua_count by src_user, image_id, src, user_type | where unique_ua_count > 1 diff --git a/package/default/data/ui/panels/workbench_panel_investigate_failed_logins_for_multiple_destinations.xml b/package/default/data/ui/panels/workbench_panel_investigate_failed_logins_for_multiple_destinations.xml index eaacf46c7b..56dcaccf83 100644 --- a/package/default/data/ui/panels/workbench_panel_investigate_failed_logins_for_multiple_destinations.xml +++ b/package/default/data/ui/panels/workbench_panel_investigate_failed_logins_for_multiple_destinations.xml @@ -1,7 +1,7 @@
- | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app from datamodel=Authentication where Authentication.action=failure by Authentication.user | where distinct_count_dest > 1 | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` + | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app from datamodel=Authentication where Authentication.action=failure by Authentication.user | where distinct_count_dest > 1 | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` diff --git a/package/default/data/ui/panels/workbench_panel_investigate_pass_the_ticket_attempts.xml b/package/default/data/ui/panels/workbench_panel_investigate_pass_the_ticket_attempts.xml index 02d2dff20d..90efb8b630 100644 --- a/package/default/data/ui/panels/workbench_panel_investigate_pass_the_ticket_attempts.xml +++ b/package/default/data/ui/panels/workbench_panel_investigate_pass_the_ticket_attempts.xml @@ -1,7 +1,7 @@
- `wineventlog_security` EventCode=4768 OR EventCode=4769 | rex field=user "(?[^\@]+)" | stats count BY new_user, dest, EventCode | stats max(count) AS max_count sum(count) AS sum_count BY new_user, dest | where sum_count/max_count!=2 | rename new_user AS user + `wineventlog_security` EventCode=4768 OR EventCode=4769 | rex field=user "(?<new_user>[^\@]+)" | stats count BY new_user, dest, EventCode | stats max(count) AS max_count sum(count) AS sum_count BY new_user, dest | where sum_count/max_count!=2 | rename new_user AS user diff --git a/package/default/data/ui/panels/workbench_panel_investigate_previous_unseen_user.xml b/package/default/data/ui/panels/workbench_panel_investigate_previous_unseen_user.xml index ab5940c8fe..e5eaca5a72 100644 --- a/package/default/data/ui/panels/workbench_panel_investigate_previous_unseen_user.xml +++ b/package/default/data/ui/panels/workbench_panel_investigate_previous_unseen_user.xml @@ -1,7 +1,7 @@
- | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app values(Authentication.action) AS Authentication.action from datamodel=Authentication where Authentication.action=success by _time, Authentication.user | bucket _time span=30d | stats count min(first_login) as first_login max(last_login) as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user | where count=1 | where first_login >= relative_time(now(), "-30d") | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` + | tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app values(Authentication.action) AS Authentication.action from datamodel=Authentication where Authentication.action=success by _time, Authentication.user | bucket _time span=30d | stats count min(first_login) as first_login max(last_login) as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user | where count=1 | where first_login >= relative_time(now(), "-30d") | `security_content_ctime(first_login)` | `security_content_ctime(last_login)` | `drop_dm_object_name("Authentication")` diff --git a/package/default/data/ui/panels/workbench_panel_investigate_suspicious_strings_in_http_header.xml b/package/default/data/ui/panels/workbench_panel_investigate_suspicious_strings_in_http_header.xml index bb306f81a8..01f3ae513f 100644 --- a/package/default/data/ui/panels/workbench_panel_investigate_suspicious_strings_in_http_header.xml +++ b/package/default/data/ui/panels/workbench_panel_investigate_suspicious_strings_in_http_header.xml @@ -1,7 +1,7 @@
- | search sourcetype=stream:http src_ip="$src_ip$" dest_ip="$dest_ip$" | eval cs_content_type_length = len(cs_content_type) | search cs_content_type_length > 100 | rex field="cs_content_type" (?cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type, "application"), "True", "False") | rename suspicious_strings_found AS "Suspicious Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings, cs_content_type, cs_content_type_length, url + | search sourcetype=stream:http src_ip="$src_ip$" dest_ip="$dest_ip$" | eval cs_content_type_length = len(cs_content_type) | search cs_content_type_length > 100 | rex field="cs_content_type" (?<suspicious_strings>cmd.exe) | eval suspicious_strings_found=if(match(cs_content_type, "application"), "True", "False") | rename suspicious_strings_found AS "Suspicious Content-Type Found" | fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings, cs_content_type, cs_content_type_length, url diff --git a/package/default/es_investigations.conf b/package/default/es_investigations.conf index 8811fa4b84..9faded20c5 100644 --- a/package/default/es_investigations.conf +++ b/package/default/es_investigations.conf @@ -3,79 +3,79 @@ label = AWS Cross Account Activity description = Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. disabled = 0 -panels = ["panel://workbench_panel_aws_investigate_user_activities_by_source_user", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_aws_investigate_user_activities_by_accesskeyid"] +panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_aws_investigate_user_activities_by_accesskeyid", "panel://workbench_panel_aws_investigate_user_activities_by_source_user"] [panel_group://workbench_panel_group_aws_cryptomining] label = AWS Cryptomining description = Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. disabled = 0 -panels = ["panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_ec2_launch_details"] +panels = ["panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_ec2_launch_details", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_aws_investigate_user_activities_by_arn"] [panel_group://workbench_panel_group_aws_network_acl_activity] label = AWS Network ACL Activity description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. disabled = 0 -panels = ["panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_aws_investigate_user_activities_by_arn"] [panel_group://workbench_panel_group_aws_suspicious_provisioning_activities] label = AWS Suspicious Provisioning Activities description = Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. disabled = 0 -panels = ["panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_all_aws_activity_from_region", "panel://workbench_panel_get_all_aws_activity_from_country", "panel://workbench_panel_get_all_aws_activity_from_city"] +panels = ["panel://workbench_panel_get_all_aws_activity_from_country", "panel://workbench_panel_get_all_aws_activity_from_city", "panel://workbench_panel_get_all_aws_activity_from_region", "panel://workbench_panel_get_all_aws_activity_from_ip_address"] [panel_group://workbench_panel_group_aws_user_monitoring] label = AWS User Monitoring description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. disabled = 0 -panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_aws_user_activities_by_user_field"] +panels = ["panel://workbench_panel_investigate_aws_user_activities_by_user_field", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_notable_info"] [panel_group://workbench_panel_group_account_monitoring_and_controls] label = Account Monitoring and Controls description = A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. disabled = 0 -panels = ["panel://workbench_panel_get_logon_rights_modifications_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_logon_rights_modifications_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_logon_rights_modifications_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_logon_rights_modifications_for_endpoint"] [panel_group://workbench_panel_group_apache_struts_vulnerability] label = Apache Struts Vulnerability description = Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. disabled = 0 -panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_suspicious_strings_in_http_header", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_investigate_web_posts_from_src", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_suspicious_strings_in_http_header", "panel://workbench_panel_investigate_web_posts_from_src", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_asset_tracking] label = Asset Tracking description = Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. disabled = 0 -panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_first_occurrence_and_last_occurrence_of_a_mac_address"] +panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_first_occurrence_and_last_occurrence_of_a_mac_address"] [panel_group://workbench_panel_group_brand_monitoring] label = Brand Monitoring description = Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. disabled = 0 -panels = ["panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_email_info", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_email_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_cloud_cryptomining] label = Cloud Cryptomining description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. disabled = 0 -panels = ["panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_investigate_user_activities_in_single_cloud_region", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_user_activities_in_all_cloud_regions", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_investigate_cloud_compute_instance_activities", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_ec2_launch_details"] +panels = ["panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_ec2_launch_details", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_cloud_compute_instance_activities", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_investigate_user_activities_in_all_cloud_regions", "panel://workbench_panel_investigate_user_activities_in_single_cloud_region"] [panel_group://workbench_panel_group_coldroot_macos_rat] label = ColdRoot MacOS RAT description = Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. disabled = 0 -panels = ["panel://workbench_panel_investigate_network_traffic_from_src_ip", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_src_ip", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_investigate_network_traffic_from_src_ip", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_src_ip"] [panel_group://workbench_panel_group_collection_and_staging] label = Collection and Staging description = Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_command_and_control] label = Command and Control description = Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. disabled = 0 -panels = ["panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_aws_investigate_user_activities_by_arn"] [panel_group://workbench_panel_group_common_phishing_frameworks] label = Common Phishing Frameworks @@ -93,19 +93,19 @@ panels = ["panel://workbench_panel_investigate_aws_ecr_container_listing_activit label = Credential Dumping description = Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. disabled = 0 -panels = ["panel://workbench_panel_investigate_failed_logins_for_multiple_destinations", "panel://workbench_panel_investigate_previous_unseen_user", "panel://workbench_panel_investigate_pass_the_hash_attempts", "panel://workbench_panel_investigate_pass_the_ticket_attempts"] +panels = ["panel://workbench_panel_investigate_pass_the_hash_attempts", "panel://workbench_panel_investigate_previous_unseen_user", "panel://workbench_panel_investigate_pass_the_ticket_attempts", "panel://workbench_panel_investigate_failed_logins_for_multiple_destinations"] [panel_group://workbench_panel_group_dhs_report_ta18_074a] label = DHS Report TA18-074A description = Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_process_registry_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_process_file_activity", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_process_registry_activity", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_process_file_activity"] [panel_group://workbench_panel_group_dns_amplification_attacks] label = DNS Amplification Attacks description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. disabled = 0 -panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] [panel_group://workbench_panel_group_dns_hijacking] label = DNS Hijacking @@ -117,91 +117,103 @@ panels = ["panel://workbench_panel_get_dns_server_history_for_a_host"] label = Data Protection description = Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. disabled = 0 -panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_dns_server_history_for_a_host"] [panel_group://workbench_panel_group_disabling_security_tools] label = Disabling Security Tools description = Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_dynamic_dns] label = Dynamic DNS description = Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. disabled = 0 -panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_src_ip", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_investigate_web_activity_from_src_ip"] [panel_group://workbench_panel_group_emotet_malware__dhs_report_ta18_201a_] label = Emotet Malware DHS Report TA18-201A description = Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_vulnerability_logs_for_endpoint"] [panel_group://workbench_panel_group_hidden_cobra_malware] label = Hidden Cobra Malware description = Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. disabled = 0 -panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_investigate_successful_remote_desktop_authentications"] [panel_group://workbench_panel_group_host_redirection] label = Host Redirection description = Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. disabled = 0 -panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_dns_server_history_for_a_host"] [panel_group://workbench_panel_group_jboss_vulnerability] label = JBoss Vulnerability description = In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. disabled = 0 -panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_vulnerability_logs_for_endpoint"] [panel_group://workbench_panel_group_kubernetes_scanning_activity] label = Kubernetes Scanning Activity description = This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. disabled = 0 -panels = ["panel://workbench_panel_gcp_kubernetes_activity_by_src_ip", "panel://workbench_panel_amazon_eks_kubernetes_activity_by_src_ip", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info"] +panels = ["panel://workbench_panel_amazon_eks_kubernetes_activity_by_src_ip", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_gcp_kubernetes_activity_by_src_ip"] + +[panel_group://workbench_panel_group_kubernetes_sensitive_object_access_activity] +label = Kubernetes Sensitive Object Access Activity +description = This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_info"] + +[panel_group://workbench_panel_group_kubernetes_sensitive_role_activity] +label = Kubernetes Sensitive Role Activity +description = This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. +disabled = 0 +panels = ["panel://workbench_panel_get_notable_info"] [panel_group://workbench_panel_group_lateral_movement] label = Lateral Movement description = Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. disabled = 0 -panels = ["panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_successful_remote_desktop_authentications"] [panel_group://workbench_panel_group_malicious_powershell] label = Malicious PowerShell description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_monitor_backup_solution] label = Monitor Backup Solution description = Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. disabled = 0 -panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_all_backup_logs_for_host"] +panels = ["panel://workbench_panel_all_backup_logs_for_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] [panel_group://workbench_panel_group_monitor_for_unauthorized_software] label = Monitor for Unauthorized Software description = Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_vulnerability_logs_for_endpoint"] [panel_group://workbench_panel_group_monitor_for_updates] label = Monitor for Updates description = Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. disabled = 0 -panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] +panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] [panel_group://workbench_panel_group_netsh_abuse] label = Netsh Abuse description = Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_orangeworm_attack_group] label = Orangeworm Attack Group description = Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_phishing_payloads] label = Phishing Payloads @@ -213,61 +225,61 @@ panels = ["panel://workbench_panel_get_parent_process_info"] label = Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns description = Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities"] [panel_group://workbench_panel_group_prohibited_traffic_allowed_or_protocol_mismatch] label = Prohibited Traffic Allowed or Protocol Mismatch description = Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. disabled = 0 -panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_dns_server_history_for_a_host"] [panel_group://workbench_panel_group_ransomware] label = Ransomware description = Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. disabled = 0 -panels = ["panel://workbench_panel_get_sysmon_wmi_activity_for_host", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_backup_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_backup_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_sysmon_wmi_activity_for_host"] [panel_group://workbench_panel_group_router_and_infrastructure_security] label = Router and Infrastructure Security description = Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. disabled = 0 -panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_sql_injection] label = SQL Injection description = Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. disabled = 0 -panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] +panels = ["panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] [panel_group://workbench_panel_group_samsam_ransomware] label = SamSam Ransomware description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. disabled = 0 -panels = ["panel://workbench_panel_investigate_successful_remote_desktop_authentications", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_backup_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_update_logs_for_endpoint", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_backup_logs_for_endpoint", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_investigate_successful_remote_desktop_authentications"] [panel_group://workbench_panel_group_spectre_and_meltdown_vulnerabilities] label = Spectre And Meltdown Vulnerabilities description = Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. disabled = 0 -panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_splunk_enterprise_vulnerability] label = Splunk Enterprise Vulnerability description = Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. disabled = 0 -panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] +panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint"] [panel_group://workbench_panel_group_splunk_enterprise_vulnerability_cve_2018_11409] label = Splunk Enterprise Vulnerability CVE-2018-11409 description = Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. disabled = 0 -panels = ["panel://workbench_panel_investigate_network_traffic_from_src_ip", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_investigate_web_activity_from_src_ip", "panel://workbench_panel_get_notable_info"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_investigate_network_traffic_from_src_ip", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_web_activity_from_src_ip"] [panel_group://workbench_panel_group_suspicious_aws_ec2_activities] label = Suspicious AWS EC2 Activities description = Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. disabled = 0 -panels = ["panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_ec2_launch_details"] +panels = ["panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_ec2_launch_details", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_aws_investigate_user_activities_by_arn"] [panel_group://workbench_panel_group_suspicious_aws_login_activities] label = Suspicious AWS Login Activities @@ -279,37 +291,37 @@ panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn"] label = Suspicious AWS S3 Activities description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. disabled = 0 -panels = ["panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_aws_s3_bucket_details_via_bucketname"] +panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_investigate_aws_activities_via_region_name", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_aws_s3_bucket_details_via_bucketname"] [panel_group://workbench_panel_group_suspicious_aws_traffic] label = Suspicious AWS Traffic description = Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). disabled = 0 -panels = ["panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_aws_network_interface_details_via_resourceid", "panel://workbench_panel_aws_network_acl_details_from_id", "panel://workbench_panel_get_all_aws_activity_from_ip_address", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_aws_investigate_user_activities_by_arn"] [panel_group://workbench_panel_group_suspicious_command_line_executions] label = Suspicious Command-Line Executions description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities"] [panel_group://workbench_panel_group_suspicious_dns_traffic] label = Suspicious DNS Traffic description = Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. disabled = 0 -panels = ["panel://workbench_panel_get_dns_server_history_for_a_host", "panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_process_responsible_for_the_dns_traffic", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_dns_traffic_ratio", "panel://workbench_panel_get_dns_server_history_for_a_host"] [panel_group://workbench_panel_group_suspicious_emails] label = Suspicious Emails description = Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. disabled = 0 -panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_email_info", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_email_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_suspicious_mshta_activity] label = Suspicious MSHTA Activity description = Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities"] [panel_group://workbench_panel_group_suspicious_okta_activity] label = Suspicious Okta Activity @@ -321,73 +333,79 @@ panels = ["panel://workbench_panel_investigate_user_activities_in_okta", "panel: label = Suspicious WMI Use description = Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. disabled = 0 -panels = ["panel://workbench_panel_get_sysmon_wmi_activity_for_host", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_sysmon_wmi_activity_for_host"] [panel_group://workbench_panel_group_suspicious_windows_registry_activities] label = Suspicious Windows Registry Activities description = Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities"] + +[panel_group://workbench_panel_group_suspicious_zoom_child_processes] +label = Suspicious Zoom Child Processes +description = Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. +disabled = 0 +panels = ["panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_process_registry_activity", "panel://workbench_panel_get_process_file_activity"] [panel_group://workbench_panel_group_unusual_aws_ec2_modifications] label = Unusual AWS EC2 Modifications description = Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. disabled = 0 -panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_ec2_instance_details_by_instanceid"] +panels = ["panel://workbench_panel_aws_investigate_user_activities_by_arn", "panel://workbench_panel_get_ec2_instance_details_by_instanceid", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_unusual_processes] label = Unusual Processes description = Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_use_of_cleartext_protocols] label = Use of Cleartext Protocols description = Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. disabled = 0 -panels = ["panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_information_for_port_activity", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_web_fraud_detection] label = Web Fraud Detection description = Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. disabled = 0 -panels = ["panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_web_session_information_via_session_id"] +panels = ["panel://workbench_panel_get_emails_from_specific_sender", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_web_session_information_via_session_id", "panel://workbench_panel_get_notable_history"] [panel_group://workbench_panel_group_windows_defense_evasion_tactics] label = Windows Defense Evasion Tactics description = Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities"] [panel_group://workbench_panel_group_windows_file_extension_and_association_abuse] label = Windows File Extension and Association Abuse description = Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities"] [panel_group://workbench_panel_group_windows_log_manipulation] label = Windows Log Manipulation description = Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_vulnerability_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_vulnerability_logs_for_endpoint"] [panel_group://workbench_panel_group_windows_persistence_techniques] label = Windows Persistence Techniques description = Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities"] [panel_group://workbench_panel_group_windows_privilege_escalation] label = Windows Privilege Escalation description = Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_registry_activities"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_investigate_web_activity_from_host", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_registry_activities"] [panel_group://workbench_panel_group_windows_service_abuse] label = Windows Service Abuse description = Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. disabled = 0 -panels = ["panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_notable_history", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_user_information_from_identity_table"] +panels = ["panel://workbench_panel_get_risk_modifiers_for_user", "panel://workbench_panel_get_process_info", "panel://workbench_panel_get_risk_modifiers_for_endpoint", "panel://workbench_panel_get_authentication_logs_for_endpoint", "panel://workbench_panel_get_notable_info", "panel://workbench_panel_get_parent_process_info", "panel://workbench_panel_get_user_information_from_identity_table", "panel://workbench_panel_get_notable_history"] diff --git a/package/default/macros.conf b/package/default/macros.conf index 53e8b92870..e3b5df2f50 100644 --- a/package/default/macros.conf +++ b/package/default/macros.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-25T14:45:46 UTC +# On Date: 2020-06-04T22:46:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -106,6 +106,42 @@ description = customer specific splunk configurations(eg- index, source, sourcet definition = lookup update=true is_windows_system_file filename as process_name OUTPUT systemFile | search systemFile=true description = This macro limits the output to process names that are in the Windows System directory +[kubernetes_azure] +definition = sourcetype=mscs:storage:blob:json +description = customer specific splunk configurations(eg- index, source, sourcetype) for Kubernetes data from Azure. Replace the macro definition with configurations for your Splunk Environmnent. + +[kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_detect_rbac_authorization_by_account_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_detect_sensitive_object_access_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_detect_sensitive_role_access_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_detect_suspicious_kubectl_calls_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_pod_scan_fingerprint_detection_filter] +definition = search * +description = Use this macro to add additional filters + +[kubernetes_azure_scan_fingerprint_filter] +definition = search * +description = Use this macro to add additional filters + [netbackup] definition = sourcetype="netbackup_logs" description = customer specific splunk configurations(eg- index, source, sourcetype). Replace the macro definition with configurations for your Splunk Environmnent. @@ -134,6 +170,14 @@ description = Use this macro to determine how far into the past the window shoul definition = -70m@m description = Use this macro to determine how far into the past the window should be to determine if the region is new or not +[previously_seen_zoom_child_processes_forget_window] +definition = -90d@d +description = Use this macro to determine how long to keep track of zoom child processes + +[previously_seen_zoom_child_processes_window] +definition = -90d@d +description = Use this macro to determine how far back you should be checking for new zoom child processes + [prohibited_apps_launching_cmd] definition = | inputlookup prohibited_apps_launching_cmd | rename prohibited_applications as parent_process_name | eval parent_process_name="*" . parent_process_name | table parent_process_name description = This macro outputs a list of process that should not be the parent process of cmd.exe @@ -591,6 +635,10 @@ description = Update this macro to limit the output results to filter out false definition = search * description = Update this macro to limit the output results to filter out false positives. +[first_time_seen_child_process_of_zoom_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + [first_time_seen_running_windows_service_filter] definition = search * description = Update this macro to limit the output results to filter out false positives. @@ -619,6 +667,38 @@ description = Update this macro to limit the output results to filter out false definition = search * description = Update this macro to limit the output results to filter out false positives. +[kubernetes_azure_detect_rbac_authorization_by_account_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_sensitive_object_access_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_sensitive_role_access_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_detect_suspicious_kubectl_calls_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_pod_scan_fingerprint_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + +[kubernetes_azure_scan_fingerprint_filter] +definition = search * +description = Update this macro to limit the output results to filter out false positives. + [large_volume_of_dns_any_queries_filter] definition = search * description = Update this macro to limit the output results to filter out false positives. diff --git a/package/default/savedsearches.conf b/package/default/savedsearches.conf index f402d27b79..82d806ca2c 100644 --- a/package/default/savedsearches.conf +++ b/package/default/savedsearches.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-25T14:45:46 UTC +# On Date: 2020-06-04T22:46:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -209,7 +209,7 @@ relation = greater than quantity = 0 realtime_schedule = 0 is_visible = false -search = `cloudtrail` eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId != requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | multireport [| stats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) as firstTime max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | where fact=fiction] [| eventstats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) as firstTime, max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | where firstTime >= relative_time(now(), "-70m@m") AND isnotnull(_time) | spath output=accessKeyId path=responseElements.credentials.accessKeyId | spath output=requestingARN path=resources{}.ARN | stats values(awsRegion) as awsRegion values(firstTime) as firstTime values(lastTime) as lastTime values(sharedEventID) as sharedEventID, values(requestingARN) as src_user, values(responseElements.assumedRoleUser.arn) as dest_user by _time, requestingAccountId, requestedAccountId, accessKeyId] | table _time, firstTime, lastTime, src_user, requestingAccountId, dest_user, requestedAccountId, awsRegion, accessKeyId, sharedEventID | `aws_cross_account_activity_from_previously_unseen_account_filter` +search = `cloudtrail` eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId != requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | multireport [| stats min(eval(coalesce(firstTime, _time))) as firstTime max(eval(coalesce(lastTime, _time))) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | where fact=fiction] [| eventstats min(eval(coalesce(firstTime, _time))) as firstTime, max(eval(coalesce(lastTime, _time))) as lastTime by requestingAccountId, requestedAccountId | where firstTime >= relative_time(now(), "-70m@m") AND isnotnull(_time) | spath output=accessKeyId path=responseElements.credentials.accessKeyId | spath output=requestingARN path=resources{}.ARN | stats values(awsRegion) as awsRegion values(firstTime) as firstTime values(lastTime) as lastTime values(sharedEventID) as sharedEventID, values(requestingARN) as src_user, values(responseElements.assumedRoleUser.arn) as dest_user by _time, requestingAccountId, requestedAccountId, accessKeyId] | table _time, firstTime, lastTime, src_user, requestingAccountId, dest_user, requestedAccountId, awsRegion, accessKeyId, sharedEventID | `aws_cross_account_activity_from_previously_unseen_account_filter` [ESCU - AWS Network Access Control List Created with All Open Ports - Rule] action.escu = 0 @@ -2420,7 +2420,7 @@ action.escu.confidence = high action.escu.full_search_name = ESCU - Detect Prohibited Applications Spawning cmd exe - Rule action.escu.search_type = detection action.escu.providing_technologies = [] -action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity"] +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity", "Suspicious Zoom Child Processes"] cron_schedule = */30 * * * * dispatch.earliest_time = -30m dispatch.latest_time = now @@ -3892,6 +3892,46 @@ realtime_schedule = 0 is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml | `file_with_samsam_extension_filter` +[ESCU - First Time Seen Child Process of Zoom - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. +action.escu.mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.data_models = ["Endpoint"] +action.escu.eli5 = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. +action.escu.known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - First Time Seen Child Process of Zoom - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Suspicious Zoom Child Processes"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - First Time Seen Child Process of Zoom - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.nes_fields = ['dest'] +action.notable.param.rule_description = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. +action.notable.param.rule_title = First Time Seen Child Process of Zoom +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +alert.digest_mode = 1 +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +disabled = true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = | tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest | `drop_dm_object_name(Processes)` | lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen | where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") | `security_content_ctime(firstTime)` | table firstTime dest, process_id, process_name, parent_process_id, parent_process_name |`first_time_seen_child_process_of_zoom_filter` + [ESCU - First Time Seen Running Windows Service - Rule] action.escu = 0 action.escu.enabled = 1 @@ -4168,6 +4208,318 @@ realtime_schedule = 0 is_visible = false search = | from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `security_content_ctime(endDate)` | `security_content_ctime(startDate)`| table identity empStatus endDate startDate | `identify_new_user_accounts_filter` +[ESCU - Kubernetes Azure detect RBAC authorization by account - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. +action.escu.creation_date = 2020-05-26 +action.escu.modification_date = 2020-05-26 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect RBAC authorization by account - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect RBAC authorization by account - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences +action.notable.param.rule_title = Kubernetes Azure detect RBAC authorization by account +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +disabled = true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = sourcetype:mscs:storage:blob:json category=kube-audit | spath input=properties.log | search annotations.authorization.k8s.io/reason=* | table sourceIPs{} user.username userAgent annotations.authorization.k8s.io/reason |stats count by user.username annotations.authorization.k8s.io/reason | rare user.username annotations.authorization.k8s.io/reason |`kubernetes_azure_detect_rbac_authorization_by_account_filter` + +[ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. +action.escu.creation_date = 2020-05-26 +action.escu.modification_date = 2020-05-26 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb +action.notable.param.rule_title = Kubernetes Azure detect most active service accounts by pod namespace +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +disabled = true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = sourcetype:mscs:storage:blob:json category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace | top sourceIPs{} user.username verb responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_most_active_service_accounts_by_pod_namespace_filter` + +[ESCU - Kubernetes Azure detect sensitive object access - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect sensitive object access - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect sensitive object access - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.notable.param.rule_title = Kubernetes Azure detect sensitive object access +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +disabled = true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_object_access_filter` + +[ESCU - Kubernetes Azure detect sensitive role access - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect sensitive role access - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Role Activity"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect sensitive role access - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +action.notable.param.rule_title = Kubernetes Azure detect sensitive role access +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +disabled = true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=clusterroles OR clusterrolebindings | table sourceIPs{} user.username user.groups{} objectRef.namespace requestURI annotations.authorization.k8s.io/reason | dedup user.username user.groups{} |`kubernetes_azure_detect_sensitive_role_access_filter` + +[ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubernetes service accounts with failure or forbidden access status +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubernetes service accounts with failure or forbidden access status +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubernetes service accounts with failure or forbidden access status +action.notable.param.rule_title = Kubernetes Azure detect service accounts forbidden failure access +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +disabled = true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search user.groups{}=system:serviceaccounts* responseStatus.reason=Forbidden | table sourceIPs{} user.username userAgent verb responseStatus.reason responseStatus.status properties.pod objectRef.namespace |`kubernetes_azure_detect_service_accounts_forbidden_failure_access_filter` + +[ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information on Kubectl calls with IP, verb namespace and object access context +action.escu.mappings = {"kill_chain_phases": ["Lateral Movement"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information on Kubectl calls with IP, verb namespace and object access context +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets +action.escu.creation_date = 2020-05-26 +action.escu.modification_date = 2020-05-26 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Sensitive Object Access Activity"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information on Kubectl calls with IP, verb namespace and object access context +action.notable.param.rule_title = Kubernetes Azure detect suspicious kubectl calls +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +disabled = true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log| search objectRef.resource=secrets OR configmaps |table user.username user.groups{} objectRef.resource objectRef.namespace objectRef.name annotations.authorization.k8s.io/reason |dedup user.username user.groups{} |`kubernetes_azure_detect_suspicious_kubectl_calls_filter` + +[ESCU - Kubernetes Azure pod scan fingerprint - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure +action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure pod scan fingerprint - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Scanning Activity"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure pod scan fingerprint - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure +action.notable.param.rule_title = Kubernetes Azure pod scan fingerprint +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +disabled = true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod |`kubernetes_azure_pod_scan_fingerprint_filter` + +[ESCU - Kubernetes Azure scan fingerprint - Rule] +action.escu = 0 +action.escu.enabled = 1 +description = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure +action.escu.mappings = {"kill_chain_phases": ["Reconnaissance"]} +action.escu.data_models = [] +action.escu.eli5 = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure +action.escu.how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +action.escu.known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +action.escu.creation_date = 2020-05-19 +action.escu.modification_date = 2020-05-19 +action.escu.confidence = high +action.escu.full_search_name = ESCU - Kubernetes Azure scan fingerprint - Rule +action.escu.search_type = detection +action.escu.providing_technologies = [] +action.escu.analytic_story = ["Kubernetes Scanning Activity"] +cron_schedule = */30 * * * * +dispatch.earliest_time = -30m +dispatch.latest_time = now +action.correlationsearch.enabled = 1 +action.correlationsearch.label = ESCU - Kubernetes Azure scan fingerprint - Rule +schedule_window = auto +action.notable = 1 +action.notable.param.rule_description = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure +action.notable.param.rule_title = Kubernetes Azure scan fingerprint +action.notable.param.security_domain = threat +action.notable.param.severity = high +alert.digest_mode = 1 +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +disabled = true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +is_visible = false +search = `kubernetes_azure` category=kube-audit | spath input=properties.log | search responseStatus.code=401 | table sourceIPs{} userAgent verb requestURI responseStatus.reason |`kubernetes_azure_scan_fingerprint_filter` + [ESCU - Large Volume of DNS ANY Queries - Rule] action.escu = 0 action.escu.enabled = 1 @@ -7597,7 +7949,7 @@ search = | tstats `security_content_summariesonly` count min(_time) as firstTime ### ESCU BASELINES ### -[ESCU - Add Prohibited Processes to Enterprise Security - Baseline] +[ESCU - Add Prohibited Processes to Enterprise Security] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7617,7 +7969,7 @@ disabled = true is_visible = false search = | inputlookup interesting_processes_lookup | search note!=ESCU* | inputlookup append=T prohibitedProcesses_lookup | fillnull value=* dest dest_pci_domain | fillnull value=false is_required is_secure | fillnull value=true is_prohibited | outputlookup interesting_processes_lookup | stats count -[ESCU - Baseline of API Calls per User ARN - Baseline] +[ESCU - Baseline of API Calls per User ARN] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7637,7 +7989,7 @@ disabled = true is_visible = false search = `cloudtrail` eventType=AwsApiCall | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count -[ESCU - Baseline of Command Line Length - MLTK - Baseline] +[ESCU - Baseline of Command Line Length - MLTK] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7657,7 +8009,7 @@ disabled = true is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process | `drop_dm_object_name(Processes)` | search user!=unknown | `security_content_ctime(start_time)`| `security_content_ctime(end_time)`| eval processlen=len(process) | fit DensityFunction processlen by user into cmdline_pdfmodel -[ESCU - Baseline of DNS Query Length - MLTK - Baseline] +[ESCU - Baseline of DNS Query Length - MLTK] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7677,7 +8029,7 @@ disabled = true is_visible = false search = | tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query DNS.record_type | search DNS.record_type=* | `drop_dm_object_name("DNS")` | eval query_length = len(query) | fit DensityFunction query_length by record_type into dns_query_pdfmodel -[ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK - Baseline] +[ESCU - Baseline of Excessive AWS Instances Launched by User - MLTK] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7699,7 +8051,7 @@ disabled = true is_visible = false search = `cloudtrail` eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_launched by _time src_user | fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1 -[ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK - Baseline] +[ESCU - Baseline of Excessive AWS Instances Terminated by User - MLTK] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7721,7 +8073,7 @@ disabled = true is_visible = false search = `cloudtrail` eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter` | bucket span=10m _time | stats count as instances_terminated by _time src_user | fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1 -[ESCU - Baseline of Network ACL Activity by ARN - Baseline] +[ESCU - Baseline of Network ACL Activity by ARN] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7741,7 +8093,7 @@ disabled = true is_visible = false search = `cloudtrail` `network_acl_events` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats count -[ESCU - Baseline of S3 Bucket deletion activity by ARN - Baseline] +[ESCU - Baseline of S3 Bucket deletion activity by ARN] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7761,7 +8113,7 @@ disabled = true is_visible = false search = `cloudtrail` eventName=DeleteBucket | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | stats count -[ESCU - Baseline of SMB Traffic - MLTK - Baseline] +[ESCU - Baseline of SMB Traffic - MLTK] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7781,7 +8133,7 @@ disabled = true is_visible = false search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=10m, All_Traffic.src | eval HourOfDay=strftime(_time, "%H") | eval DayOfWeek=strftime(_time, "%A") | `drop_dm_object_name("All_Traffic")` | fit DensityFunction count by "HourOfDay,DayOfWeek" into smb_pdfmodel -[ESCU - Baseline of Security Group Activity by ARN - Baseline] +[ESCU - Baseline of Security Group Activity by ARN] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7801,7 +8153,7 @@ disabled = true is_visible = false search = `cloudtrail` `security_group_api_calls` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | stats count -[ESCU - Baseline of blocked outbound traffic from AWS - Baseline] +[ESCU - Baseline of blocked outbound traffic from AWS] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7821,7 +8173,7 @@ disabled = true is_visible = false search = `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | bucket _time span=1h | stats count as numberOfBlockedConnections by _time, src_ip | stats count(numberOfBlockedConnections) as numDataPoints, latest(numberOfBlockedConnections) as latestCount, avg(numberOfBlockedConnections) as avgBlockedConnections, stdev(numberOfBlockedConnections) as stdevBlockedConnections by src_ip | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | stats count -[ESCU - Count of Unique IPs Connecting to Ports - Baseline] +[ESCU - Count of Unique IPs Connecting to Ports] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7841,7 +8193,7 @@ disabled = true is_visible = false search = | tstats `security_content_summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | sort - count -[ESCU - Count of assets by category - Baseline] +[ESCU - Count of assets by category] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7861,7 +8213,7 @@ disabled = true is_visible = false search = | from datamodel Identity_Management.All_Assets | stats count values(nt_host) by category | sort -count -[ESCU - Create a list of approved AWS service accounts - Baseline] +[ESCU - Create a list of approved AWS service accounts] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7881,7 +8233,7 @@ disabled = true is_visible = false search = `cloudtrail` errorCode=success | rename userName as identity | search NOT [inputlookup identity_lookup_expanded | fields identity] | stats count by identity | table identity | outputlookup aws_service_accounts | stats count -[ESCU - DNSTwist Domain Names - Baseline] +[ESCU - DNSTwist Domain Names] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7901,7 +8253,7 @@ disabled = true is_visible = false search = | dnstwist domainlist=domains.csv | `remove_valid_domains` | eval domain_abuse="true" | table domain, domain_abuse | outputlookup brandMonitoring_lookup | stats count -[ESCU - Discover DNS records - Baseline] +[ESCU - Discover DNS records] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7921,7 +8273,7 @@ disabled = true is_visible = false search = | inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, "\*", "")) | join domain [|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,domain,type,query,answer | outputlookup createinapp=true discovered_dns_records.csv -[ESCU - Identify Systems Creating Remote Desktop Traffic - Baseline] +[ESCU - Identify Systems Creating Remote Desktop Traffic] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7941,7 +8293,7 @@ disabled = true is_visible = false search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")` | sort - count -[ESCU - Identify Systems Receiving Remote Desktop Traffic - Baseline] +[ESCU - Identify Systems Receiving Remote Desktop Traffic] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7961,7 +8313,7 @@ disabled = true is_visible = false search = | tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - count -[ESCU - Identify Systems Using Remote Desktop - Baseline] +[ESCU - Identify Systems Using Remote Desktop] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -7981,7 +8333,7 @@ disabled = true is_visible = false search = | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name="*mstsc.exe*" by Processes.dest Processes.process_name | `drop_dm_object_name(Processes)` | sort - count -[ESCU - Monitor Successful Backups - Baseline] +[ESCU - Monitor Successful Backups] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8001,7 +8353,7 @@ disabled = true is_visible = false search = `netbackup` "Disk/Partition backup completed successfully." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE -[ESCU - Monitor Unsuccessful Backups - Baseline] +[ESCU - Monitor Unsuccessful Backups] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8021,7 +8373,7 @@ disabled = true is_visible = false search = `netbackup` "An error occurred, failed to backup." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE -[ESCU - Previously Seen AWS Cross Account Activity - Baseline] +[ESCU - Previously Seen AWS Cross Account Activity] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8041,7 +8393,7 @@ disabled = true is_visible = false search = `cloudtrail` eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId!=requestedAccountId | stats earliest(_time) as firstTime latest(_time) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | stats count -[ESCU - Previously Seen AWS Provisioning Activity Sources - Baseline] +[ESCU - Previously Seen AWS Provisioning Activity Sources] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8061,7 +8413,7 @@ disabled = true is_visible = false search = `cloudtrail` (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats count -[ESCU - Previously Seen AWS Regions - Baseline] +[ESCU - Previously Seen AWS Regions] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8081,7 +8433,7 @@ disabled = true is_visible = false search = `cloudtrail` StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | stats count -[ESCU - Previously Seen Cloud Compute Creations By User - Baseline] +[ESCU - Previously Seen Cloud Compute Creations By User] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8101,7 +8453,7 @@ disabled = true is_visible = false search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_creations_by_user_input_filter` by Compute.src_user | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_creations_by_user | stats count -[ESCU - Previously Seen Cloud Compute Images - Baseline] +[ESCU - Previously Seen Cloud Compute Images] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8121,7 +8473,7 @@ disabled = true is_visible = false search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_image_input_filter` by Compute.image_id | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_images | stats count -[ESCU - Previously Seen Cloud Compute Instance Types - Baseline] +[ESCU - Previously Seen Cloud Compute Instance Types] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8141,7 +8493,7 @@ disabled = true is_visible = false search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=run `previously_seen_cloud_compute_instance_types_input_filter` by Compute.instance_type | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_compute_instance_types | stats count -[ESCU - Previously Seen Cloud Regions - Baseline] +[ESCU - Previously Seen Cloud Regions] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8161,7 +8513,7 @@ disabled = true is_visible = false search = | tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Cloud_Infrastructure.Compute where Compute.action=start `previously_seen_cloud_regions_input_filter` by Compute.region | `drop_dm_object_name("Compute")` | outputlookup previously_seen_cloud_regions | stats count -[ESCU - Previously Seen EC2 AMIs - Baseline] +[ESCU - Previously Seen EC2 AMIs] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8181,7 +8533,7 @@ disabled = true is_visible = false search = `cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instancesSet.items{}.imageId as amiID | stats earliest(_time) as firstTime latest(_time) as lastTime by amiID | outputlookup previously_seen_ec2_amis.csv | stats count -[ESCU - Previously Seen EC2 Instance Types - Baseline] +[ESCU - Previously Seen EC2 Instance Types] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8201,7 +8553,7 @@ disabled = true is_visible = false search = `cloudtrail` eventName=RunInstances errorCode=success | rename requestParameters.instanceType as instanceType | fillnull value="m1.small" instanceType | stats earliest(_time) as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | stats count -[ESCU - Previously Seen EC2 Launches By User - Baseline] +[ESCU - Previously Seen EC2 Launches By User] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8221,7 +8573,7 @@ disabled = true is_visible = false search = `cloudtrail` eventName=RunInstances errorCode=success | rename userIdentity.arn as arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | stats count -[ESCU - Previously Seen EC2 Modifications By User - Baseline] +[ESCU - Previously Seen EC2 Modifications By User] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8241,7 +8593,7 @@ disabled = true is_visible = false search = `cloudtrail` `ec2_modification_api_calls` errorCode=success | spath output=arn userIdentity.arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | stats count -[ESCU - Previously Seen Running Windows Services - Baseline] +[ESCU - Previously Seen Running Windows Services] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8261,7 +8613,47 @@ disabled = true is_visible = false search = `wineventlog_system` signature_id=7036 | rename param1 as service_name | rename param2 as action | search action="running" | stats earliest(_time) as firstTime, latest(_time) as lastTime by service_name | outputlookup previously_seen_running_windows_services | stats count -[ESCU - Previously seen API call per user roles in CloudTrail - Baseline] +[ESCU - Previously Seen Zoom Child Processes - Initial] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen Zoom Child Processes - Initial +description = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is outputed to disk. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.analytic_story = ["Suspicious Zoom Child Processes"] +action.escu.data_models = ["Endpoint"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is outputed to disk. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` | table dest, process_name, firstTimeSeen, lastTimeSeen | outputlookup zoom_first_time_child_process + +[ESCU - Previously Seen Zoom Child Processes - Update] +action.escu = 0 +action.escu.enabled = 1 +action.escu.search_type = support +action.escu.full_search_name = ESCU - Previously Seen Zoom Child Processes - Update +description = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk. +action.escu.creation_date = 2020-05-20 +action.escu.modification_date = 2020-05-20 +action.escu.analytic_story = ["Suspicious Zoom Child Processes"] +action.escu.data_models = ["Endpoint"] +dispatch.earliest_time = -30m +dispatch.latest_time = now +schedule_window = auto +action.escu.providing_technologies = [] +action.escu.eli5 = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. +disabled = true +is_visible = false +search = | tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest| `drop_dm_object_name(Processes)` | table firstTimeSeen, lastTimeSeen, process_name, dest | inputlookup zoom_first_time_child_process append=t | stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by process_name, dest | where lastTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_forget_window`") | outputlookup zoom_first_time_child_process + +[ESCU - Previously seen API call per user roles in CloudTrail] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8281,7 +8673,7 @@ disabled = true is_visible = false search = `cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles | stats count -[ESCU - Previously seen S3 bucket access by remote IP - Baseline] +[ESCU - Previously seen S3 bucket access by remote IP] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8301,7 +8693,7 @@ disabled = true is_visible = false search = `aws_s3_accesslogs` http_status=200 | stats earliest(_time) as earliest latest(_time) as latest by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip | stats count -[ESCU - Previously seen command line arguments - Baseline] +[ESCU - Previously seen command line arguments] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8321,7 +8713,7 @@ disabled = true is_visible = false search = | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe AND Processes.process="* /c *" by Processes.process | `drop_dm_object_name(Processes)` -[ESCU - Previously seen users in CloudTrail - Baseline] +[ESCU - Previously seen users in CloudTrail] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8341,7 +8733,7 @@ disabled = true is_visible = false search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) as firstTime latest(_time) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins.csv | stats count -[ESCU - Systems Ready for Spectre-Meltdown Windows Patch - Baseline] +[ESCU - Systems Ready for Spectre-Meltdown Windows Patch] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8361,7 +8753,7 @@ disabled = true is_visible = false search = | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name("All_Changes")` -[ESCU - Update previously seen users in CloudTrail - Baseline] +[ESCU - Update previously seen users in CloudTrail] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8381,7 +8773,7 @@ disabled = true is_visible = false search = `cloudtrail` eventName=ConsoleLogin | rename userIdentity.arn as user | iplocation src | eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) | stats earliest(_time) AS firstTime latest(_time) AS lastTime by user src City Region Country | inputlookup append=t previously_seen_users_console_logins.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country | outputlookup previously_seen_users_console_logins.csv -[ESCU - Windows Updates Install Failures - Baseline] +[ESCU - Windows Updates Install Failures] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8401,7 +8793,7 @@ disabled = true is_visible = false search = | tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure by _time span=1d -[ESCU - Windows Updates Install Successes - Baseline] +[ESCU - Windows Updates Install Successes] action.escu = 0 action.escu.enabled = 1 action.escu.search_type = support @@ -8706,7 +9098,7 @@ action.escu.full_search_name = ESCU - Get Authentication Logs For Endpoint description = This search returns all users that have attempted to access a particular endpoint. action.escu.creation_date = 2017-11-01 action.escu.modification_date = 2017-11-01 -action.escu.analytic_story = ["AWS Network ACL Activity", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Brand Monitoring", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] +action.escu.analytic_story = ["AWS Network ACL Activity", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Brand Monitoring", "ColdRoot MacOS RAT", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse", "Suspicious Zoom Child Processes"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] @@ -9000,7 +9392,7 @@ action.escu.full_search_name = ESCU - Get Notable Info description = This search queries the notable index to retrieve detailed information captured within the notable. Every notable has a unique ID associated with it, which is used to point us directly to the notable event under investigation. action.escu.creation_date = 2017-09-20 action.escu.modification_date = 2017-09-20 -action.escu.analytic_story = ["AWS Cryptomining", "AWS Network ACL Activity", "AWS User Monitoring", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Asset Tracking", "Brand Monitoring", "Cloud Cryptomining", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "JBoss Vulnerability", "Kubernetes Scanning Activity", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Monitor for Updates", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Splunk Enterprise Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Use of Cleartext Protocols", "Web Fraud Detection", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse"] +action.escu.analytic_story = ["AWS Cryptomining", "AWS Network ACL Activity", "AWS User Monitoring", "Account Monitoring and Controls", "Apache Struts Vulnerability", "Asset Tracking", "Brand Monitoring", "Cloud Cryptomining", "Collection and Staging", "Command and Control", "DHS Report TA18-074A", "DNS Amplification Attacks", "Data Protection", "Disabling Security Tools", "Dynamic DNS", "Emotet Malware DHS Report TA18-201A ", "Hidden Cobra Malware", "Host Redirection", "JBoss Vulnerability", "Kubernetes Scanning Activity", "Lateral Movement", "Malicious PowerShell", "Monitor for Unauthorized Software", "Monitor for Updates", "Netsh Abuse", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Router and Infrastructure Security", "SQL Injection", "SamSam Ransomware", "Spectre And Meltdown Vulnerabilities", "Splunk Enterprise Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Suspicious DNS Traffic", "Suspicious Emails", "Suspicious MSHTA Activity", "Suspicious WMI Use", "Suspicious Windows Registry Activities", "Unusual Processes", "Use of Cleartext Protocols", "Web Fraud Detection", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Windows Log Manipulation", "Windows Persistence Techniques", "Windows Privilege Escalation", "Windows Service Abuse", "Kubernetes Sensitive Role Activity", "Kubernetes Sensitive Object Access Activity"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] @@ -9063,7 +9455,7 @@ action.escu.full_search_name = ESCU - Get Process File Activity description = This search returns the file activity for a specific process on a specific endpoint action.escu.creation_date = 2019-11-06 action.escu.modification_date = 2019-11-06 -action.escu.analytic_story = ["DHS Report TA18-074A"] +action.escu.analytic_story = ["DHS Report TA18-074A", "Suspicious Zoom Child Processes"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] @@ -9126,7 +9518,7 @@ action.escu.full_search_name = ESCU - Get Process Registry Activity description = This search returns the registry activity for a specific process on a specific endpoint action.escu.creation_date = 2019-11-06 action.escu.modification_date = 2019-11-06 -action.escu.analytic_story = ["DHS Report TA18-074A"] +action.escu.analytic_story = ["DHS Report TA18-074A", "Suspicious Zoom Child Processes"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 action.escu.providing_technologies = [] diff --git a/package/default/transforms.conf b/package/default/transforms.conf index e3e880572b..f5c86a9fd0 100644 --- a/package/default/transforms.conf +++ b/package/default/transforms.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-25T14:45:46 UTC +# On Date: 2020-06-04T22:46:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -200,3 +200,9 @@ default_match = false match_type = WILDCARD(file) min_matches = 1 +[zoom_first_time_child_process] +collection = zoom_first_time_child_process +external_type = kvstore +# description = A list of suspicious file names +fields_list = _key, dest, process_name, firstTimeSeen, lastTimeSeen + diff --git a/package/default/use_case_library.conf b/package/default/use_case_library.conf index 06455a4bae..6f8df9ccd3 100644 --- a/package/default/use_case_library.conf +++ b/package/default/use_case_library.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-25T14:45:46 UTC +# On Date: 2020-06-04T22:46:46 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -12,9 +12,9 @@ category = Cloud Security last_updated = 2018-06-04 version = 1 references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By AccessKeyId"] +searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User"] description = Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. narrative = Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ @@ -25,9 +25,9 @@ category = Cloud Security last_updated = 2018-03-08 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] +searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN"] description = Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS). It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ @@ -39,9 +39,9 @@ category = Cloud Security last_updated = 2018-05-21 version = 2 references = ["https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html", "https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN"] description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls. @@ -50,9 +50,9 @@ category = Cloud Security last_updated = 2018-03-16 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From City"] +searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From IP Address"] description = Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. narrative = Because most enterprise AWS activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to whitelist specific IPs (because they vary).\ This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. @@ -62,9 +62,9 @@ category = Cloud Security last_updated = 2018-03-12 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Investigate AWS User Activities by user field"] +searches = ["ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Investigate AWS User Activities by user field", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ @@ -76,9 +76,9 @@ category = Best Practices last_updated = 2017-09-06 version = 1 references = [] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Logon Rights Modifications For Endpoint"] description = A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. narrative = Monitoring user accounts within your enterprise is a critical analytic function that helps ensure that credential and access policies/procedures are properly implemented and are being enforced. Proactive ad-hoc hunting, as well as routine monitoring, can ensure user or system accounts are not being abused by unauthorized individuals or processes. In the event of a network event or breach, user-authentication logs are a key resource in determining if or how an account might have been compromised or co-opted, leading to suspicious or malicious activity. @@ -87,9 +87,9 @@ category = Vulnerability last_updated = 2018-12-06 version = 1 references = ["https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web POSTs From src", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Investigate Web POSTs From src", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. narrative = In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ @@ -111,9 +111,9 @@ category = Best Practices last_updated = 2017-09-13 version = 1 references = ["https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address"] +searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address"] description = Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. narrative = This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business context and awareness of their assets during an incident. Information derived from this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. @@ -122,9 +122,9 @@ category = Abuse last_updated = 2017-12-19 version = 1 references = ["https://www.zerofox.com/blog/what-is-digital-risk-monitoring/", "https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/", "https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Emails From Specific Sender", "ESCU - Get Email Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. narrative = While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and will happily leverage it for nefarious purposes whenever possible3phishing with lookalike addresses, embedding faux command-and-control domains in malware, and hosting malicious content on domains that closely mimic your corporate servers. This is where brand monitoring comes in.\ You can use our adaptation of `DNSTwist`, together with the support searches in this Analytic Story, to generate permutations of specified brands and external domains. Splunk can monitor email, DNS requests, and web traffic for these permutations and provide you with early warnings and situational awareness--powerful elements of an effective defense.\ @@ -135,9 +135,9 @@ category = Cloud Security last_updated = 2019-10-02 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Get Notable History", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] +searches = ["ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Investigate User Activities In Single Cloud Region"] description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ @@ -149,9 +149,9 @@ category = Malware last_updated = 2019-01-09 version = 1 references = ["https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/", "https://objective-see.com/blog/blog_0x2A.html", "https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/"] -maintainers = "Jose Hernandez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Jose Hernandez"}] spec_version = 3 -searches = ["ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From src ip"] description = Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. narrative = Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ @@ -162,9 +162,9 @@ category = Adversary Tactics last_updated = 2020-02-03 version = 1 references = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. narrative = A common adversary goal is to identify and exfiltrate data of value from a target organization. This data may include email conversations and addresses, confidential company information, links to network design/infrastructure, important dates, and so on.\ Attacks are composed of three activities: identification, collection, and staging data for exfiltration. Identification typically involves scanning systems and observing user activity. Collection can involve the transfer of large amounts of data from various repositories. Staging/preparation includes moving data to a central location and compressing (and optionally encoding and/or encrypting) it. All of these activities provide opportunities for defenders to identify their presence. \ @@ -175,9 +175,9 @@ category = Adversary Tactics last_updated = 2018-06-01 version = 1 references = ["https://attack.mitre.org/wiki/Command_and_Control", "https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Protocol or Port Mismatch - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - TOR Traffic - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN"] description = Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. narrative = Threat actors typically architect and implement an infrastructure to use in various ways during the course of their attack campaigns. In some cases, they leverage this infrastructure for scanning and performing reconnaissance activities. In others, they may use this infrastructure to launch actual attacks. One of the most important functions of this infrastructure is to establish servers that will communicate with implants on compromised endpoints. These servers establish a command and control channel that is used to proxy data between the compromised endpoint and the attacker. These channels relay commands from the attacker to the compromised endpoint and the output of those commands back to the attacker.\ Because this communication is so critical for an adversary, they often use techniques designed to hide the true nature of the communications. There are many different techniques used to establish and communicate over these channels. This Analytic Story provides searches that look for a variety of the techniques used for these channels, as well as indications that these channels are active, by examining logs associated with border control devices and network-access control lists. @@ -187,7 +187,7 @@ category = Adversary Tactics last_updated = 2019-04-29 version = 1 references = ["https://github.com/kgretzky/evilginx2", "https://attack.mitre.org/techniques/T1192/", "https://breakdev.org/evilginx-advanced-phishing-with-two-factor-authentication-bypass/"] -maintainers = "Splunk Research Team, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Splunk Research Team"}] spec_version = 3 searches = ["ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule", "ESCU - Get Certificate logs for a domain"] description = Detect DNS and web requests to fake websites generated by the EvilGinx2 toolkit. These websites are designed to fool unwitting users who have clicked on a malicious link in a phishing email. @@ -199,7 +199,7 @@ category = Cloud Security last_updated = 2020-02-20 version = 1 references = ["https://github.com/splunk/cloud-datamodel-security-research"] -maintainers = "Rod Soto, Rico Valdez, Splunk" +maintainers = [{"company": "Rico Valdez, Splunk", "email": "-", "name": "Rod Soto"}] spec_version = 3 searches = ["ESCU - GCP GCR container uploaded - Rule", "ESCU - New container uploaded to AWS ECR - Rule", "ESCU - Investigate AWS ECR container listing activity"] description = Use the searches in this story to monitor your Kubernetes registry repositories for upload, and deployment of potentially vulnerable, backdoor, or implanted containers. These searches provide information on source users, destination path, container names and repository names. The searches provide context to address Mitre T1525 which refers to container implantation upload to a company's repository either in Amazon Elastic Container Registry, Google Container Registry and Azure Container Registry. @@ -210,9 +210,9 @@ category = Adversary Tactics last_updated = 2020-02-04 version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1003", "https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Pass the Ticket Attempts"] +searches = ["ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Failed Logins for Multiple Destinations"] description = Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. narrative = Credential dumping—gathering credentials from a target system, often hashed or encrypted—is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems. The threat actors target a variety of sources to extract them, including the Security Accounts Manager (SAM), Local Security Authority (LSA), NTDS from Domain Controllers, or the Group Policy Preference (GPP) files.\ Once attackers obtain valid credentials, they use them to move throughout a target network with ease, discovering new systems and identifying assets of interest. Credentials obtained in this manner typically include those of privileged users, which may provide access to more sensitive information and system operations.\ @@ -223,9 +223,9 @@ category = Malware last_updated = 2020-01-22 version = 2 references = ["https://www.us-cert.gov/ncas/alerts/TA18-074A"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Create local admin accounts using net exe - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Registry Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Process File Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Create local admin accounts using net exe - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Process Registry Activity", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process File Activity"] description = Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. narrative = The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ @@ -237,9 +237,9 @@ category = Abuse last_updated = 2016-09-13 version = 1 references = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.imperva.com/learn/application-security/dns-amplification/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. narrative = The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. @@ -249,9 +249,9 @@ category = Adversary Tactics last_updated = 2020-02-04 version = 1 references = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS record changed - Rule", "ESCU - Get DNS Server History for a host"] +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS record changed - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Get DNS Server History for a host"] description = Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. narrative = Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.\ The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous. Not only can hackers bring down an entire business, they can intercept confidential information, emails, and login credentials, as well. \ @@ -268,9 +268,9 @@ category = Abuse last_updated = 2017-09-14 version = 1 references = ["https://www.cisecurity.org/controls/data-protection/", "https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect USB device insertion - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect USB device insertion - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host"] description = Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. narrative = Attackers can leverage a variety of resources to compromise or exfiltrate enterprise data. Common exfiltration techniques include remote-access channels via low-risk, high-payoff active-collections operations and close-access operations using insiders and removable media. While this Analytic Story is not a comprehensive listing of all the methods by which attackers can exfiltrate data, it provides a useful starting point. @@ -279,9 +279,9 @@ category = Adversary Tactics last_updated = 2020-02-04 version = 2 references = ["https://attack.mitre.org/wiki/Technique/T1089", "https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Processes launching netsh - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Suspicious Reg exe Process - Rule", "ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. narrative = Attackers employ a variety of tactics in order to avoid detection and operate without barriers. This often involves modifying the configuration of security tools to get around them or explicitly disabling them to prevent them from running. This Analytic Story includes searches that look for activity consistent with attackers attempting to disable various security mechanisms. Such activity may involve monitoring for suspicious registry activity, as this is where much of the configuration for Windows and various other programs reside, or explicitly attempting to shut down security-related services. Other times, attackers attempt various tricks to prevent specific programs from running, such as adding the certificates with which the security tools are signed to a blacklist (which would prevent them from running). @@ -290,9 +290,9 @@ category = Malware last_updated = 2018-09-06 version = 2 references = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - Investigate Web Activity From src ip"] description = Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. narrative = Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and blacklists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. @@ -301,9 +301,9 @@ category = Malware last_updated = 2020-01-27 version = 1 references = ["https://www.us-cert.gov/ncas/alerts/TA18-201A", "https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf", "https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - Detection of tools built by NirSoft - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get Vulnerability Logs For Endpoint"] description = Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. narrative = The trojan downloader known as Emotet first surfaced in 2014, when it was discovered targeting the banking industry to steal credentials. However, according to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), Emotet has evolved far beyond those beginnings to become what a ThreatPost article called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For example, in early 2018, Emotet was found to be using its loader function to spread the Quakbot and Ransomware variants. \ According to the TA, the the malware continues to be among the most costly and destructive malware affecting the private and public sectors. Researchers have linked it to the threat group Mealybug, which has also been on the security communitys radar since 2014.\ @@ -314,9 +314,9 @@ category = Malware last_updated = 2020-01-22 version = 2 references = ["https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Create or delete windows shares using net exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Create or delete windows shares using net exe - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - Investigate Successful Remote Desktop Authentications"] description = Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. narrative = North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ @@ -328,9 +328,9 @@ category = Abuse last_updated = 2017-09-14 version = 1 references = ["https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host"] description = Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. narrative = Attackers will often attempt to manipulate client communications for nefarious purposes. In some cases, an attacker may endeavor to modify a local host file to redirect communications with resources (such as antivirus or system-update services) to prevent clients from receiving patches or updates. In other cases, an attacker might use this tactic to have the client connect to a site that looks like the intended site, but instead installs malware or collects information from the victim. Additionally, an attacker may redirect a victim in order to execute a MITM attack and observe communications. @@ -339,9 +339,9 @@ category = Vulnerability last_updated = 2017-09-14 version = 1 references = ["http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info"] +searches = ["ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint"] description = In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. narrative = This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with the Samsam ransomware variant. Incidents involving this ransomware are unique, in that they begin with attacks against vulnerable services, rather than the phishing or drive-by attacks more common with ransomware. In this case, vulnerable JBoss applications appear to be the target of choice.\ It is helpful to understand how often a notable event generated by this story occurs, as well as the commonalities between some of these events, both of which may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. It may also help to understand whether the issue is restricted to a single user/system or whether it is broader in scope.\ @@ -364,20 +364,42 @@ category = Cloud Security last_updated = 2020-04-15 version = 1 references = ["https://github.com/splunk/cloud-datamodel-security-research"] -maintainers = "Rod Soto, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] spec_version = 3 -searches = ["ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - GCP Kubernetes activity by src ip", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] +searches = ["ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Kubernetes Azure pod scan fingerprint - Rule", "ESCU - Kubernetes Azure scan fingerprint - Rule", "ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - GCP Kubernetes activity by src ip"] description = This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. +[analytic_story://Kubernetes Sensitive Object Access Activity] +category = Cloud Security +last_updated = 2020-05-20 +version = 1 +references = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +searches = ["ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule", "ESCU - Kubernetes Azure detect sensitive object access - Rule", "ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule", "ESCU - Get Notable Info"] +description = This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects. + +[analytic_story://Kubernetes Sensitive Role Activity] +category = Cloud Security +last_updated = 2020-05-20 +version = 1 +references = ["https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html"] +maintainers = [{"company": "Splunk", "email": "-", "name": "Rod Soto"}] +spec_version = 3 +searches = ["ESCU - Kubernetes Azure detect sensitive role access - Rule", "ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule", "ESCU - Kubernetes Azure detect RBAC authorization by account - Rule", "ESCU - Get Notable Info"] +description = This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces. +narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities + [analytic_story://Lateral Movement] category = Adversary Tactics last_updated = 2020-02-04 version = 2 references = ["https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate Successful Remote Desktop Authentications"] description = Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. narrative = Once attackers gain a foothold within an enterprise, they will seek to expand their accesses and leverage techniques that facilitate lateral movement. Attackers will often spend quite a bit of time and effort moving laterally. Because lateral movement renders an attacker the most vulnerable to detection, it's an excellent focus for detection and investigation.\ Indications of lateral movement can include the abuse of system utilities (such as `psexec.exe`), unauthorized use of remote desktop services, `file/admin$` shares, WMI, PowerShell, pass-the-hash, or the abuse of scheduled tasks. Organizations must be extra vigilant in detecting lateral movement techniques and look for suspicious activity in and around high-value strategic network assets, such as Active Directory, which are often considered the primary target or "crown jewels" to a persistent threat actor.\ @@ -390,9 +412,9 @@ category = Adversary Tactics last_updated = 2017-08-23 version = 4 references = ["https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/", "https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. narrative = The searches in this Analytic Story monitor for parameters often used for malicious purposes. It is helpful to understand how often the notable events generated by this story occur, as well as the commonalities between some of these events. These factors may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. Likewise, it is important to determine whether the issue is restricted to a single user/system or is broader in scope.\ The following factors may assist you in determining whether the event is malicious: \ @@ -410,9 +432,9 @@ category = Best Practices last_updated = 2017-09-12 version = 1 references = ["https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - All backup logs for host"] +searches = ["ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - All backup logs for host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint"] description = Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. narrative = Having backups is a standard best practice that helps ensure continuity of business operations. Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. The detection searches in this Analytic Story will help you identify systems that have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint. @@ -421,9 +443,9 @@ category = Best Practices last_updated = 2017-09-15 version = 1 references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint"] description = Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. narrative = It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within Enterprise Security with prohibited processes. An included support search will augment this data, adding information on processes thought to be malicious. This search requires data from endpoint detection-and-response solutions, endpoint data sources (such as Sysmon), or Windows Event Logs--assuming that the Active Directory administrator has enabled process tracking within the System Event Audit Logs.\ It is important to investigate any software identified as suspicious, in order to understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. @@ -433,9 +455,9 @@ category = Best Practices last_updated = 2017-09-15 version = 1 references = ["https://learn.cisecurity.org/20-controls-download"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] description = Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. narrative = It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important to eliminate known vulnerabilities that may be exploited by various threat actors.\ Searches in this analytic story are designed to help analysts monitor endpoints for system patches and/or updates. This helps analysts identify any systems that are not successfully updated in a timely matter.\ @@ -446,9 +468,9 @@ category = Abuse last_updated = 2017-01-05 version = 1 references = ["https://technet.microsoft.com/library/bb490939.aspx", "https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html", "http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. narrative = It is a common practice for attackers of all types to leverage native Windows tools and functionality to execute commands for malicious reasons. One such tool on Windows OS is `netsh.exe`,a command-line scripting utility that allows you to--either locally or remotely--display or modify the network configuration of a computer that is currently running. `Netsh.exe` can be used to discover and disable local firewall settings. It can also be used to set up a remote connection to a host from an infected system.\ To get started, run the detection search to identify parent processes of `netsh.exe`. @@ -458,9 +480,9 @@ category = Malware last_updated = 2020-01-22 version = 2 references = ["https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia", "https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. narrative = In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with remote access to the target system, decrypting and extracting a copy of its main DLL payload from its resource section. Before writing the payload to disk, it inserts a randomly generated string into the middle of the decrypted payload in an attempt to evade hash-based detections.\ Awareness of the Orangeworm group first surfaced in January, 2015. It has conducted targeted attacks against related industries, as well, such as pharmaceuticals and healthcare IT solution providers.\ @@ -473,7 +495,7 @@ category = Adversary Tactics last_updated = 2019-04-29 version = 1 references = ["https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html"] -maintainers = "Splunk Research Team, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Splunk Research Team"}] spec_version = 3 searches = ["ESCU - Detect Oulook exe writing a zip file - Rule", "ESCU - Suspicious LNK file launching a process - Rule", "ESCU - Get Parent Process Info"] description = Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. @@ -491,9 +513,9 @@ category = Adversary Tactics last_updated = 2020-01-22 version = 1 references = ["https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/", "http://blog.amossys.fr/badflick-is-not-so-bad.html"] -maintainers = "iDefense Cyber Espionage Team, iDefense" +maintainers = [{"company": "iDefense", "email": "-", "name": "iDefense Cyber Espionage Team"}] spec_version = 3 -searches = ["ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - Unusually Long Command Line - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. narrative = This story was created as a joint effort between iDefense and Splunk.\ iDefense analysts have recently discovered a Windows executable file that, upon execution, spoofs a decryption tool and then drops a file that appears to be the custom-built javascript backdoor, "Orz," which is associated with the threat actors known as MUDCARP (as well as "temp.Periscope" and "Leviathan"). The file is executed using Wscript.\ @@ -529,9 +551,9 @@ category = Best Practices last_updated = 2017-09-11 version = 1 references = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host"] description = Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. narrative = A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. The combined effect allows both network defenders and security controls to focus and not be mired in superfluous traffic or data types. Looking for deviations to policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. @@ -540,9 +562,9 @@ category = Malware last_updated = 2020-02-04 version = 1 references = ["https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/", "https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Registry Activities", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Sysmon WMI Activity for Host"] description = Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. narrative = Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise. Attackers can deploy ransomware to enterprises through spearphishing campaigns and driveby downloads, as well as through traditional remote service-based exploitation. In the case of the WannaCry campaign, there was self-propagating wormable functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. @@ -551,9 +573,9 @@ category = Best Practices last_updated = 2017-09-12 version = 1 references = ["https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html", "https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. narrative = Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. @@ -563,9 +585,9 @@ category = Adversary Tactics last_updated = 2017-09-19 version = 1 references = ["https://capec.mitre.org/data/definitions/66.html", "https://www.incapsula.com/web-application-security/sql-injection.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] description = Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. narrative = It is very common for attackers to inject SQL parameters into vulnerable web applications, which then interpret the malicious SQL statements.\ This Analytic Story contains a search designed to identify attempts by attackers to leverage this technique to compromise a host and gain a foothold in the target environment. @@ -575,9 +597,9 @@ category = Malware last_updated = 2018-12-13 version = 1 references = ["https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/", "https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/", "https://thehackernews.com/2018/07/samsam-ransomware-attacks.html"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Parent Process Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Samsam Test File Write - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Investigate Successful Remote Desktop Authentications"] description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. narrative = The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ @@ -591,9 +613,9 @@ category = Vulnerability last_updated = 2018-01-08 version = 1 references = ["https://meltdownattack.com/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. narrative = Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched. @@ -602,9 +624,9 @@ category = Vulnerability last_updated = 2017-09-19 version = 1 references = ["http://www.splunk.com/view/SP-CAAAPQ6#announce", "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For Endpoint"] description = Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. narrative = This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\ \ @@ -622,9 +644,9 @@ category = Vulnerability last_updated = 2018-06-14 version = 1 references = ["https://nvd.nist.gov/vuln/detail/CVE-2018-11409", "https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings", "https://www.exploit-db.com/exploits/44865/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable Info"] +searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Investigate Web Activity From src ip"] description = Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. narrative = Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). NIST has included it in its vulnerability database (read more here: https://nvd.nist.gov/vuln/detail/CVE-2018-11409). The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Customers should upgrade to the latest version to reduce the risk of this vulnerability.\ Splunk Enterprise exposes partial information about the host operating system, hardware, and Splunk license. Splunk Enterprise before 6.6.0 exposes this information without authentication. Splunk Enterprise 6.6.0 and later exposes this information only to authenticated Splunk users. Based on the information exposure, Splunk characterizes this issue as a low severity impact.\ @@ -636,9 +658,9 @@ category = Cloud Security last_updated = 2018-02-09 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details"] +searches = ["ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable Info", "ESCU - Get EC2 Launch Details", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN"] description = Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and risk auditing within your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Console, AWS command-line interface, and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your AWS EC2 instances and helps you respond and investigate those activities. @@ -647,9 +669,9 @@ category = Cloud Security last_updated = 2019-05-01 version = 1 references = ["https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect new user AWS Console Login - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - AWS Investigate User Activities By ARN"] +searches = ["ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect new user AWS Console Login - Rule", "ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - AWS Investigate User Activities By ARN"] description = Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. narrative = It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker. @@ -658,9 +680,9 @@ category = Cloud Security last_updated = 2018-07-24 version = 2 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - Detect New Open S3 buckets - Rule", "ESCU - Get User Information from Identity Table", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable History", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - AWS S3 Bucket details via bucketName"] +searches = ["ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable History", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS S3 Bucket details via bucketName"] description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. narrative = As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ @@ -671,9 +693,9 @@ category = Cloud Security last_updated = 2018-05-07 version = 1 references = ["https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Network ACL Details from ID", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get DNS traffic ratio", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - AWS Network Interface details via resourceId", "ESCU - AWS Network ACL Details from ID", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host", "ESCU - AWS Investigate User Activities By ARN"] description = Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). narrative = A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ @@ -685,9 +707,9 @@ category = Adversary Tactics last_updated = 2020-02-03 version = 2 references = ["https://attack.mitre.org/wiki/Technique/T1059", "https://www.microsoft.com/en-us/wdsi/threats/macro-malware", "https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. narrative = The ability to execute arbitrary commands via the Windows CLI is a primary goal for the adversary. With access to the shell, an attacker can easily run scripts and interact with the target system. Often, attackers may only have limited access to the shell or may obtain access in unusual ways. In addition, malware may execute and interact with the CLI in ways that would be considered unusual and inconsistent with typical user activity. This provides defenders with opportunities to identify suspicious use and investigate, as appropriate. This Analytic Story contains various searches to help identify this suspicious activity, as well as others to aid you in deeper investigation. @@ -696,9 +718,9 @@ category = Adversary Tactics last_updated = 2017-09-18 version = 1 references = ["http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/", "http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680", "https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get DNS traffic ratio", "ESCU - Get DNS Server History for a host"] description = Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. narrative = Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. @@ -707,9 +729,9 @@ category = Adversary Tactics last_updated = 2020-01-27 version = 1 references = ["https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Email Info", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Get Emails From Specific Sender", "ESCU - Get Email Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. narrative = It is a common practice for attackers of all types to leverage targeted spearphishing campaigns and mass mailers to deliver weaponized email messages and attachments. Fortunately, there are a number of ways to monitor email data in Splunk to detect suspicious content.\ Once a phishing message has been detected, the next steps are to answer the following questions: \ @@ -722,9 +744,9 @@ category = Adversary Tactics last_updated = 2020-02-03 version = 1 references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5", "https://attack.mitre.org/wiki/Technique/T1170"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect mshta exe running scripts in command-line arguments - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - Detect mshta exe running scripts in command-line arguments - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. narrative = One common adversary tactic is to bypass application white-listing solutions via the mshta.exe process, which executes Microsoft HTML applications with the .hta suffix. In these cases, attackers use the trusted Windows utility to eproxy execution of malicious files, whether an .hta application, javascript, or VBScript.\ One example of a notable mshta.exe attack was the Kovter malware (https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5) that was implicated in ransomware and click-fraud attacks. Kovter utilized .hta to execute a series of javascript commands, each progressively more dangerous. According to the Mitre Parternship Network (https://attack.mitre.org/wiki/Technique/T1170), FIN7 has leveraged mshta.exe, as has the MuddyWater group, who used it to execute its POWERSTATS payload (which then used the utility to execute additional payloads).\ @@ -735,9 +757,9 @@ category = Adversary Tactics last_updated = 2020-04-02 version = 1 references = ["https://attack.mitre.org/wiki/Technique/T1078", "https://owasp.org/www-community/attacks/Credential_stuffing", "https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Investigate User Activities In Okta", "ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate Okta Activity by app"] +searches = ["ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Investigate User Activities In Okta", "ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate Okta Activity by app"] description = Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. narrative = Okta is the leading single sign on (SSO) provider, allowing users to authenticate once to Okta, and from there access a variety of web-based applications. These applications are assigned to users and allow administrators to centrally manage which users are allowed to access which applications. It also provides centralized logging to help understand how the applications are used and by whom. \ While SSO is a major convenience for users, it also provides attackers with an opportunity. If the attacker can gain access to Okta, they can access a variety of applications. As such monitoring the environment is important. \ @@ -748,9 +770,9 @@ category = Adversary Tactics last_updated = 2018-10-23 version = 2 references = ["https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf", "https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Process Execution via WMI - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Script Execution via WMI - Rule", "ESCU - Process Execution via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Sysmon WMI Activity for Host"] description = Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. narrative = WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning to WMI abuse in their efforts to conduct nefarious tasks, such as reconnaissance, detection of antivirus and virtual machines, code execution, lateral movement, persistence, and data exfiltration. \ The detection searches included in this Analytic Story are used to look for suspicious use of WMI commands that attackers may leverage to interact with remote systems. The searches specifically look for the use of WMI to run processes on remote systems.\ @@ -761,22 +783,34 @@ category = Adversary Tactics last_updated = 2018-05-31 version = 1 references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. narrative = Attackers are developing increasingly sophisticated techniques for hijacking target servers, while evading detection. One such technique that has become progressively more common is registry modification.\ The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ The searches in this story are designed to help you detect behaviors associated with manipulation of the Windows registry. +[analytic_story://Suspicious Zoom Child Processes] +category = Adversary Tactics +last_updated = 2020-04-13 +version = 1 +references = ["https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/", "https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/"] +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] +spec_version = 3 +searches = ["ESCU - First Time Seen Child Process of Zoom - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Registry Activity", "ESCU - Get Process File Activity"] +description = Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new child processes of zoom and provides investigative actions for this detection. +narrative = Zoom is a leader in modern enterprise video communications and its usage has increased dramatically with a large amount of the population under stay-at-home orders due to the COVID-19 pandemic. With increased usage has come increased scrutiny and several security flaws have been found with this application on both Windows and macOS systems.\ +Current detections focus on finding new child processes of this application on a per host basis. Investigative searches are included to gather information needed during an investigation. + [analytic_story://Unusual AWS EC2 Modifications] category = Cloud Security last_updated = 2018-04-09 version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History", "ESCU - Get EC2 Instance Details by instanceId"] +searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History"] description = Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. narrative = A common attack technique is to infiltrate a cloud instance and make modifications. The adversary can then secure access to your infrastructure or hide their activities. So it's important to stay alert to changes that may indicate that your environment has been compromised. \ Searches within this Analytic Story can help you detect the presence of a threat by monitoring for EC2 instances that have been created or changed--either by users that have never previously performed these activities or by known users who modify or create instances in a way that have not been done before. This story also provides investigative searches that help you go deeper once you detect suspicious behavior. @@ -786,9 +820,9 @@ category = Malware last_updated = 2020-02-04 version = 2 references = ["https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html", "https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf", "https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History"] description = Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. narrative = Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ @@ -799,9 +833,9 @@ category = Best Practices last_updated = 2017-09-15 version = 1 references = ["https://www.monkey.org/~dugsong/dsniff/"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. narrative = Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information could be highly sensitive, or could allow for session hijacking. In addition, these protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. @@ -810,9 +844,9 @@ category = Abuse last_updated = 2018-10-08 version = 1 references = ["https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud", "https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718"] -maintainers = "Jim Apger, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Jim Apger"}] spec_version = 3 -searches = ["ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Emails From Specific Sender", "ESCU - Get Web Session Information via session id"] +searches = ["ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable Info", "ESCU - Get Web Session Information via session id", "ESCU - Get Notable History"] description = Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. narrative = The Federal Bureau of Investigations (FBI) defines Internet fraud as the use of Internet services or software with Internet access to defraud victims or to otherwise take advantage of them. According to the Bureau, Internet crime schemes are used to steal millions of dollars each year from victims and continue to plague the Internet through various methods. The agency includes phishing scams, data breaches, Denial of Service (DOS) attacks, email account compromise, malware, spoofing, and ransomware in this category.\ These crimes are not the fraud itself, but rather the attack techniques commonly employed by fraudsters in their pursuit of data that enables them to commit malicious actssuch as obtaining and using stolen credit cards. They represent a serious problem that is steadily increasing and not likely to go away anytime soon.\ @@ -826,9 +860,9 @@ category = Adversary Tactics last_updated = 2018-05-31 version = 1 references = ["https://attack.mitre.org/wiki/Defense_Evasion"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - Suspicious Reg exe Process - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others narrative = Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. @@ -837,9 +871,9 @@ category = Malware last_updated = 2018-01-26 version = 1 references = ["https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/", "https://attack.mitre.org/wiki/Technique/T1042"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. narrative = Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ @@ -852,9 +886,9 @@ category = Adversary Tactics last_updated = 2017-09-12 version = 2 references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/", "https://zeltser.com/security-incident-log-review-checklist/", "http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Windows Event Log Cleared - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Deleting Shadow Copies - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Vulnerability Logs For Endpoint"] description = Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. narrative = Because attackers often modify system logs to cover their tracks and/or to thwart the investigative process, log monitoring is an industry-recognized best practice. While there are legitimate reasons to manipulate system logs, it is still worthwhile to keep track of who manipulated the logs, when they manipulated them, and in what way they manipulated them (determining which accesses, tools, or utilities were employed). Even if no malicious activity is detected, the knowledge of an attempt to manipulate system logs may be indicative of a broader security risk that should be thoroughly investigated.\ The Analytic Story gives users two different ways to detect manipulation of Windows Event Logs and one way to detect deletion of the Update Sequence Number (USN) Change Journal. The story helps determine the history of the host and the users who have accessed it. Finally, the story aides in investigation by retrieving all the information on the process that caused these events (if the process has been identified). @@ -864,9 +898,9 @@ category = Adversary Tactics last_updated = 2018-05-31 version = 2 references = ["http://www.fuzzysecurity.com/tutorials/19.html", "https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html", "http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/", "https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html", "https://www.youtube.com/watch?v=dq2Hv7J9fvk"] -maintainers = "Bhavin Patel, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Bhavin Patel"}] spec_version = 3 -searches = ["ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. narrative = Maintaining persistence is one of the first steps taken by attackers after the initial compromise. Attackers leverage various custom and built-in tools to ensure survivability and persistent access within a compromised enterprise. This Analytic Story provides searches to help you identify various behaviors used by attackers to maintain persistent access to a Windows environment. @@ -875,9 +909,9 @@ category = Adversary Tactics last_updated = 2020-02-04 version = 2 references = ["https://attack.mitre.org/tactics/TA0004/"] -maintainers = "David Dorsey, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "David Dorsey"}] spec_version = 3 -searches = ["ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Registry Activities"] +searches = ["ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web Activity From Host", "ESCU - Get Notable History", "ESCU - Get Registry Activities"] description = Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. narrative = Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. @@ -886,9 +920,9 @@ category = Malware last_updated = 2017-11-02 version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1050", "https://attack.mitre.org/wiki/Technique/T1031"] -maintainers = "Rico Valdez, Splunk" +maintainers = [{"company": "Splunk", "email": "-", "name": "Rico Valdez"}] spec_version = 3 -searches = ["ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Notable History", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get User Information from Identity Table"] +searches = ["ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] description = Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. narrative = The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to run privileged code that can interact with the kernel. In many cases, attackers will create a new service to host their malicious code. Attackers have also been observed modifying unnecessary or unused services to point to their own code, as opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. @@ -905,7 +939,7 @@ how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or lat annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule] type = detection @@ -916,7 +950,7 @@ how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or lat annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] type = detection @@ -927,7 +961,7 @@ how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or lat annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule] type = detection @@ -938,7 +972,7 @@ how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or lat annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule] type = detection @@ -948,7 +982,7 @@ explanation = This search looks for AssumeRole events where an IAM role in a dif how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - AWS Network Access Control List Created with All Open Ports - Rule] type = detection @@ -958,7 +992,7 @@ explanation = The search looks for CloudTrail events to detect if any network AC how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - AWS Network Access Control List Deleted - Rule] type = detection @@ -968,7 +1002,7 @@ explanation = Enforcing network-access controls is one of the defensive mechanis how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has legitimately deleted a network ACL. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Launched by User - Rule] type = detection @@ -978,7 +1012,7 @@ explanation = This search looks for CloudTrail events where a user successfully how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule] type = detection @@ -988,7 +1022,7 @@ explanation = This search looks for CloudTrail events where a user successfully how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - Rule] type = detection @@ -998,7 +1032,7 @@ explanation = This search looks for CloudTrail events where an abnormally high n how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule] type = detection @@ -1008,7 +1042,7 @@ explanation = This search looks for CloudTrail events where a user successfully how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Access LSASS Memory for Dump Creation - Rule] type = detection @@ -1018,7 +1052,7 @@ explanation = Detect memory dumping of the LSASS process. how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Amazon EKS Kubernetes Pod scan detection - Rule] type = detection @@ -1028,7 +1062,7 @@ explanation = This search provides detection information on unauthenticated requ how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Amazon EKS Kubernetes cluster scan detection - Rule] type = detection @@ -1038,7 +1072,7 @@ explanation = This search provides information of unauthenticated requests via u how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Attempt To Add Certificate To Untrusted Store - Rule] type = detection @@ -1048,7 +1082,7 @@ explanation = Attempt to add a certificate to the untrusted certificate store how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule] type = detection @@ -1058,7 +1092,7 @@ explanation = Monitor for changes of the ExecutionPolicy in the registry to the how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["DE.CM"]} known_false_positives = Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Attempt To Stop Security Service - Rule] type = detection @@ -1068,7 +1102,7 @@ explanation = This search looks for attempts to stop security-related services o how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Attempted Credential Dump From Registry via Reg exe - Rule] type = detection @@ -1078,7 +1112,7 @@ explanation = Monitor for execution of reg.exe with parameters specifying an exp how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = None identified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Batch File Write to System32 - Rule] type = detection @@ -1088,7 +1122,7 @@ explanation = The search looks for a batch file (.bat) written to the Windows sy how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Child Processes of Spoolsv exe - Rule] type = detection @@ -1098,7 +1132,7 @@ explanation = This search looks for child processes of spoolsv.exe. This activit how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. annotations = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} known_false_positives = Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Clients Connecting to Multiple DNS Servers - Rule] type = detection @@ -1110,7 +1144,7 @@ This search produces fields (`dest_count`) that are not yet supported by ES Inci Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048"], "nist": ["PR.PT", "DE.AE", "PR.DS"]} known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule] type = detection @@ -1120,7 +1154,7 @@ explanation = This search looks for cloud compute instances created by users who how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule] type = detection @@ -1130,7 +1164,7 @@ explanation = This search looks for cloud compute instances being created with p how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Images" support search to create a baseline of previously seen images. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule] type = detection @@ -1140,7 +1174,7 @@ explanation = Find EC2 instances being created with previously unseen instance t how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the " Previously Seen Cloud Compute Instance Types" support search to create a baseline of previously seen regions. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule] type = detection @@ -1150,7 +1184,7 @@ explanation = This search looks at cloud-infrastructure events where an instance how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the \"Previously Seen Cloud Compute Instance Types\" support search to create a baseline of previously seen regions. annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Common Ransomware Extensions - Rule] type = detection @@ -1164,7 +1198,7 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Common Ransomware Notes - Rule] type = detection @@ -1174,7 +1208,7 @@ explanation = The search looks for files created with names matching those typic how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Create Remote Thread into LSASS - Rule] type = detection @@ -1184,7 +1218,7 @@ explanation = Detect remote thread creation into LSASS consistent with credentia how_to_implement = This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Create local admin accounts using net exe - Rule] type = detection @@ -1194,7 +1228,7 @@ explanation = This search looks for the creation of local administrator accounts how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create admin accounts. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Create or delete windows shares using net exe - Rule] type = detection @@ -1204,7 +1238,7 @@ explanation = This search looks for the creation or deletion of hidden shares us how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Creation of Shadow Copy - Rule] type = detection @@ -1214,7 +1248,7 @@ explanation = Monitor for signs that Ntdsutil, Vssadmin, or Wmic has been used t how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Legtimate administrator usage of Ntdsutil, Vssadmin, or Wmic will create false positives. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Creation of Shadow Copy with wmic and powershell - Rule] type = detection @@ -1224,7 +1258,7 @@ explanation = This search detects the use of wmic and Powershell to create a sha how_to_implement = You must enable Powershell scriptblock logging in order to detect this attack.This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Legtimate administrator usage of wmic to create a shadow copy. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule] type = detection @@ -1234,7 +1268,7 @@ explanation = This search detects credential dumping using copy command from a s how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = unknown -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Credential Dumping via Symlink to Shadow Copy - Rule] type = detection @@ -1244,7 +1278,7 @@ explanation = This search detects the creation of a symlink to a shadow copy. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = unknown -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - DNS Query Length Outliers - MLTK - Rule] type = detection @@ -1260,7 +1294,7 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - DNS Query Length With High Standard Deviation - Rule] type = detection @@ -1270,7 +1304,7 @@ explanation = This search allows you to identify DNS requests and compute the st how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It's possible there can be long domain names that are legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] type = detection @@ -1280,7 +1314,7 @@ explanation = This search will detect DNS requests resolved by unauthorized DNS how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - DNS record changed - Rule] type = detection @@ -1294,7 +1328,7 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "DNS annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Deleting Shadow Copies - Rule] type = detection @@ -1304,7 +1338,7 @@ explanation = The vssadmin.exe utility is used to interact with the Volume Shado how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect API activity from users without MFA - Rule] type = detection @@ -1320,7 +1354,7 @@ This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect AWS API Activities From Unapproved Accounts - Rule] type = detection @@ -1336,7 +1370,7 @@ This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not ye Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect AWS Console Login by User from New City - Rule] type = detection @@ -1346,7 +1380,7 @@ explanation = This search looks for CloudTrail events wherein a console login ev how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect AWS Console Login by User from New Country - Rule] type = detection @@ -1356,7 +1390,7 @@ explanation = This search looks for CloudTrail events wherein a console login ev how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect AWS Console Login by User from New Region - Rule] type = detection @@ -1366,7 +1400,7 @@ explanation = This search looks for CloudTrail events wherein a console login ev how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] type = detection @@ -1376,7 +1410,7 @@ explanation = This search looks for specific authentication events from the Wind how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1075"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Credential Dumping through LSASS access - Rule] type = detection @@ -1386,7 +1420,7 @@ explanation = This search looks for reading lsass memory consistent with credent how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect DNS requests to Phishing Sites leveraging EvilGinx2 - Rule] type = detection @@ -1400,7 +1434,7 @@ If Splunk>Phantom is also configured in your environment, a Playbook called `Let annotations = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1192"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Excessive Account Lockouts From Endpoint - Rule] type = detection @@ -1414,7 +1448,7 @@ If Splunk>Phantom is also configured in your environment, a Playbook called "Exc annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["PR.IP"]} known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Excessive User Account Lockouts - Rule] type = detection @@ -1424,7 +1458,7 @@ explanation = This search detects user accounts that have been locked out a rela how_to_implement = ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["PR.IP"]} known_false_positives = It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Large Outbound ICMP Packets - Rule] type = detection @@ -1434,7 +1468,7 @@ explanation = This search looks for outbound ICMP packets with a packet size lar how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1095"], "nist": ["DE.AE"]} known_false_positives = ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the search to adjust the byte threshold or whitelist specific IP addresses, as necessary. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Long DNS TXT Record Response - Rule] type = detection @@ -1444,7 +1478,7 @@ explanation = This search is used to detect attempts to use DNS tunneling, by ca how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Mimikatz Using Loaded Images - Rule] type = detection @@ -1454,7 +1488,7 @@ explanation = This search looks for reading loaded Images unique to credential d how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.AE", "DE.CM"]} known_false_positives = Other tools can import the same DLLs. These tools should be part of a whtelist. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4703 - Rule] type = detection @@ -1464,7 +1498,7 @@ explanation = This search looks for PowerShell requesting privileges consistent how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect New Local Admin account - Rule] type = detection @@ -1480,7 +1514,7 @@ This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1078"], "nist": ["PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect New Login Attempts to Routers - Rule] type = detection @@ -1490,7 +1524,7 @@ explanation = The search queries the authentication logs for assets that are cat how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]} known_false_positives = Legitimate router connections may appear as new connections -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect New Open S3 buckets - Rule] type = detection @@ -1500,7 +1534,7 @@ explanation = This search looks for CloudTrail events where a user has created a how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail inputs. The threshold value should be tuned to your environment. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Oulook exe writing a zip file - Rule] type = detection @@ -1510,7 +1544,7 @@ explanation = This search looks for execution of process `outlook.exe` where the how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = It is not uncommon for outlook to write legitimate zip files to the disk. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Outbound SMB Traffic - Rule] type = detection @@ -1520,7 +1554,7 @@ explanation = This search looks for outbound SMB connections made by hosts withi how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Path Interception By Creation Of program exe - Rule] type = detection @@ -1530,7 +1564,7 @@ explanation = The search is looking for the creation of program.exe in the C: dr how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is unlikely that a normal user may create and place this file in the C: drive. Confirm with the user. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Prohibited Applications Spawning cmd exe - Rule] type = detection @@ -1540,7 +1574,7 @@ explanation = This search looks for executions of cmd.exe spawned by a process t how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect PsExec With accepteula Flag - Rule] type = detection @@ -1550,7 +1584,7 @@ explanation = This search looks for events where `PsExec.exe` is run with the `a how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Rare Executables - Rule] type = detection @@ -1560,7 +1594,7 @@ explanation = This search will return a table of rare processes, the names of th how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_whitelist` searches two lookup files to whitelist your processes. These consist of `rare_process_whitelist_default.csv` and `rare_process_whitelist_local.csv`. To add your own processes to the whitelist, add them to `rare_process_whitelist_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the whitelist value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. annotations = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]} known_false_positives = Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_whitelist_local.csv` to filter them out of your search results. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect S3 access from a new IP - Rule] type = detection @@ -1570,7 +1604,7 @@ explanation = This search looks at S3 bucket-access logs and detects new or prev how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. annotations = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Spike in AWS API Activity - Rule] type = detection @@ -1586,7 +1620,7 @@ This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Spike in Network ACL Activity - Rule] type = detection @@ -1596,7 +1630,7 @@ explanation = This search will detect users creating spikes in API activity rela how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. annotations = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Spike in S3 Bucket deletion - Rule] type = detection @@ -1606,7 +1640,7 @@ explanation = This search detects users creating spikes in API activity related how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Spike in Security Group Activity - Rule] type = detection @@ -1616,7 +1650,7 @@ explanation = This search will detect users creating spikes in API activity rela how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] type = detection @@ -1626,7 +1660,7 @@ explanation = This search will detect spike in blocked outbound network connecti how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect USB device insertion - Rule] type = detection @@ -1636,7 +1670,7 @@ explanation = The search is used to detect hosts that generate Windows Event ID how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"]} known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Unauthorized Assets by MAC address - Rule] type = detection @@ -1646,7 +1680,7 @@ explanation = By populating the organization's assets within the assets_by_str.c how_to_implement = This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. annotations = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule] type = detection @@ -1656,7 +1690,7 @@ explanation = This search looks for the execution of the cscript.exe or wscript. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications may exhibit this behavior. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] type = detection @@ -1666,7 +1700,7 @@ explanation = This search looks for specific GET or HEAD requests to web servers how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1082"]} known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect hosts connecting to dynamic domain providers - Rule] type = detection @@ -1682,7 +1716,7 @@ This search produces fields (query, answer, isDynDNS) that are not yet supported Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} known_false_positives = Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - Rule] type = detection @@ -1692,7 +1726,7 @@ explanation = This search is used to detect malicious HTTP requests crafted to e how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model annotations = {"cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} known_false_positives = No known false positives for this detection. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect mshta exe running scripts in command-line arguments - Rule] type = detection @@ -1702,7 +1736,7 @@ explanation = This search looks for the execution of "mshta.exe" with command-li how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect new API calls from user roles - Rule] type = detection @@ -1712,7 +1746,7 @@ explanation = This search detects new API calls that have either never been seen how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect new user AWS Console Login - Rule] type = detection @@ -1722,7 +1756,7 @@ explanation = This search looks for CloudTrail events wherein a console login ev how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect processes used for System Network Configuration Discovery - Rule] type = detection @@ -1732,7 +1766,7 @@ explanation = This search looks for fast execution of processes used for system how_to_implement = You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detect web traffic to dynamic domain providers - Rule] type = detection @@ -1744,7 +1778,7 @@ This search produces fields (`isDynDNS`) that are not yet supported by ES Incide Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1102", "T1041"], "nist": ["PR.IP", "DE.DP"]} known_false_positives = It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detection of DNS Tunnels - Rule] type = detection @@ -1754,7 +1788,7 @@ explanation = This search is used to detect DNS tunneling, by calculating the su how_to_implement = To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "PR.DS"]} known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Detection of tools built by NirSoft - Rule] type = detection @@ -1764,7 +1798,7 @@ explanation = This search looks for specific command-line arguments that may ind how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072", "T1087"], "nist": ["PR.IP"]} known_false_positives = While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Disabling Remote User Account Control - Rule] type = detection @@ -1774,7 +1808,7 @@ explanation = The search looks for modifications to registry keys that control t how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Dump LSASS via comsvcs DLL - Rule] type = detection @@ -1784,7 +1818,7 @@ explanation = Detect the usage of comsvcs.dll for dumping the lsass process. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = None identified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Modified With Previously Unseen User - Rule] type = detection @@ -1794,7 +1828,7 @@ explanation = This search looks for EC2 instances being modified by users who ha how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started In Previously Unseen Region - Rule] type = detection @@ -1804,7 +1838,7 @@ explanation = This search looks for CloudTrail events where an instance is start how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen AMI - Rule] type = detection @@ -1814,7 +1848,7 @@ explanation = This search looks for EC2 instances being created with previously how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] type = detection @@ -1824,7 +1858,7 @@ explanation = This search looks for EC2 instances being created with previously how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen User - Rule] type = detection @@ -1834,7 +1868,7 @@ explanation = This search looks for EC2 instances being created by users who hav how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Email Attachments With Lots Of Spaces - Rule] type = detection @@ -1846,7 +1880,7 @@ how_to_implement = You need to ingest data from emails. Specifically, the sender If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Email files written outside of the Outlook directory - Rule] type = detection @@ -1856,7 +1890,7 @@ explanation = The search looks at the change-analysis data model and detects ema how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114"]} known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Email servers sending high volume traffic to hosts - Rule] type = detection @@ -1866,7 +1900,7 @@ explanation = This search looks for an increase of data transfers from your emai how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114", "T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Excessive DNS Failures - Rule] type = detection @@ -1876,7 +1910,7 @@ explanation = This search identifies DNS query failures by counting the number o how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. annotations = {"cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048", "T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Execution of File With Spaces Before Extension - Rule] type = detection @@ -1886,7 +1920,7 @@ explanation = This search looks for processes launched from files with at least how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Execution of File with Multiple Extensions - Rule] type = detection @@ -1896,7 +1930,7 @@ explanation = This search looks for processes launched from files that have doub how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Extended Period Without Successful Netbackup Backups - Rule] type = detection @@ -1906,7 +1940,7 @@ explanation = This search returns a list of hosts that have not successfully com how_to_implement = To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - File with Samsam Extension - Rule] type = detection @@ -1916,7 +1950,17 @@ explanation = The search looks for file writes with extensions consistent with a how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Because these extensions are not typically used in normal operations, you should investigate all results. -providing_technologies = none +providing_technologies = [] + +[savedsearch://ESCU - First Time Seen Child Process of Zoom - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window. +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1068"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +known_false_positives = A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken. +providing_technologies = [] [savedsearch://ESCU - First Time Seen Running Windows Service - Rule] type = detection @@ -1926,7 +1970,7 @@ explanation = This search looks for the first time a Windows service is seen run how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs in order for this search to execute successfully. The support search, `Previously Seen Running Windows Services`, should be run before this search to create the baseline of known Windows services. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. annotations = {"cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1050"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]} known_false_positives = A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - First time seen command line argument - Rule] type = detection @@ -1936,7 +1980,7 @@ explanation = This search looks for command-line arguments that use a `/c` param how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1064", "T1059"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - GCP GCR container uploaded - Rule] type = detection @@ -1946,7 +1990,7 @@ explanation = This search show information on uploaded containers including sour how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. annotations = {} known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - GCP Kubernetes cluster scan detection - Rule] type = detection @@ -1956,7 +2000,7 @@ explanation = This search provides information of unauthenticated requests via u how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Hiding Files And Directories With Attrib exe - Rule] type = detection @@ -1966,7 +2010,7 @@ explanation = Attackers leverage an existing Windows binary, attrib.exe, to mark how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Hosts receiving high volume of network traffic from email server - Rule] type = detection @@ -1976,7 +2020,7 @@ explanation = This search looks for an increase of data transfers from your emai how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Identify New User Accounts - Rule] type = detection @@ -1986,7 +2030,87 @@ explanation = This detection search will help profile user accounts in your envi how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1136"], "nist": ["PR.IP"]} known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. -providing_technologies = none +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect RBAC authorization by account - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes RBAC authorizations by accounts, this search can be modified by adding rare or top to see both extremes of RBAC by accounts occurrences +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all RBAC Authorications are malicious. RBAC authorizations can uncover malicious activity specially if sensitive Roles have been granted. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect most active service accounts by pod namespace - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts,accessing pods and namespaces by IP address and verb +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Not all service accounts interactions are malicious. Analyst must consider IP and verb context when trying to detect maliciousness. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect sensitive object access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive object access is not necessarily malicious but user and object context can provide guidance for detection. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect sensitive role access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes accounts accessing sensitve objects such as configmpas or secrets +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Sensitive role resource access is necessary for cluster operation, however source IP, namespace and user group may indicate possible malicious use. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect service accounts forbidden failure access - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubernetes service accounts with failure or forbidden access status +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = This search can give false positives as there might be inherent issues with authentications and permissions at cluster. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure detect suspicious kubectl calls - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information on Kubectl calls with IP, verb namespace and object access context +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Lateral Movement"]} +known_false_positives = Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially suspicious IPs and sensitive objects such as configmaps or secrets +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure pod scan fingerprint - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster pod in Azure +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Reconnaissance"]} +known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +providing_technologies = [] + +[savedsearch://ESCU - Kubernetes Azure scan fingerprint - Rule] +type = detection +asset_type = Azure AKS Kubernetes cluster +confidence = medium +explanation = This search provides information of unauthenticated requests via source IP user agent, request URI and response status data against Kubernetes cluster in Azure +how_to_implement = You must install the Add-on for Microsoft Cloud Services and Configure Kube-Audit data diagnostics +annotations = {"kill_chain_phases": ["Reconnaissance"]} +known_false_positives = Not all unauthenticated requests are malicious, but source IPs, userAgent, verb, request URI and response status will provide context. +providing_technologies = [] [savedsearch://ESCU - Large Volume of DNS ANY Queries - Rule] type = detection @@ -1996,7 +2120,7 @@ explanation = The search is used to identify attempts to use your DNS Infrastruc how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. annotations = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.IP"]} known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - MacOS - Re-opened Applications - Rule] type = detection @@ -2006,7 +2130,7 @@ explanation = This search looks for processes referencing the plist files that d how_to_implement = In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM"]} known_false_positives = At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be whitelisted. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule] type = detection @@ -2016,7 +2140,7 @@ explanation = This search looks for PowerShell processes started with parameters how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule] type = detection @@ -2026,7 +2150,7 @@ explanation = This search looks for PowerShell processes that have encoded the s how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = System administrators may use this option, but it's not common. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule] type = detection @@ -2036,7 +2160,7 @@ explanation = This search looks for PowerShell processes started with parameters how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule] type = detection @@ -2046,7 +2170,7 @@ explanation = This search looks for PowerShell processes started with a base64 e how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule] type = detection @@ -2056,7 +2180,7 @@ explanation = This search looks for PowerShell processes launched with arguments how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = These characters might be legitimately on the command-line, but it is not common. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Monitor DNS For Brand Abuse - Rule] type = detection @@ -2066,7 +2190,7 @@ explanation = This search looks for DNS requests for faux domains similar to the how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. annotations = {"kill_chain_phases": ["Delivery", "Actions on Objectives"]} known_false_positives = None at this time -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Monitor Email For Brand Abuse - Rule] type = detection @@ -2076,7 +2200,7 @@ explanation = This search looks for emails claiming to be sent from a domain sim how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Monitor Registry Keys for Print Monitors - Rule] type = detection @@ -2086,7 +2210,7 @@ explanation = This search looks for registry activity associated with modificati how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. annotations = {"cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.AC"]} known_false_positives = You will encounter noise from legitimate print-monitor registry entries. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Monitor Web Traffic For Brand Abuse - Rule] type = detection @@ -2096,7 +2220,7 @@ explanation = This search looks for Web requests to faux domains similar to the how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule] type = detection @@ -2106,7 +2230,7 @@ explanation = This search detects Okta login failures due to bad credentials for how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro XXXXXXXXXXXXX to raise the threshold or except specific IP adresses from triggering this search. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - New container uploaded to AWS ECR - Rule] type = detection @@ -2116,7 +2240,7 @@ explanation = This searches show information on uploaded containers including so how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. annotations = {} known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - No Windows Updates in a time frame - Rule] type = detection @@ -2126,7 +2250,7 @@ explanation = This search looks for Windows endpoints that have not generated an how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. annotations = {"cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Okta Account Lockout Events - Rule] type = detection @@ -2136,7 +2260,7 @@ explanation = Detect Okta user lockout events how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Okta Failed SSO Attempts - Rule] type = detection @@ -2146,7 +2270,7 @@ explanation = Detect failed Okta SSO events how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = There may be a faulty config preventing legitmate users from accessing apps they should have access to. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Okta User Logins From Multiple Cities - Rule] type = detection @@ -2156,7 +2280,7 @@ explanation = This search detects logins from the same user from different state how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Open Redirect in Splunk Web - Rule] type = detection @@ -2166,7 +2290,7 @@ explanation = This search allows you to look for evidence of exploitation for CV how_to_implement = No extra steps needed to implement this search. annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Osquery pack - ColdRoot detection - Rule] type = detection @@ -2176,7 +2300,7 @@ explanation = This search looks for ColdRoot events from the osx-attacks osquery how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]} known_false_positives = There are no known false positives. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Overwriting Accessibility Binaries - Rule] type = detection @@ -2186,7 +2310,7 @@ explanation = Microsoft Windows contains accessibility features that can be laun how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Process Execution via WMI - Rule] type = detection @@ -2196,7 +2320,7 @@ explanation = This search looks for processes launched via WMI. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Processes Tapping Keyboard Events - Rule] type = detection @@ -2206,7 +2330,7 @@ explanation = This search looks for processes in an MacOS system that is tapping how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP"]} known_false_positives = There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Processes created by netsh - Rule] type = detection @@ -2216,7 +2340,7 @@ explanation = This search looks for processes launching netsh.exe to execute var how_to_implement = To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Processes launching netsh - Rule] type = detection @@ -2226,7 +2350,7 @@ explanation = This search looks for processes launching netsh.exe. Netsh is a co how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059", "T1089"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Prohibited Network Traffic Allowed - Rule] type = detection @@ -2236,7 +2360,7 @@ explanation = This search looks for network traffic defined by port and transpor how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Prohibited Software On Endpoint - Rule] type = detection @@ -2246,7 +2370,7 @@ explanation = This search looks for applications on the endpoint that you have m how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response products, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Protocol or Port Mismatch - Rule] type = detection @@ -2256,7 +2380,7 @@ explanation = This search looks for network traffic on common ports where a high how_to_implement = Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Protocols passing authentication in cleartext - Rule] type = detection @@ -2266,7 +2390,7 @@ explanation = This search looks for cleartext protocols at risk of leaking crede how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. annotations = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} known_false_positives = Some networks may use kerberized FTP or telnet servers, however, this is rare. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule] type = detection @@ -2276,7 +2400,7 @@ explanation = The search looks for reg.exe modifying registry keys that define W how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} known_false_positives = It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Reg exe used to hide files directories via registry keys - Rule] type = detection @@ -2286,7 +2410,7 @@ explanation = The search looks for command-line arguments used to hide a file or how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = None at the moment -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Registry Keys Used For Persistence - Rule] type = detection @@ -2296,7 +2420,7 @@ explanation = The search looks for modifications to registry keys that can be us how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1103", "T1131"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Registry Keys Used For Privilege Escalation - Rule] type = detection @@ -2306,7 +2430,7 @@ explanation = This search looks for modifications to registry keys that can be u how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Registry Keys for Creating SHIM Databases - Rule] type = detection @@ -2316,7 +2440,7 @@ explanation = This search looks for registry activity associated with applicatio how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Remote Desktop Network Bruteforce - Rule] type = detection @@ -2326,7 +2450,7 @@ explanation = This search looks for RDP application network traffic and filters how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. annotations = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Remote Desktop Network Traffic - Rule] type = detection @@ -2336,7 +2460,7 @@ explanation = This search looks for network traffic on TCP/3389, the default por how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Remote Desktop Process Running On System - Rule] type = detection @@ -2346,7 +2470,7 @@ explanation = This search looks for the remote desktop process mstsc.exe running how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Remote Process Instantiation via WMI - Rule] type = detection @@ -2356,7 +2480,7 @@ explanation = This search looks for wmic.exe being launched with parameters to s how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Remote Registry Key modifications - Rule] type = detection @@ -2366,7 +2490,7 @@ explanation = This search monitors for remote modifications to registry keys. how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Remote WMI Command Attempt - Rule] type = detection @@ -2376,7 +2500,7 @@ explanation = This search looks for wmic.exe being launched with parameters to o how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Administrators may use this legitimately to gather info from remote systems. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - RunDLL Loading DLL By Ordinal - Rule] type = detection @@ -2386,7 +2510,7 @@ explanation = This search looks for DLLs under %AppData% being loaded by rundll3 how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1085"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - SMB Traffic Spike - Rule] type = detection @@ -2396,7 +2520,7 @@ explanation = This search looks for spikes in the number of Server Message Block how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - SMB Traffic Spike - MLTK - Rule] type = detection @@ -2409,7 +2533,7 @@ This search produces a field (Number of events,count) that are not yet supported Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - SQL Injection with Long URLs - Rule] type = detection @@ -2419,7 +2543,7 @@ explanation = This search looks for long URLs that have several SQL commands vis how_to_implement = To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. annotations = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1043"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Samsam Test File Write - Rule] type = detection @@ -2429,7 +2553,7 @@ explanation = The search looks for a file named "test.txt" written to the window how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = No false positives have been identified. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Sc exe Manipulating Windows Services - Rule] type = detection @@ -2439,7 +2563,7 @@ explanation = This search looks for arguments to sc.exe indicating the creation how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} known_false_positives = Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule] type = detection @@ -2449,7 +2573,7 @@ explanation = This search looks for flags passed to schtasks.exe on the command- how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = No known false positives -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Scheduled tasks used in BadRabbit ransomware - Rule] type = detection @@ -2459,7 +2583,7 @@ explanation = This search looks for flags passed to schtasks.exe on the command- how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = No known false positives -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Schtasks scheduling job on remote system - Rule] type = detection @@ -2469,7 +2593,7 @@ explanation = This search looks for flags passed to schtasks.exe on the command- how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Schtasks used for forcing a reboot - Rule] type = detection @@ -2479,7 +2603,7 @@ explanation = This search looks for flags passed to schtasks.exe on the command- how_to_implement = To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Script Execution via WMI - Rule] type = detection @@ -2489,7 +2613,7 @@ explanation = This search looks for scripts launched via WMI. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Shim Database File Creation - Rule] type = detection @@ -2499,7 +2623,7 @@ explanation = This search looks for shim database files being written to default how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["DE.CM"]} known_false_positives = Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule] type = detection @@ -2509,7 +2633,7 @@ explanation = This search detects the process execution and arguments required t how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["DE.CM"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Short Lived Windows Accounts - Rule] type = detection @@ -2519,7 +2643,7 @@ explanation = This search detects accounts that were created and deleted in a sh how_to_implement = This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1136"], "nist": ["PR.IP"]} known_false_positives = It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Single Letter Process On Endpoint - Rule] type = detection @@ -2529,7 +2653,7 @@ explanation = This search looks for process names that consist only of a single how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Spectre and Meltdown Vulnerable Systems - Rule] type = detection @@ -2539,7 +2663,7 @@ explanation = The search is used to detect systems that are still vulnerable to how_to_implement = The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. annotations = {"cis20": ["CIS 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]} known_false_positives = It is possible that your vulnerability scanner is not detecting that the patches have been applied. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Spike in File Writes - Rule] type = detection @@ -2549,7 +2673,7 @@ explanation = The search looks for a sharp increase in the number of files writt how_to_implement = In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response products, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Splunk Enterprise Information Disclosure - Rule] type = detection @@ -2559,7 +2683,7 @@ explanation = This search allows you to look for evidence of exploitation for CV how_to_implement = The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives. annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} known_false_positives = Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious Changes to File Associations - Rule] type = detection @@ -2569,7 +2693,7 @@ explanation = This search looks for changes to registry values that control Wind how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious Email - UBA Anomaly - Rule] type = detection @@ -2579,7 +2703,7 @@ explanation = This detection looks for emails that are suspicious because of the how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and whitelist the URL if you determine that it is a legitimate sender. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious Email Attachment Extensions - Rule] type = detection @@ -2591,7 +2715,7 @@ how_to_implement = You need to ingest data from emails. Specifically, the sender If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["DE.AE", "PR.IP"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious File Write - Rule] type = detection @@ -2601,7 +2725,7 @@ explanation = The search looks for files created with names that have been linke how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious Java Classes - Rule] type = detection @@ -2611,7 +2735,7 @@ explanation = This search looks for suspicious Java classes that are often used how_to_implement = In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. annotations = {"cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "nist": ["DE.AE"]} known_false_positives = There are no known false positives. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious LNK file launching a process - Rule] type = detection @@ -2621,7 +2745,7 @@ explanation = This search looks for a ``*.lnk` file under `C:\User*` or `*\Local how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This detection should yield little or no false positive results. It is uncommon for LNK files to execute process from temporary or user directories. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious Reg exe Process - Rule] type = detection @@ -2631,7 +2755,7 @@ explanation = This search looks for reg.exe being launched from a command prompt how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112", "T1089"], "nist": ["DE.CM"]} known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious wevtutil Usage - Rule] type = detection @@ -2641,7 +2765,7 @@ explanation = The wevtutil.exe application is the windows event log utility. Thi how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]} known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious writes to System Volume Information - Rule] type = detection @@ -2651,7 +2775,7 @@ explanation = This search detects writes to the 'System Volume Information' fold how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]} known_false_positives = It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Suspicious writes to windows Recycle Bin - Rule] type = detection @@ -2661,7 +2785,7 @@ explanation = This search detects writes to the recycle bin by a process other t how_to_implement = To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]} known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - System Processes Run From Unexpected Locations - Rule] type = detection @@ -2671,7 +2795,7 @@ explanation = This search looks for system processes that normally run out of C: how_to_implement = To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - TOR Traffic - Rule] type = detection @@ -2681,7 +2805,7 @@ explanation = This search looks for network traffic identified as The Onion Rout how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE"]} known_false_positives = None at this time -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - USN Journal Deletion - Rule] type = detection @@ -2691,7 +2815,7 @@ explanation = The fsutil.exe application is a legitimate Windows utility used to how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Uncommon Processes On Endpoint - Rule] type = detection @@ -2701,7 +2825,7 @@ explanation = This search looks for applications on the endpoint that you have m how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Unload Sysmon Filter Driver - Rule] type = detection @@ -2711,7 +2835,7 @@ explanation = Attackers often disable security tools to avoid detection. This se how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["DE.CM"]} known_false_positives = -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Unsigned Image Loaded by LSASS - Rule] type = detection @@ -2721,7 +2845,7 @@ explanation = This search detects loading of unsigned images by LSASS. how_to_implement = This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Unsuccessful Netbackup backups - Rule] type = detection @@ -2731,7 +2855,7 @@ explanation = This search gives you the hosts where a backup was attempted and t how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} known_false_positives = None identified -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Unusually Long Command Line - Rule] type = detection @@ -2741,7 +2865,7 @@ explanation = Command lines that are extremely long may be indicative of malicio how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications start with long command lines. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Unusually Long Command Line - MLTK - Rule] type = detection @@ -2751,7 +2875,7 @@ explanation = Command lines that are extremely long may be indicative of malicio how_to_implement = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Unusually Long Content-Type Length - Rule] type = detection @@ -2761,7 +2885,7 @@ explanation = This search looks for unusually long strings in the Content-Type h how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} known_false_positives = Very few legitimate Content-Type fields will have a length greater than 100 characters. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - WMI Permanent Event Subscription - Rule] type = detection @@ -2771,7 +2895,7 @@ explanation = This search looks for the creation of WMI permanent event subscrip how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - WMI Permanent Event Subscription - Sysmon - Rule] type = detection @@ -2781,7 +2905,7 @@ explanation = This search looks for the creation of WMI permanent event subscrip how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - WMI Temporary Event Subscription - Rule] type = detection @@ -2791,7 +2915,7 @@ explanation = This search looks for the creation of WMI temporary event subscrip how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Web Fraud - Account Harvesting - Rule] type = detection @@ -2801,7 +2925,7 @@ explanation = This search is used to identify the creation of multiple user acco how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Web Fraud - Anomalous User Clickspeed - Rule] type = detection @@ -2811,7 +2935,7 @@ explanation = This search is used to examine web sessions to identify those wher how_to_implement = Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. annotations = {"cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.AE", "DE.CM"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Web Fraud - Password Sharing Across Accounts - Rule] type = detection @@ -2821,7 +2945,7 @@ explanation = This search is used to identify user accounts that share a common how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Web Servers Executing Suspicious Processes - Rule] type = detection @@ -2831,7 +2955,7 @@ explanation = This search looks for suspicious processes on all systems labeled how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]} known_false_positives = Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Windows Event Log Cleared - Rule] type = detection @@ -2841,7 +2965,7 @@ explanation = This search looks for Windows events that indicate one of the Wind how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts. annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]} known_false_positives = It is possible that these logs may be legitimately cleared by Administrators. -providing_technologies = none +providing_technologies = [] [savedsearch://ESCU - Windows hosts file modification - Rule] type = detection @@ -2851,7 +2975,7 @@ explanation = The search looks for modifications to the hosts file on all Window how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. annotations = {"cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} known_false_positives = There may be legitimate reasons for system administrators to add entries to this file. -providing_technologies = none +providing_technologies = [] ### END DETECTIONS ### @@ -3657,6 +3781,20 @@ how_to_implement = While this search does not require you to adhere to Splunk CI known_false_positives = not defined providing_technologies = none +[savedsearch://ESCU - Previously Seen Zoom Child Processes - Initial] +type = support +explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is outputed to disk. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. +known_false_positives = not defined +providing_technologies = none + +[savedsearch://ESCU - Previously Seen Zoom Child Processes - Update] +type = support +explanation = This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. +known_false_positives = not defined +providing_technologies = none + [savedsearch://ESCU - Previously seen API call per user roles in CloudTrail] type = support explanation = This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role. diff --git a/package/lookups/mitre_enrichment.csv b/package/lookups/mitre_enrichment.csv new file mode 100644 index 0000000000..9f7287c354 --- /dev/null +++ b/package/lookups/mitre_enrichment.csv @@ -0,0 +1,267 @@ +mitre_id,technique,tactics,groups +T1531,Account Access Removal,Impact,no +T1506,Web Session Cookie,Defense Evasion|Lateral Movement,no +T1539,Steal Web Session Cookie,Credential Access,no +T1529,System Shutdown/Reboot,Impact,Lazarus Group|APT38|APT37 +T1519,Emond,Persistence|Privilege Escalation,no +T1518,Software Discovery,Discovery,no +T1534,Internal Spearphishing,Lateral Movement,no +T1528,Steal Application Access Token,Credential Access,APT28 +T1522,Cloud Instance Metadata API,Credential Access,no +T1536,Revert Cloud Instance,Defense Evasion,no +T1535,Unused/Unsupported Cloud Regions,Defense Evasion,no +T1525,Implant Container Image,Persistence,no +T1538,Cloud Service Dashboard,Discovery,no +T1530,Data from Cloud Storage Object,Collection,no +T1537,Transfer Data to Cloud Account,Exfiltration,no +T1526,Cloud Service Discovery,Discovery,no +T1527,Application Access Token,Defense Evasion|Lateral Movement,APT28 +T1514,Elevated Execution with Prompt,Privilege Escalation,no +T1505,Server Software Component,Persistence,no +T1503,Credentials from Web Browsers,Credential Access,TA505|Stolen Pencil|MuddyWater +T1504,PowerShell Profile,Persistence|Privilege Escalation,Turla +T1502,Parent PID Spoofing,Defense Evasion|Privilege Escalation,no +T1500,Compile After Delivery,Defense Evasion,MuddyWater +T1501,Systemd Service,Persistence,no +T1499,Endpoint Denial of Service,Impact,no +T1497,Virtualization/Sandbox Evasion,Defense Evasion|Discovery,The White Company|FIN7 +T1498,Network Denial of Service,Impact,no +T1496,Resource Hijacking,Impact,APT41|Lazarus Group +T1495,Firmware Corruption,Impact,no +T1494,Runtime Data Manipulation,Impact,APT38 +T1493,Transmitted Data Manipulation,Impact,APT38 +T1492,Stored Data Manipulation,Impact,FIN4|APT38 +T1491,Defacement,Impact,no +T1490,Inhibit System Recovery,Impact,no +T1489,Service Stop,Impact,Lazarus Group +T1488,Disk Content Wipe,Impact,Lazarus Group +T1487,Disk Structure Wipe,Impact,Lazarus Group|APT38|APT37 +T1486,Data Encrypted for Impact,Impact,APT41|TA505|APT38 +T1485,Data Destruction,Impact,Lazarus Group|APT38 +T1484,Group Policy Modification,Defense Evasion,no +T1483,Domain Generation Algorithms,Command And Control,APT41 +T1482,Domain Trust Discovery,Discovery,no +T1480,Execution Guardrails,Defense Evasion,APT33|Equation +T1223,Compiled HTML File,Defense Evasion|Execution,APT41|Silence|Lazarus Group|Dark Caracal|OilRig +T1222,File and Directory Permissions Modification,Defense Evasion,APT32 +T1220,XSL Script Processing,Defense Evasion|Execution,Cobalt Group +T1221,Template Injection,Defense Evasion,APT28|Tropic Trooper|Dragonfly 2.0|DarkHydrus +T1197,BITS Jobs,Defense Evasion|Persistence,Leviathan +T1217,Browser Bookmark Discovery,Discovery,no +T1191,CMSTP,Defense Evasion|Execution,Cobalt Group|MuddyWater +T1196,Control Panel Items,Defense Evasion|Execution,no +T1214,Credentials in Registry,Credential Access,Soft Cell +T1207,DCShadow,Defense Evasion,no +T1213,Data from Information Repositories,Collection,Ke3chang|APT28 +T1212,Exploitation for Credential Access,Credential Access,no +T1190,Exploit Public-Facing Application,Initial Access,Soft Cell|Night Dragon|Axiom +T1210,Exploitation of Remote Services,Lateral Movement,Threat Group-3390|APT28 +T1189,Drive-by Compromise,Initial Access,Darkhotel|APT38|Lazarus Group|Dragonfly 2.0|Leafminer|BRONZE BUTLER|Dark Caracal|Threat Group-3390|APT32|APT19|Elderwood|Patchwork|APT37|PLATINUM +T1211,Exploitation for Defense Evasion,Defense Evasion,APT28 +T1203,Exploitation for Client Execution,Execution,APT41|admin@338|Threat Group-3390|APT12|The White Company|APT33|APT32|APT28|Tropic Trooper|BRONZE BUTLER|Lazarus Group|Cobalt Group|APT37|APT29|Patchwork|Leviathan|Elderwood|TA459 +T1215,Kernel Modules and Extensions,Persistence,no +T1200,Hardware Additions,Initial Access,no +T1208,Kerberoasting,Credential Access,no +T1202,Indirect Command Execution,Defense Evasion,no +T1201,Password Policy Discovery,Discovery,OilRig +T1205,Port Knocking,Defense Evasion|Persistence|Command And Control,no +T1198,SIP and Trust Provider Hijacking,Defense Evasion|Persistence,no +T1218,Signed Binary Proxy Execution,Defense Evasion|Execution,TA505|Rancor|Cobalt Group +T1194,Spearphishing via Service,Initial Access,FIN6|OilRig|Dark Caracal|Magic Hound +T1192,Spearphishing Link,Initial Access,Machete|Kimsuky|TA505|Stolen Pencil|APT39|FIN4|APT32|Night Dragon|APT28|Turla|Cobalt Group|Dragonfly 2.0|OilRig|APT33|Elderwood|Patchwork|APT29|Leviathan|Magic Hound|FIN8 +T1195,Supply Chain Compromise,Initial Access,APT41|Elderwood +T1219,Remote Access Tools,Command And Control,Kimsuky|Night Dragon|Thrip|Cobalt Group|Carbanak +T1206,Sudo Caching,Privilege Escalation,no +T1199,Trusted Relationship,Initial Access,APT28|menuPass +T1216,Signed Script Proxy Execution,Defense Evasion|Execution,APT32 +T1193,Spearphishing Attachment,Initial Access,APT41|Machete|admin@338|Kimsuky|APT12|TA505|Silence|The White Company|APT39|FIN4|Darkhotel|Gallmaker|Tropic Trooper|Turla|Gorgon Group|Rancor|DarkHydrus|Lazarus Group|OilRig|Cobalt Group|FIN7|BRONZE BUTLER|APT19|Dragonfly 2.0|APT32|FIN8|MuddyWater|APT28|TA459|Elderwood|APT37|APT29|Patchwork|Leviathan|Magic Hound|menuPass|PLATINUM +T1209,Time Providers,Persistence,no +T1204,User Execution,Execution,Machete|admin@338|APT12|TA505|Silence|The White Company|APT39|FIN4|Night Dragon|Darkhotel|Gallmaker|Dragonfly 2.0|APT33|APT19|BRONZE BUTLER|Dark Caracal|Cobalt Group|FIN7|DarkHydrus|Turla|Gorgon Group|Patchwork|OilRig|Lazarus Group|APT32|MuddyWater|Rancor|APT37|APT28|APT29|menuPass|FIN8|TA459|Elderwood|Magic Hound|Leviathan|PLATINUM +T1182,AppCert DLLs,Persistence|Privilege Escalation,Honeybee +T1176,Browser Extensions,Persistence,Kimsuky|Stolen Pencil +T1175,Component Object Model and Distributed COM,Lateral Movement|Execution,MuddyWater +T1172,Domain Fronting,Command And Control,APT29 +T1181,Extra Window Memory Injection,Defense Evasion|Privilege Escalation,no +T1173,Dynamic Data Exchange,Execution,TA505|MuddyWater|Gallmaker|Patchwork|Cobalt Group|APT37|APT28|FIN7 +T1187,Forced Authentication,Credential Access,DarkHydrus|Dragonfly 2.0 +T1179,Hooking,Persistence|Privilege Escalation|Credential Access,PLATINUM +T1177,LSASS Driver,Execution|Persistence,no +T1188,Multi-hop Proxy,Command And Control,FIN4|APT29 +T1183,Image File Execution Options Injection,Privilege Escalation|Persistence|Defense Evasion,TEMP.Veles +T1171,LLMNR/NBT-NS Poisoning and Relay,Credential Access,no +T1185,Man in the Browser,Collection,no +T1170,Mshta,Defense Evasion|Execution,Kimsuky|APT32|MuddyWater|FIN7 +T1180,Screensaver,Persistence,no +T1174,Password Filter DLL,Credential Access,no +T1184,SSH Hijacking,Lateral Movement,no +T1178,SID-History Injection,Privilege Escalation,no +T1186,Process Doppelgänging,Defense Evasion,no +T1156,.bash_profile and .bashrc,Persistence,no +T1134,Access Token Manipulation,Defense Evasion|Privilege Escalation,Turla|Lazarus Group|APT28 +T1155,AppleScript,Execution|Lateral Movement,no +T1138,Application Shimming,Persistence|Privilege Escalation,FIN7 +T1139,Bash History,Credential Access,no +T1146,Clear Command History,Defense Evasion,APT41 +T1136,Create Account,Persistence,APT41|Soft Cell|Dragonfly 2.0|Leafminer|APT3 +T1140,Deobfuscate/Decode Files or Information,Defense Evasion,Turla|WIRTE|Darkhotel|Tropic Trooper|Honeybee|menuPass|Gorgon Group|Threat Group-3390|APT19|Leviathan|MuddyWater|APT28|OilRig|BRONZE BUTLER +T1157,Dylib Hijacking,Persistence|Privilege Escalation,no +T1148,HISTCONTROL,Defense Evasion,no +T1147,Hidden Users,Defense Evasion,no +T1143,Hidden Window,Defense Evasion,Gorgon Group|Deep Panda|DarkHydrus|CopyKittens|APT19|APT32|APT28|APT3|Magic Hound +T1141,Input Prompt,Credential Access,FIN4 +T1149,LC_MAIN Hijacking,Defense Evasion,no +T1152,Launchctl,Defense Evasion|Execution|Persistence,no +T1162,Login Item,Persistence,no +T1137,Office Application Startup,Persistence,APT32|APT28 +T1144,Gatekeeper Bypass,Defense Evasion,no +T1158,Hidden Files and Directories,Defense Evasion|Persistence,APT32|Tropic Trooper|APT28|Lazarus Group +T1161,LC_LOAD_DYLIB Addition,Persistence,no +T1168,Local Job Scheduling,Persistence|Execution,no +T1160,Launch Daemon,Persistence|Privilege Escalation,no +T1142,Keychain,Credential Access,no +T1159,Launch Agent,Persistence,no +T1163,Rc.common,Persistence,no +T1135,Network Share Discovery,Discovery,APT41|Tropic Trooper|APT1|Dragonfly 2.0|Sowbug +T1151,Space after Filename,Defense Evasion|Execution,no +T1150,Plist Modification,Defense Evasion|Persistence|Privilege Escalation,no +T1145,Private Keys,Credential Access,no +T1167,Securityd Memory,Credential Access,no +T1166,Setuid and Setgid,Privilege Escalation|Persistence,no +T1153,Source,Execution,no +T1164,Re-opened Applications,Persistence,no +T1154,Trap,Execution|Persistence,no +T1165,Startup Items,Persistence|Privilege Escalation,no +T1169,Sudo,Privilege Escalation,no +T1133,External Remote Services,Persistence|Initial Access,APT41|Soft Cell|TEMP.Veles|Night Dragon|OilRig|Ke3chang|Dragonfly 2.0|FIN5|Threat Group-3390|APT18 +T1132,Data Encoding,Command And Control,APT33|APT19|Lazarus Group|BRONZE BUTLER|Patchwork +T1131,Authentication Package,Persistence,no +T1130,Install Root Certificate,Defense Evasion,no +T1129,Execution through Module Load,Execution,no +T1128,Netsh Helper DLL,Persistence,no +T1127,Trusted Developer Utilities,Defense Evasion|Execution,no +T1126,Network Share Connection Removal,Defense Evasion,Threat Group-3390 +T1125,Video Capture,Collection,Silence|FIN7 +T1124,System Time Discovery,Discovery,The White Company|Lazarus Group|BRONZE BUTLER|Turla +T1123,Audio Capture,Collection,APT37 +T1122,Component Object Model Hijacking,Defense Evasion|Persistence,APT28 +T1121,Regsvcs/Regasm,Defense Evasion|Execution,no +T1120,Peripheral Device Discovery,Discovery,APT37|Gamaredon Group|Equation|APT28 +T1119,Automated Collection,Collection,APT1|APT28|Patchwork|OilRig|FIN5|Threat Group-3390|FIN6 +T1118,InstallUtil,Defense Evasion|Execution,no +T1117,Regsvr32,Defense Evasion|Execution,WIRTE|APT19|Cobalt Group|Leviathan|APT32|Deep Panda +T1116,Code Signing,Defense Evasion,APT41|FIN6|TA505|FIN7|Honeybee|APT37|Leviathan|CopyKittens|Winnti Group|Suckfly|Molerats|Darkhotel +T1115,Clipboard Data,Collection,APT38 +T1114,Email Collection,Collection,FIN4|APT28|Dragonfly 2.0|Magic Hound|Ke3chang|Leafminer|APT1 +T1113,Screen Capture,Collection,Silence|MuddyWater|OilRig|Dragonfly 2.0|Dark Caracal|FIN7|BRONZE BUTLER|Magic Hound|Group5|APT28 +T1112,Modify Registry,Defense Evasion,APT41|Turla|APT32|APT38|Dragonfly 2.0|APT19|Patchwork|Threat Group-3390|Honeybee|Gorgon Group|FIN8 +T1111,Two-Factor Authentication Interception,Credential Access,no +T1110,Brute Force,Credential Access,APT41|APT33|Leafminer|OilRig|Dragonfly 2.0|APT3|Lazarus Group|Turla +T1109,Component Firmware,Defense Evasion|Persistence,Equation +T1108,Redundant Access,Defense Evasion|Persistence,Stolen Pencil|Cobalt Group|Leafminer|APT3|FIN5|OilRig|Threat Group-3390 +T1107,File Deletion,Defense Evasion,APT41|Kimsuky|Silence|The White Company|TEMP.Veles|APT32|APT38|Honeybee|Patchwork|Cobalt Group|Dragonfly 2.0|menuPass|FIN8|OilRig|FIN5|BRONZE BUTLER|Magic Hound|APT3|FIN10|APT28|Threat Group-3390|Group5|Lazarus Group|APT18|APT29 +T1106,Execution through API,Execution,Turla|Silence|APT37|Gorgon Group +T1105,Remote File Copy,Command And Control|Lateral Movement,Soft Cell|TA505|WIRTE|APT33|MuddyWater|APT18|APT38|Turla|Rancor|Cobalt Group|Gorgon Group|Dragonfly 2.0|OilRig|APT37|FIN8|Leviathan|PLATINUM|Elderwood|Magic Hound|APT3|BRONZE BUTLER|APT32|FIN7|menuPass|FIN10|Gamaredon Group|Patchwork|Lazarus Group|Threat Group-3390|APT28 +T1104,Multi-Stage Channels,Command And Control,MuddyWater|APT3 +T1103,AppInit DLLs,Persistence|Privilege Escalation,no +T1102,Web Service,Command And Control|Defense Evasion,APT41|APT12|FIN6|Turla|FIN7|BRONZE BUTLER|Leviathan|APT37|Magic Hound|RTM|Patchwork|Carbanak +T1101,Security Support Provider,Persistence,no +T1100,Web Shell,Persistence|Privilege Escalation,Soft Cell|Threat Group-3390|TEMP.Veles|Leviathan|APT39|Dragonfly 2.0|APT32|OilRig|Deep Panda +T1099,Timestomp,Defense Evasion,TEMP.Veles|APT32|Lazarus Group|APT28 +T1098,Account Manipulation,Credential Access|Persistence,Magic Hound|Dragonfly 2.0|APT3|Lazarus Group +T1097,Pass the Ticket,Lateral Movement,APT32|Ke3chang|BRONZE BUTLER|APT29 +T1096,NTFS File Attributes,Defense Evasion,APT32 +T1095,Standard Non-Application Layer Protocol,Command And Control,APT29|PLATINUM|APT3 +T1094,Custom Command and Control Protocol,Command And Control,PLATINUM|APT37|OilRig|APT32 +T1093,Process Hollowing,Defense Evasion,menuPass|Gorgon Group|Patchwork +T1092,Communication Through Removable Media,Command And Control,APT28 +T1091,Replication Through Removable Media,Lateral Movement|Initial Access,Darkhotel|APT28 +T1090,Connection Proxy,Command And Control|Defense Evasion,APT41|Soft Cell|Turla|APT39|MuddyWater|APT3|Lazarus Group|menuPass|Strider|APT28 +T1089,Disabling Security Tools,Defense Evasion,Kimsuky|Turla|Night Dragon|Dragonfly 2.0|Gorgon Group|Threat Group-3390|Lazarus Group|Putter Panda|Carbanak +T1088,Bypass User Account Control,Defense Evasion|Privilege Escalation,APT37|MuddyWater|Honeybee|Threat Group-3390|Cobalt Group|BRONZE BUTLER|Patchwork|APT29 +T1087,Account Discovery,Discovery,APT32|APT1|Dragonfly 2.0|BRONZE BUTLER|OilRig|Threat Group-3390|menuPass|FIN6|Poseidon Group|APT3|admin@338|Ke3chang +T1086,PowerShell,Execution,APT41|Kimsuky|Soft Cell|TA505|WIRTE|TEMP.Veles|APT33|Gallmaker|Turla|APT19|DarkHydrus|APT28|Thrip|Dragonfly 2.0|Cobalt Group|Gorgon Group|Leviathan|TA459|FIN8|MuddyWater|CopyKittens|OilRig|Magic Hound|BRONZE BUTLER|APT32|FIN10|FIN7|Threat Group-3390|menuPass|Patchwork|Stealth Falcon|FIN6|Poseidon Group|APT3|APT29|Deep Panda +T1085,Rundll32,Defense Evasion|Execution,TA505|MuddyWater|APT29|APT19|CopyKittens|APT3|Carbanak|APT28 +T1084,Windows Management Instrumentation Event Subscription,Persistence,Turla|Leviathan|APT29 +T1083,File and Directory Discovery,Discovery,Kimsuky|APT32|MuddyWater|APT18|Leafminer|Dragonfly 2.0|Dark Caracal|Honeybee|Magic Hound|APT3|Sowbug|BRONZE BUTLER|APT28|Patchwork|Lazarus Group|Dust Storm|admin@338|Turla|Ke3chang +T1082,System Information Discovery,Discovery,Kimsuky|Tropic Trooper|Darkhotel|MuddyWater|APT18|APT37|APT19|Honeybee|APT32|OilRig|Magic Hound|APT3|Sowbug|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|admin@338|Turla|Ke3chang +T1081,Credentials in Files,Credential Access,OilRig|Kimsuky|Turla|TA505|Stolen Pencil|MuddyWater|APT3 +T1080,Taint Shared Content,Lateral Movement,Darkhotel +T1079,Multilayer Encryption,Command And Control,no +T1078,Valid Accounts,Defense Evasion|Persistence|Privilege Escalation|Initial Access,APT41|Soft Cell|TEMP.Veles|APT39|Stolen Pencil|FIN4|Night Dragon|Dragonfly 2.0|FIN8|APT33|Leviathan|APT3|FIN5|OilRig|menuPass|APT28|FIN10|APT32|Suckfly|FIN6|Threat Group-1314|Threat Group-3390|APT18|PittyTiger|Carbanak +T1077,Windows Admin Shares,Lateral Movement,APT32|Orangeworm|FIN8|APT3|Lazarus Group|Threat Group-1314|Turla|Deep Panda|Ke3chang +T1076,Remote Desktop Protocol,Lateral Movement,APT41|TEMP.Veles|Leviathan|APT39|Stolen Pencil|Cobalt Group|Dragonfly 2.0|FIN8|APT3|OilRig|menuPass|FIN10|Patchwork|FIN6|Lazarus Group|APT1|Axiom +T1075,Pass the Hash,Lateral Movement,Soft Cell|APT32|Night Dragon|APT28|APT1 +T1074,Data Staged,Collection,Machete|Soft Cell|TEMP.Veles|Night Dragon|Honeybee|Patchwork|Dragonfly 2.0|Leviathan|FIN8|APT3|FIN5|menuPass|FIN6|Lazarus Group|Threat Group-3390|APT28 +T1073,DLL Side-Loading,Defense Evasion,APT41|Soft Cell|Tropic Trooper|Patchwork|APT19|APT32|APT3|menuPass|Threat Group-3390 +T1072,Third-party Software,Execution|Lateral Movement,Threat Group-1314 +T1071,Standard Application Layer Protocol,Command And Control,APT41|Machete|WIRTE|APT33|FIN4|Night Dragon|APT18|SilverTerrier|APT38|Dragonfly 2.0|APT19|Cobalt Group|FIN7|Threat Group-3390|APT37|Rancor|Orangeworm|Turla|Honeybee|Ke3chang|Dark Caracal|Lazarus Group|BRONZE BUTLER|OilRig|APT32|Magic Hound|Gamaredon Group|Stealth Falcon|FIN6|APT28 +T1070,Indicator Removal on Host,Defense Evasion,APT41|APT29|APT38|Dragonfly 2.0|APT32|FIN8|FIN5|APT28 +T1069,Permission Groups Discovery,Discovery,FIN6|Dragonfly 2.0|OilRig|APT3|admin@338|Ke3chang +T1068,Exploitation for Privilege Escalation,Privilege Escalation,APT33|Cobalt Group|PLATINUM|FIN8|APT32|Threat Group-3390|FIN6|APT28 +T1067,Bootkit,Persistence,APT41|Lazarus Group|APT28 +T1066,Indicator Removal from Tools,Defense Evasion,Soft Cell|TEMP.Veles|Patchwork|APT3|Turla|OilRig|Deep Panda +T1065,Uncommonly Used Port,Command And Control,TEMP.Veles|APT33|APT32|Gorgon Group|Magic Hound|Group5|Lazarus Group|APT3 +T1064,Scripting,Defense Evasion|Execution,Machete|Turla|TA505|Silence|WIRTE|APT39|FIN4|APT32|Darkhotel|Gallmaker|Dark Caracal|Lazarus Group|menuPass|APT19|Dragonfly 2.0|Leafminer|Rancor|Honeybee|Cobalt Group|APT37|Ke3chang|FIN7|Gorgon Group|Patchwork|MuddyWater|Leviathan|FIN8|TA459|APT28|Magic Hound|OilRig|BRONZE BUTLER|FIN5|FIN10|Gamaredon Group|Stealth Falcon|FIN6|APT3|APT29|Deep Panda|APT1 +T1063,Security Software Discovery,Discovery,The White Company|Cobalt Group|Darkhotel|MuddyWater|Tropic Trooper|FIN8|Patchwork|Naikon +T1062,Hypervisor,Persistence,no +T1061,Graphical User Interface,Execution,APT3 +T1060,Registry Run Keys / Startup Folder,Persistence,APT41|Machete|Kimsuky|APT33|APT39|APT32|APT18|Turla|APT19|Cobalt Group|Honeybee|Dark Caracal|Ke3chang|Threat Group-3390|Dragonfly 2.0|Gorgon Group|MuddyWater|APT37|Leviathan|BRONZE BUTLER|APT3|Magic Hound|FIN10|FIN7|Patchwork|FIN6|Lazarus Group|Putter Panda|APT29|Darkhotel +T1059,Command-Line Interface,Execution,APT41|Soft Cell|Turla|Silence|APT32|Cobalt Group|MuddyWater|APT18|APT38|Dragonfly 2.0|Gorgon Group|APT28|FIN7|Rancor|Honeybee|Leviathan|APT37|FIN8|Magic Hound|Sowbug|OilRig|BRONZE BUTLER|menuPass|Threat Group-3390|Suckfly|Patchwork|Lazarus Group|Threat Group-1314|APT3|admin@338|APT1|Ke3chang +T1058,Service Registry Permissions Weakness,Persistence|Privilege Escalation,no +T1057,Process Discovery,Discovery,Darkhotel|MuddyWater|APT1|APT38|Tropic Trooper|APT37|Honeybee|OilRig|APT3|Magic Hound|APT28|Winnti Group|Stealth Falcon|Poseidon Group|Lazarus Group|Molerats|Turla|Deep Panda|Ke3chang +T1056,Input Capture,Collection|Credential Access,APT41|Kimsuky|menuPass|Stolen Pencil|FIN4|APT38|OilRig|Ke3chang|PLATINUM|Sowbug|Magic Hound|Group5|Lazarus Group|Threat Group-3390|APT3|Darkhotel|APT28 +T1055,Process Injection,Defense Evasion|Privilege Escalation,APT41|Kimsuky|Tropic Trooper|Gorgon Group|Turla|Threat Group-3390|APT37|Cobalt Group|Honeybee|Lazarus Group|PLATINUM|Putter Panda +T1054,Indicator Blocking,Defense Evasion,no +T1053,Scheduled Task,Execution|Persistence|Privilege Escalation,APT41|Machete|Soft Cell|Silence|TEMP.Veles|APT33|APT39|Cobalt Group|Dragonfly 2.0|Patchwork|OilRig|Rancor|FIN8|BRONZE BUTLER|menuPass|FIN10|APT32|FIN7|Stealth Falcon|FIN6|Threat Group-3390|APT18|APT3|APT29 +T1052,Exfiltration Over Physical Medium,Exfiltration,no +T1051,Shared Webroot,Lateral Movement,no +T1050,New Service,Persistence|Privilege Escalation,Kimsuky|Tropic Trooper|Cobalt Group|Ke3chang|FIN7|APT32|Threat Group-3390|APT3|Lazarus Group|Carbanak +T1049,System Network Connections Discovery,Discovery,APT41|APT38|Soft Cell|APT32|APT1|OilRig|APT3|menuPass|Threat Group-3390|Poseidon Group|admin@338|Turla|Ke3chang +T1048,Exfiltration Over Alternative Protocol,Exfiltration,Turla|APT33|Thrip|FIN8|OilRig|Lazarus Group +T1047,Windows Management Instrumentation,Execution,APT41|FIN6|Soft Cell|APT32|MuddyWater|OilRig|Threat Group-3390|Leviathan|FIN8|menuPass|Stealth Falcon|Lazarus Group|APT29|Deep Panda +T1046,Network Service Scanning,Discovery,APT41|Tropic Trooper|APT39|APT32|Leafminer|Cobalt Group|OilRig|menuPass|Suckfly|FIN6|Threat Group-3390 +T1045,Software Packing,Defense Evasion,Soft Cell|The White Company|APT39|APT38|Dark Caracal|Elderwood|APT3|Group5|Patchwork|APT29|Night Dragon +T1044,File System Permissions Weakness,Persistence|Privilege Escalation,no +T1043,Commonly Used Port,Command And Control,Machete|OilRig|APT28|TEMP.Veles|APT33|APT32|Night Dragon|APT29|APT18|Tropic Trooper|APT19|FIN7|Dragonfly 2.0|FIN8|APT37|Magic Hound|APT3|Lazarus Group|Threat Group-3390 +T1042,Change Default File Association,Persistence,Kimsuky +T1041,Exfiltration Over Command and Control Channel,Exfiltration,Kimsuky|Soft Cell|APT32|APT3|Gamaredon Group|Stealth Falcon|Lazarus Group|Ke3chang +T1040,Network Sniffing,Credential Access|Discovery,APT33|Stolen Pencil|APT28 +T1039,Data from Network Shared Drive,Collection,Sowbug|BRONZE BUTLER|menuPass +T1038,DLL Search Order Hijacking,Persistence|Privilege Escalation|Defense Evasion,Threat Group-3390|menuPass +T1037,Logon Scripts,Lateral Movement|Persistence,Cobalt Group|APT28 +T1036,Masquerading,Defense Evasion,APT41|Soft Cell|PLATINUM|Ke3chang|Scarlet Mimic|menuPass|FIN6|TEMP.Veles|Dragonfly 2.0|MuddyWater|BRONZE BUTLER|Sowbug|FIN7|APT32|Patchwork|Poseidon Group|admin@338|Carbanak|APT1 +T1035,Service Execution,Execution,Silence|FIN6|APT32|Honeybee|Ke3chang +T1034,Path Interception,Persistence|Privilege Escalation,no +T1033,System Owner/User Discovery,Discovery,APT41|Soft Cell|Tropic Trooper|APT39|MuddyWater|APT32|APT37|APT19|Dragonfly 2.0|Magic Hound|OilRig|FIN10|Gamaredon Group|Patchwork|Stealth Falcon|Lazarus Group|APT3 +T1032,Standard Cryptographic Protocol,Command And Control,Machete|APT33|Tropic Trooper|Cobalt Group|OilRig|FIN8|BRONZE BUTLER|Stealth Falcon|FIN6|Lazarus Group|Taidoor +T1031,Modify Existing Service,Persistence,APT41|APT32|Honeybee|APT19 +T1030,Data Transfer Size Limits,Exfiltration,Threat Group-3390 +T1029,Scheduled Transfer,Exfiltration,no +T1028,Windows Remote Management,Execution|Lateral Movement,Threat Group-3390 +T1027,Obfuscated Files or Information,Defense Evasion,Machete|Soft Cell|Turla|TA505|Silence|APT33|Night Dragon|Darkhotel|Gallmaker|APT29|APT18|Tropic Trooper|menuPass|Patchwork|Cobalt Group|APT37|Threat Group-3390|Leafminer|Honeybee|Dark Caracal|APT19|FIN8|BlackOasis|Elderwood|Leviathan|MuddyWater|FIN7|Magic Hound|APT3|OilRig|APT32|Group5|Dust Storm|Lazarus Group|Putter Panda|APT28 +T1026,Multiband Communication,Command And Control,Lazarus Group +T1025,Data from Removable Media,Collection,Machete|Turla|Gamaredon Group|APT28 +T1024,Custom Cryptographic Protocol,Command And Control,APT28|BRONZE BUTLER|Lazarus Group +T1023,Shortcut Modification,Persistence,APT39|Darkhotel|APT29|Gorgon Group|FIN7|Dragonfly 2.0|Leviathan|Lazarus Group +T1022,Data Encrypted,Exfiltration,Kimsuky|Soft Cell|Turla|menuPass|APT32|Patchwork|Honeybee|CopyKittens|BRONZE BUTLER|FIN6|Lazarus Group|Threat Group-3390|Ke3chang +T1021,Remote Services,Lateral Movement,TEMP.Veles|Leviathan|APT39|OilRig|menuPass|GCMAN +T1020,Automated Exfiltration,Exfiltration,Honeybee +T1019,System Firmware,Persistence,no +T1018,Remote System Discovery,Discovery,Soft Cell|APT32|Threat Group-3390|Dragonfly 2.0|Deep Panda|Ke3chang|Leafminer|FIN8|APT3|FIN5|BRONZE BUTLER|menuPass|FIN6|Turla +T1017,Application Deployment Software,Lateral Movement,APT32 +T1016,System Network Configuration Discovery,Discovery,APT41|Soft Cell|APT39|APT32|Darkhotel|MuddyWater|APT1|APT19|Dragonfly 2.0|OilRig|Magic Hound|menuPass|Threat Group-3390|Stealth Falcon|Lazarus Group|APT3|Naikon|admin@338|Turla|Ke3chang +T1015,Accessibility Features,Persistence|Privilege Escalation,APT41|APT3|APT29|Deep Panda|Axiom +T1014,Rootkit,Defense Evasion,APT41|APT28|Winnti Group +T1013,Port Monitors,Persistence|Privilege Escalation,no +T1012,Query Registry,Discovery,APT32|Dragonfly 2.0|Threat Group-3390|OilRig|Stealth Falcon|Lazarus Group|Turla +T1011,Exfiltration Over Other Network Medium,Exfiltration,no +T1010,Application Window Discovery,Discovery,Lazarus Group +T1009,Binary Padding,Defense Evasion,Patchwork|APT32|Leviathan|BRONZE BUTLER|Moafee +T1008,Fallback Channels,Command And Control,APT41|OilRig|Lazarus Group +T1007,System Service Discovery,Discovery,APT1|OilRig|Poseidon Group|admin@338|Turla|Ke3chang +T1006,File System Logical Offsets,Defense Evasion,no +T1005,Data from Local System,Collection,Kimsuky|Soft Cell|Turla|menuPass|Dark Caracal|Dragonfly 2.0|Honeybee|APT37|APT28|APT3|BRONZE BUTLER|Patchwork|Stealth Falcon|Lazarus Group|Dust Storm|Threat Group-3390|APT1|Ke3chang +T1004,Winlogon Helper DLL,Persistence,Tropic Trooper|Turla +T1003,Credential Dumping,Credential Access,APT41|Soft Cell|TEMP.Veles|APT33|Leviathan|APT39|Stolen Pencil|APT32|Night Dragon|Dragonfly 2.0|Leafminer|Lazarus Group|Magic Hound|APT37|MuddyWater|PLATINUM|FIN8|Sowbug|BRONZE BUTLER|FIN5|OilRig|menuPass|Strider|Patchwork|Stealth Falcon|Suckfly|FIN6|Poseidon Group|Threat Group-3390|APT3|Molerats|APT28|APT1|Ke3chang|Cleaver|Axiom +T1002,Data Compressed,Exfiltration,APT41|Soft Cell|Gallmaker|APT33|APT32|APT39|MuddyWater|Honeybee|Magic Hound|APT28|Dragonfly 2.0|FIN8|BRONZE BUTLER|CopyKittens|Sowbug|APT3|menuPass|FIN6|Lazarus Group|Threat Group-3390|APT1|Ke3chang +T1001,Data Obfuscation,Command And Control,APT28|Axiom diff --git a/package/lookups/prohibited_apps_launching_cmd.csv b/package/lookups/prohibited_apps_launching_cmd.csv index 01edda483b..5e4a2cf651 100644 --- a/package/lookups/prohibited_apps_launching_cmd.csv +++ b/package/lookups/prohibited_apps_launching_cmd.csv @@ -14,3 +14,4 @@ firefox.exe,prohibited java.exe,prohibited powershell.exe,prohibited mshta.exe, prohibited +zoom.exe,prohibitied diff --git a/requirements.txt b/requirements.txt index 021ea66934..e852aa2697 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,7 +7,7 @@ cfgv==3.1.0 chardet==3.0.4 configparser==5.0.0 contextlib2==0.6.0.post1 -identify==1.4.16 +identify==1.4.19 idna==2.9 importlib-metadata==1.6.0 importlib-resources==1.5.0 @@ -15,7 +15,7 @@ Jinja2==2.11.2 jsonschema==3.2.0 MarkupSafe==1.1.1 more-itertools==8.3.0 -nodeenv==1.3.5 +nodeenv==1.4.0 pathlib2==2.3.5 pre-commit==2.4.0 pyrsistent==0.16.0 diff --git a/response_tasks/get_authentication_logs_for_endpoint.yml b/response_tasks/get_authentication_logs_for_endpoint.yml index 7c8c273710..1539cbd67e 100644 --- a/response_tasks/get_authentication_logs_for_endpoint.yml +++ b/response_tasks/get_authentication_logs_for_endpoint.yml @@ -54,3 +54,4 @@ tags: - Windows Persistence Techniques - Windows Privilege Escalation - Windows Service Abuse + - Suspicious Zoom Child Processes diff --git a/response_tasks/get_notable_info.yml b/response_tasks/get_notable_info.yml index b51d7d48b7..90491c4aac 100644 --- a/response_tasks/get_notable_info.yml +++ b/response_tasks/get_notable_info.yml @@ -67,3 +67,5 @@ tags: - Windows Persistence Techniques - Windows Privilege Escalation - Windows Service Abuse + - Kubernetes Sensitive Role Activity + - Kubernetes Sensitive Object Access Activity diff --git a/response_tasks/get_process_file_activity.yml b/response_tasks/get_process_file_activity.yml index 541db0c677..8442bbdfd1 100644 --- a/response_tasks/get_process_file_activity.yml +++ b/response_tasks/get_process_file_activity.yml @@ -18,3 +18,4 @@ search: '| tstats `security_content_summariesonly` values(Filesystem.file_name) tags: analytics_story: - DHS Report TA18-074A + - Suspicious Zoom Child Processes diff --git a/response_tasks/get_process_registry_activity.yml b/response_tasks/get_process_registry_activity.yml index 71b74cb8ad..c522b90708 100644 --- a/response_tasks/get_process_registry_activity.yml +++ b/response_tasks/get_process_registry_activity.yml @@ -18,3 +18,4 @@ search: '| tstats `security_content_summariesonly` values(Registry.registry_key_ tags: analytics_story: - DHS Report TA18-074A + - Suspicious Zoom Child Processes diff --git a/spec/lookups.spec.json b/spec/lookups.spec.json index 0c85720259..4d59673a35 100644 --- a/spec/lookups.spec.json +++ b/spec/lookups.spec.json @@ -47,6 +47,13 @@ ], "type": "string" }, + "fields_list": { + "description": "A comma and space separated list of field names", + "examples": [ + "_key, dest, process_name" + ], + "type": "string" + }, "filename": { "description": "The name of the file to use for this lookup", "examples": [ @@ -54,6 +61,13 @@ ], "type": "string" }, + "filter": { + "description": "Use this attribute to improve search performance when working with significantly large KV", + "examples": [ + "dest=\"SPLK_*\"" + ], + "type": "string" + }, "match_type": { "description": "A comma and space-delimited list of () specification to allow for non-exact matching", "examples": [ diff --git a/stories/kubernetes_scanning_activity.yml b/stories/kubernetes_scanning_activity.yml index be38a9c8a1..4a9f4de4f7 100644 --- a/stories/kubernetes_scanning_activity.yml +++ b/stories/kubernetes_scanning_activity.yml @@ -1,20 +1,15 @@ -name: Kubernetes Scanning Activity +author: "Rod Soto, Splunk" +date: "2020-04-15" +description: "This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names." id: a9ef59cf-e981-4e66-9eef-bb049f695c09 -version: 1 -date: '2020-04-15' -description: This story addresses detection against Kubernetes cluster fingerprint - scan and attack by providing information on items such as source ip, user agent, - cluster names. -narrative: Kubernetes is the most used container orchestration platform, this orchestration - platform contains sensitve information and management priviledges of production - workloads, microservices and applications. These searches allow operator to detect - suspicious unauthenticated requests from the internet to kubernetes cluster. -author: Rod Soto, Splunk -type: ESCU +name: "Kubernetes Scanning Activity" +narrative: "Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster." references: -- https://github.com/splunk/cloud-datamodel-security-research + - "https://github.com/splunk/cloud-datamodel-security-research" tags: - analytics_story: Kubernetes Scanning Activity - usecase: Security Monitoring + analytics_story: "Kubernetes Scanning Activity" category: - - Cloud Security + - "Cloud Security" + usecase: "Security Monitoring" +type: ESCU +version: 1 diff --git a/stories/kubernetes_sensitive_object_access_activity.yml b/stories/kubernetes_sensitive_object_access_activity.yml new file mode 100644 index 0000000000..1ff38bbbb2 --- /dev/null +++ b/stories/kubernetes_sensitive_object_access_activity.yml @@ -0,0 +1,15 @@ +author: "Rod Soto, Splunk" +date: "2020-05-20" +description: "This story addresses detection and response of accounts acccesing Kubernetes cluster sensitive objects such as configmaps or secrets providing information on items such as user user, group. object, namespace and authorization reason." +id: 2574e6d9-7254-4751-8925-0447deeec8ea +name: "Kubernetes Sensitive Object Access Activity" +narrative: "Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive objects within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes sensitive objects." +references: + - https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html +tags: + analytics_story: "Kubernetes Sensitive Object Access Activity" + category: + - "Cloud Security" + usecase: "Security Monitoring" +type: ESCU +version: 1 diff --git a/stories/kubernetes_sensitive_role_activity.yml b/stories/kubernetes_sensitive_role_activity.yml new file mode 100644 index 0000000000..739bce3ae7 --- /dev/null +++ b/stories/kubernetes_sensitive_role_activity.yml @@ -0,0 +1,15 @@ +author: "Rod Soto, Splunk" +date: "2020-05-20" +description: "This story addresses detection and response around Sensitive Role usage within a Kubernetes clusters against cluster resources and namespaces." +id: 2574e6d9-7254-4751-8925-0447deeec8ew +name: "Kubernetes Sensitive Role Activity" +narrative: "Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitive roles within its architecture, specifically configmaps and secrets, if accessed by an attacker can lead to further compromise. These searches allow operator to detect suspicious requests against Kubernetes role activities" +references: + - https://www.splunk.com/en_us/blog/security/approaching-kubernetes-security-detecting-kubernetes-scan-with-splunk.html +tags: + analytics_story: "Kubernetes Sensitive Role Activity" + category: + - "Cloud Security" + usecase: "Security Monitoring" +type: ESCU +version: 1 diff --git a/stories/suspicious_zoom_child_processes.yml b/stories/suspicious_zoom_child_processes.yml new file mode 100644 index 0000000000..72d7e859c4 --- /dev/null +++ b/stories/suspicious_zoom_child_processes.yml @@ -0,0 +1,24 @@ +name: Suspicious Zoom Child Processes +id: aa3749a6-49c7-491e-a03f-4eaee5fe0258 +version: 1 +date: '2020-04-13' +description: Attackers are using Zoom as an vector to increase privileges on a sytems. This story detects new + child processes of zoom and provides investigative actions for this detection. +narrative: 'Zoom is a leader in modern enterprise video communications and its usage has + increased dramatically with a large amount of the population under stay-at-home orders + due to the COVID-19 pandemic. With increased usage has come increased scrutiny and + several security flaws have been found with this application on both Windows and macOS + systems.\ + + Current detections focus on finding new child processes of this application on a per host + basis. Investigative searches are included to gather information needed during an investigation.' +author: David Dorsey, Splunk +type: ESCU +references: + - https://blog.rapid7.com/2020/04/02/dispelling-zoom-bugbears-what-you-need-to-know-about-the-latest-zoom-vulnerabilities/ + - https://threatpost.com/two-zoom-zero-day-flaws-uncovered/154337/ +tags: + analytics_story: Suspicious Zoom Child Processes + usecase: Advanced Threat Detection + category: + - Adversary Tactics