diff --git a/.circleci/config.yml b/.circleci/config.yml index 1d9b8aed48..c2c7f57c2e 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -51,19 +51,19 @@ jobs: command: | cd security-content source venv/bin/activate - python bin/validate_manifests.py + python bin/validate_manifests.py --path . - run: name: run manifest to escu command: | cd security-content source venv/bin/activate - python bin/manifest_to_escu.py + python bin/manifest_to_escu.py --path . --output src/default/ - run: name: run manifest to usecase library command: | cd security-content source venv/bin/activate - python bin/manifest_to_usecaselibrary.py + python bin/manifest_to_usecaselibrary.py --path . --output src/default/ - persist_to_workspace: root: security-content/src/default paths: @@ -87,7 +87,7 @@ jobs: fi - attach_workspace: # Must be absolute path or relative path from working_directory - at: ~/repo/updated + at: ~/repo/updated - run: name: store updated analyticstories.conf command: | @@ -101,7 +101,7 @@ jobs: git commit --allow-empty -m "updating src files [ci skip]" # Push quietly to prevent showing the token in log git push https://${GITHUB_TOKEN}@github.com/splunk/security-content.git ${CIRCLE_BRANCH} - tar -czvf content-pack.tar.gz src/* + tar -czvf content-pack.tar.gz src/* - persist_to_workspace: root: security-content/ paths: @@ -111,7 +111,7 @@ jobs: - image: cibuilds/github:0.10 steps: - attach_workspace: - at: ~/repo/updated + at: ~/repo/updated - run: name: publish release on github command: | @@ -143,4 +143,4 @@ workflows: ignore: /.*/ - + diff --git a/bin/manifest_to_escu.py b/bin/manifest_to_escu.py index 07b2012377..592d5d91ae 100644 --- a/bin/manifest_to_escu.py +++ b/bin/manifest_to_escu.py @@ -1,15 +1,14 @@ # Take the manifest files and build files for Enterprise Security Content Updates with markdown syntax + import datetime import glob import json import os +import argparse ALL_UUIDS = [] -MANIFEST_DIRECTORY = "." -OUTPUT_DIRECTORY = 'src/default/' - def markdown(x): markdown = str(x) @@ -368,4 +367,17 @@ def main(): if __name__ == "__main__": + + # grab arguments + parser = argparse.ArgumentParser(description="converts security-content manifests to source files", epilog=""" + This tool converts manifests to the source files to be used by products, specfically Splunk's. + It generates the savesearches.conf, analyticsstories.conf files for ES.""") + parser.add_argument("-p", "--path", required=True, help="path to security-security content repo") + parser.add_argument("-o", "--output", required=True, help="path to the output directory") + + # parse them + args = parser.parse_args() + MANIFEST_DIRECTORY = args.path + OUTPUT_DIRECTORY = args.output + main() diff --git a/bin/spell_check_manifests.py b/bin/spell_check_manifests.py index 659d726e3f..6b09bb9830 100644 --- a/bin/spell_check_manifests.py +++ b/bin/spell_check_manifests.py @@ -4,20 +4,18 @@ Take the manifest files and build files for Enterprise Security import glob import os.path import json -import sys -import traceback +import argparse # HIGH Level Fields -MANIFEST_DIRECTORY = "../manifests" - -#Add these lines: +# Add these lines: import nltk -from nltk.corpus import wordnet as WN from nltk.corpus import stopwords -stop_words_en = set(stopwords.words('english')) import enchant import grammar_check +stop_words_en = set(stopwords.words('english')) + + def check_spelling(sentences): errors = [] sp = enchant.Dict("en_US") @@ -47,6 +45,7 @@ def check_grammar(sentences): return errors + def spell_check_search_manifest(search): ''' Validate that the search has the required fields for savedsearches.conf ''' errors = [] @@ -103,6 +102,7 @@ def spell_check_story_manifest(story): errors.extend(spelling_errors) errors.extend(grammar_errors) + def main(): ''' Open manifest file, validate required fields ''' @@ -149,5 +149,14 @@ def main(): if not errors: print "No errors found" + if __name__ == "__main__": + # grab arguments + parser = argparse.ArgumentParser(description="spell checks the security-contents manifests") + parser.add_argument("-p", "--path", required=True, help="path to security-security content repo") + + # parse them + args = parser.parse_args() + MANIFEST_DIRECTORY = args.path + main() diff --git a/bin/validate_manifests.py b/bin/validate_manifests.py index 1a851c7c67..41e6699dea 100644 --- a/bin/validate_manifests.py +++ b/bin/validate_manifests.py @@ -3,18 +3,13 @@ Take the manifest files and build files for Enterprise Security ''' import glob -from os import path, environ import json import jsonschema import sys +import argparse +from os import path # HIGH Level Fields - -MANIFEST_DIRECTORY = str(environ["CIRCLE_WORKING_DIRECTORY"] + - "/security-content") - -print MANIFEST_DIRECTORY - STORY_CATEGORIES = [ "Abuse", "Adversary Tactics", "Best Practices", "Cloud Security", "Malware", "Vulnerability" @@ -30,6 +25,8 @@ VALID_DATA_MODELS = [ "Authentication", "Certificates", "Change_Analysis", + "Change", + "Malware", "Email", "Identity_Management", "Network_Resolution", @@ -577,4 +574,12 @@ def main(): if __name__ == "__main__": + # grab arguments + parser = argparse.ArgumentParser(description="validates security content manifests", epilog=""" + Validates security manifest for correctness, adhering to spec and other common items.""") + parser.add_argument("-p", "--path", required=True, help="path to security-security content repo") + + # parse them + args = parser.parse_args() + MANIFEST_DIRECTORY = args.path main() diff --git a/escu/searches/contextual_get_risk_for_endpoint.json b/escu/searches/contextual_get_risk_for_endpoint.json index 7127213e54..6b317c181c 100644 --- a/escu/searches/contextual_get_risk_for_endpoint.json +++ b/escu/searches/contextual_get_risk_for_endpoint.json @@ -32,7 +32,7 @@ } ], "search": "| from datamodel:Risk.All_Risk | search risk_object_type=system risk_object={dest} | stats count sum(risk_score) as risk_score values(search_name) min(_time) as firstTime max(_time) as lastTime by risk_object | `ctime(firstTime)` | `ctime(lastTime)`", - "search_description": "For the last 7 days, the search will query the Risk data model in Splunk Enterprise Security and calculate the count, sum of the risk_scores, names of the correlation searches that contributed to create a risk score for a specific endpoint(machine_name) ", + "search_description": "For the last 7 days, the search will query the Risk data model in Splunk Enterprise Security and calculate the count, sum of the risk\\_scores, names of the correlation searches that contributed to create a risk score for a specific endpoint(machine\\_name) ", "search_id": "fdcfb369-1725-4c24-824a-22972d7f0d65", "search_name": "Get Risk Modifiers For Endpoint", "search_type": "contextual", diff --git a/escu/searches/detection_dns_record_changed.json b/escu/searches/detection_dns_record_changed.json index ee129e8dd4..16e3f5bd59 100644 --- a/escu/searches/detection_dns_record_changed.json +++ b/escu/searches/detection_dns_record_changed.json @@ -33,8 +33,8 @@ "Bro" ] }, - "eli5": "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.", - "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\".", + "eli5": "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.", + "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 Investigation\" 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/4.1/playbook/dns-hijack-investigation/).

", "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.", "maintainers": [ { @@ -75,12 +75,22 @@ "name": "Jose Hernandez" } ], + "phantom_playbooks": [ + { + "phantom_server": "automation (hostname)", + "playbook_display_name": "DNS record change investigation", + "playbook_name": "community/dns_hijack_investigation", + "playbook_url": "https://my.phantom.us/4.1/playbook/dns-hijack-investigation/", + "sensitivity": "green", + "severity": "medium" + } + ], "scheduling": { "cron_schedule": "0 * * * *", "earliest_time": "-70m@m", "latest_time": "-10m@m" }, - "search": "| inputlookup discovered_dns_records.csv | rename answer as discovered_answer | join domain[|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src 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+?)(?:$|/)\"] | makemv delim=\" \" answer | makemv delim=\" \" type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | where current_answer!=discovered_answer", + "search": "| inputlookup discovered_dns_records.csv | rename answer as discovered_answer | join domain[|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!=\"unknown\" DNS.answer!=\"\" by DNS.query | rename DNS.query as query | where query!=\"unknown\" | rex field=query \"(?\\w+\\.\\w+?)(?:$|/)\"] | makemv delim=\" \" answer | makemv delim=\" \" type | sort -count | table count,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n)", "search_description": "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.", "search_id": "44d3a43e-dcd5-49f7-8356-5209bb369065", "search_name": "DNS record changed", diff --git a/escu/searches/detection_dyn_dns_queries.json b/escu/searches/detection_dyn_dns_queries.json index b79e5a5fe3..1922b90d36 100644 --- a/escu/searches/detection_dyn_dns_queries.json +++ b/escu/searches/detection_dyn_dns_queries.json @@ -33,7 +33,7 @@ "Bro" ] }, - "eli5": "The search is querying an accelerated Network_Resolution data model to count and list the values of resolved domains for each DNS query and checks that against the list of Dynamic DNS providers (lookup - dynamic_dns_providers) by each host (DNS.src)", + "eli5": "The search is querying an accelerated `Network_Resolution` data model to count and list the values of resolved domains for each DNS query and checks that against the list of Dynamic DNS providers (lookup - `dynamic_dns_providers`) by each host (DNS.src)", "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 similar solutions. 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.", "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.", "maintainers": [ diff --git a/escu/searches/investigative_get_emails_from_source.json b/escu/searches/investigative_get_emails_from_source.json new file mode 100644 index 0000000000..9201703461 --- /dev/null +++ b/escu/searches/investigative_get_emails_from_source.json @@ -0,0 +1,45 @@ +{ + "channel": "ESCU", + "creation_date": "2019-02-21", + "data_metadata": { + "data_models": [ + "Email" + ], + "data_source": [ + "Email" + ], + "providing_technologies": [ + "Microsoft Exchange" + ] + }, + "fields_required": [ + "src" + ], + "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.", + "maintainers": [ + { + "company": "Splunk", + "email": "rvaldez@splunk.com", + "name": "Rico Valdez" + } + ], + "modification_date": "2019-02-21", + "original_authors": [ + { + "company": "Splunk", + "email": "rvaldez@splunk.com", + "name": "Rico Valdez" + } + ], + "search": "|tstats `summariesonly` values(All_Email.dest) as dest values(All_Email.recipient) as recepient min(_time) as firstTime max(_time) as lastTime count from datamodel=Email.All_Email by All_Email.src |`drop_dm_object_name(All_Email)` | `ctime(firstTime)` | `ctime(lastTime)`", + "search_description": "This search returns a list of all email sources seen in the 48 hours prior to the notable event to 24 hours after, and the number of emails from each source.", + "search_id": "ddc7af28-c34d-4392-af93-7f29a4e8806c", + "search_name": "Get History Of Email Sources", + "search_type": "investigative", + "search_window": { + "earliest_time_offset": 172800, + "latest_time_offset": 86400 + }, + "spec_version": 1, + "version": "1.0" +} diff --git a/escu/searches/support_discover_dns_records.json b/escu/searches/support_discover_dns_records.json index eb6a62029d..2a9cdc71ee 100644 --- a/escu/searches/support_discover_dns_records.json +++ b/escu/searches/support_discover_dns_records.json @@ -35,7 +35,7 @@ "latest_time": "-10m@m" }, "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 summariesonly=true 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", - "search_description": "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 and finds their responses across the last 30 days from data in the Network Traffic datamodel, then stores the output under the discovered_dns_records.csv lookup", + "search_description": "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_Traffic` datamodel, then stores the output under the `discovered_dns_records.csv` lookup", "search_id": "c096f721-8842-42ce-bfc7-74bd8c72b7c3", "search_name": "Discover DNS records", "search_type": "support", diff --git a/escu/stories/dns_hijacking.json b/escu/stories/dns_hijacking.json index 89ea930f42..727f1d47db 100644 --- a/escu/stories/dns_hijacking.json +++ b/escu/stories/dns_hijacking.json @@ -13,7 +13,7 @@ ], "modification_date": "2019-02-14", "name": "DNS Hijacking", - "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. Its distributed nature is one reason, as 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.

On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity and Infrastructure Security Agency (CISA) raised awareness of some high-profile DNS hijacking attacks against infrastructure, both in the United States and abroad. It issued Emergency Directive 19-01 (see https://cyber.dhs.gov/ed/19-01/), which summarized the activity and required government agencies to take the following four actions, all within 10 days:
  1. For all .gov or other agency-managed domains, audit public DNS records on all authoritative and secondary DNS servers, verify that they resolve to the intended location or report them to CISA.
  2. Update the passwords for all accounts on systems that can make changes to each agency 2019s DNS records.
  3. Implement multi-factor authentication (MFA) for all accounts on systems that can make changes to each agency 2019s DNS records or, if impossible, provide CISA with the names of systems, the reasons why MFA cannot be enabled within the required timeline, and an ETA for when it can be enabled.
  4. CISA will begin regular delivery of newly added certificates to Certificate Transparency (CT) logs for agency domains via the Cyber Hygiene service. Upon receipt, agencies must immediately begin monitoring CT log data for certificates issued that they did not request. If an agency confirms that a certificate was unauthorized, it must report the certificate to the issuing certificate authority and to CISA.
Of course, it makes sense to put equivalent actions in place within your environment, as well.

In DNS hijacking, the attacker assumes control over an account or makes use of a DNS service exploit to make changes to DNS records. Once they gain access, attackers can substitute their own MX records, name-server records, and addresses, redirecting emails and traffic through their infrastructure, where they can read, copy, or modify information seen. They can also generate valid encryption certificates to help them avoid browser-certificate checks. In one notable attack on the Internet service provider, GoDaddy, the hackers altered Sender Policy Framework (SPF) records a relatively minor change that did not inflict excessive damage but allowed for more effective spam campaigns.

The searches in this Analytic Story help you detect and investigate activities that may indicate that DNS hijacking has taken place within your environment.", + "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.

On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity and Infrastructure Security Agency (CISA) raised awareness of some high-profile DNS hijacking attacks against infrastructure, both in the United States and abroad. It issued Emergency Directive 19-01 (see https://cyber.dhs.gov/ed/19-01/), which summarized the activity and required government agencies to take the following four actions, all within 10 days:
  1. For all .gov or other agency-managed domains, audit public DNS records on all authoritative and secondary DNS servers, verify that they resolve to the intended location or report them to CISA.
  2. Update the passwords for all accounts on systems that can make changes to each agency 2019's DNS records.
  3. Implement multi-factor authentication (MFA) for all accounts on systems that can make changes to each agency's 2019 DNS records or, if impossible, provide CISA with the names of systems, the reasons why MFA cannot be enabled within the required timeline, and an ETA for when it can be enabled.
  4. CISA will begin regular delivery of newly added certificates to Certificate Transparency (CT) logs for agency domains via the Cyber Hygiene service. Upon receipt, agencies must immediately begin monitoring CT log data for certificates issued that they did not request. If an agency confirms that a certificate was unauthorized, it must report the certificate to the issuing certificate authority and to CISA.
Of course, it makes sense to put equivalent actions in place within your environment, as well.

In DNS hijacking, the attacker assumes control over an account or makes use of a DNS service exploit to make changes to DNS records. Once they gain access, attackers can substitute their own MX records, name-server records, and addresses, redirecting emails and traffic through their infrastructure, where they can read, copy, or modify information seen. They can also generate valid encryption certificates to help them avoid browser-certificate checks. In one notable attack on the Internet service provider, GoDaddy, the hackers altered Sender Policy Framework (SPF) records a relatively minor change that did not inflict excessive damage but allowed for more effective spam campaigns.

The searches in this Analytic Story help you detect and investigate activities that may indicate that DNS hijacking has taken place within your environment.", "original_authors": [ { "company": "Splunk", @@ -47,7 +47,8 @@ "Get DNS Server History for a host", "Get DNS traffic ratio", "Get Process responsible for the DNS traffic", - "Investigate Web Activity From src_ip" + "Investigate Web Activity From src_ip", + "Get History Of Email Sources" ], "support_searches": [ "Discover DNS records" diff --git a/src/default/analytic_stories.conf b/src/default/analytic_stories.conf index be20a5cae4..e6dd3957b6 100644 --- a/src/default/analytic_stories.conf +++ b/src/default/analytic_stories.conf @@ -316,18 +316,19 @@ The search in this story can help you to detect if attackers are abusing your co [DNS Hijacking] category = Adversary Tactics creation_date = 2019-02-09 -data_models = ["Application_State", "Authentication", "Network_Resolution", "Network_Traffic", "Risk", "Vulnerabilities", "Web"] +data_models = ["Application_State", "Authentication", "Email", "Network_Resolution", "Network_Traffic", "Risk", "Vulnerabilities", "Web"] description = Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. id = ad7eb6e0-f06c-4781-b145-a422d59c26e9 version = 1.0 mappings = {"mitre_attack": ["Command and Control", "Exfiltration Over Command and Control Channel", "Commonly Used Port", "Exfiltration Over Alternative Protocol", "Exfiltration", "Standard Application Layer Protocol", "Defense Evasion"], "cis20": ["CIS 8", "CIS 9", "CIS 12", "CIS 13", "CIS 3", "CIS 1"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS", "PR.IP", "PR.PT", "DE.AE", "DE.CM"]} -modification_date = 2019-02-09 +modification_date = 2019-02-21 reference = ["https://blog.malwarebytes.com/cybercrime/2015/09/dns-hijacks-what-to-look-for/", "https://www.darkreading.com/attacks-breaches/dns-hijacking-the-silent-threat-thats-putting-your-network-at-risk/a/d-id/1330922", "https://blog.talosintelligence.com/2018/11/dnspionage-campaign-targets-middle-east.html", "https://www.fireeye.com/blog/threat-research/2019/01/global-dns-hijacking-campaign-dns-record-manipulation-at-scale.html", "https://www.splunk.com/blog/2019/01/25/cisa-emergency-directive-19-01-doing-things-the-easy-way-in-splunk.html"] -providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Nessus", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] -detection_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"] -investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Process responsible for the DNS traffic", "ESCU - Investigate Web Activity From src_ip"] +providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Exchange", "Microsoft Windows", "Nessus", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] +detection_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"] +investigative_searches = ["ESCU - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Process responsible for the DNS traffic", "ESCU - Investigate Web Activity From src_ip", "ESCU - Get History Of Email Sources"] contextual_searches = ["ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint"] -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. Its distributed nature is one reason, as it relies on unstructured connections between millions of clients and servers over inherently insecure protocols.\ +support_searches = ["ESCU - Discover 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. \ \ @@ -335,9 +336,9 @@ On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity \ 1. For all .gov or other agency-managed domains, audit public DNS records on all authoritative and secondary DNS servers, verify that they resolve to the intended location or report them to CISA.\ \ -1. Update the passwords for all accounts on systems that can make changes to each agency 2019s DNS records.\ +1. Update the passwords for all accounts on systems that can make changes to each agency 2019's DNS records.\ \ -1. Implement multi-factor authentication (MFA) for all accounts on systems that can make changes to each agency 2019s DNS records or, if impossible, provide CISA with the names of systems, the reasons why MFA cannot be enabled within the required timeline, and an ETA for when it can be enabled.\ +1. Implement multi-factor authentication (MFA) for all accounts on systems that can make changes to each agency's 2019 DNS records or, if impossible, provide CISA with the names of systems, the reasons why MFA cannot be enabled within the required timeline, and an ETA for when it can be enabled.\ \ 1. CISA will begin regular delivery of newly added certificates to Certificate Transparency (CT) logs for agency domains via the Cyber Hygiene service. Upon receipt, agencies must immediately begin monitoring CT log data for certificates issued that they did not request. If an agency confirms that a certificate was unauthorized, it must report the certificate to the issuing certificate authority and to CISA.Of course, it makes sense to put equivalent actions in place within your environment, as well. \ \ @@ -400,7 +401,7 @@ data_models = ["Application_State", "Authentication", "Email", "Endpoint", "Netw 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. id = bb9f5ed2-916e-4364-bb6d-91c310efcf52 version = 1.0 -mappings = {"mitre_attack": ["Third-party Software", "AppInit DLLs", "Commonly Used Port", "Command-Line Interface", "Registry Run Keys / Start Folder", "Persistence", "Defense Evasion", "Execution", "Authentication Package", "Account Discovery"], "cis20": ["CIS 7", "CIS 12", "CIS 2", "CIS 3", "CIS 8"], "kill_chain_phases": ["Exploitation", "Actions on Objectives", "Delivery", "Installation", "Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "PR.PT", "DE.AE", "DE.CM"]} +mappings = {"mitre_attack": ["Third-party Software", "AppInit DLLs", "Commonly Used Port", "Command-Line Interface", "Registry Run Keys / Start Folder", "Persistence", "Defense Evasion", "Execution", "Authentication Package", "Account Discovery"], "cis20": ["CIS 7", "CIS 12", "CIS 2", "CIS 3", "CIS 8"], "kill_chain_phases": ["Exploitation", "Delivery", "Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS", "PR.IP", "PR.PT", "DE.AE", "DE.CM"]} modification_date = 2018-12-03 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"] providing_technologies = ["Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Exchange", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] @@ -730,7 +731,7 @@ data_models = ["Application_State", "Authentication", "Network_Resolution", "Net 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. id = 6d13121c-90f3-446d-8ac3-27efbbc65218 version = 1.0 -mappings = {"mitre_attack": ["Command and Control", "Exfiltration Over Command and Control Channel", "Commonly Used Port", "Exfiltration Over Alternative Protocol", "Exfiltration", "Defense Evasion"], "cis20": ["CIS 12", "CIS 13", "CIS 8", "CIS 9"], "kill_chain_phases": ["Command and Control", "Actions on Objectives", "Delivery"], "nist": ["PR.PT", "DE.AE", "DE.CM", "PR.AC", "PR.DS"]} +mappings = {"mitre_attack": ["Command and Control", "Exfiltration Over Command and Control Channel", "Commonly Used Port", "Exfiltration Over Alternative Protocol", "Exfiltration", "Defense Evasion"], "cis20": ["CIS 12", "CIS 13", "CIS 8", "CIS 9"], "kill_chain_phases": ["Command and Control", "Delivery", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "DE.CM", "PR.AC", "PR.DS"]} modification_date = 2018-07-24 reference = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"] providing_technologies = ["Bluecoat", "Bro", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "macOS"] @@ -930,7 +931,7 @@ data_models = 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. id = 2e8948a5-5239-406b-b56b-6c50w3168af3 version = 2.0 -mappings = {"mitre_attack": ["Exfiltration", "Credential Access", "Execution", "Initial Access"], "cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "PR.DS", "DE.CM", "PR.AC"]} +mappings = {"mitre_attack": ["Exfiltration", "Credential Access", "Execution", "Initial Access"], "cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM", "PR.DS", "DE.DP", "PR.AC"]} modification_date = 2018-11-27 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/"] providing_technologies = ["AWS", "Splunk Enterprise Security"] @@ -1146,7 +1147,7 @@ data_models = ["Email"] 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. id = 31337aaa-bc22-4752-b599-ef112dq1dq7a version = 1.0 -mappings = {"mitre_attack": ["Valid Accounts", "Create Account"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 6", "CIS 16"], "nist": ["DE.CM", "DE.AE", "DE.DP"]} +mappings = {"mitre_attack": ["Valid Accounts", "Create Account"], "cis20": ["CIS 6", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE", "DE.CM"]} modification_date = 2018-10-08 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", "https://www.otalliance.org/news-events/press-releases/online-trust-alliance-reports-doubling-cyber-incidents-2017-0"] providing_technologies = ["Bro", "Microsoft Exchange", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream"] diff --git a/src/default/analyticstories.conf b/src/default/analyticstories.conf index bfaa28f35e..afe0eca898 100644 --- a/src/default/analyticstories.conf +++ b/src/default/analyticstories.conf @@ -252,7 +252,7 @@ version = 1.0 category = Adversary Tactics description = Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. maintainers = [{"company": "Splunk", "email": "bpatel@splunk.com", "name": "Bhavin Patel"}] -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. Its distributed nature is one reason, as it relies on unstructured connections between millions of clients and servers over inherently insecure protocols.\ +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. \ \ @@ -260,9 +260,9 @@ On January 22, 2019, the US Department of Homeland Security 2019's Cybersecurity \ 1. For all .gov or other agency-managed domains, audit public DNS records on all authoritative and secondary DNS servers, verify that they resolve to the intended location or report them to CISA.\ \ -1. Update the passwords for all accounts on systems that can make changes to each agency 2019s DNS records.\ +1. Update the passwords for all accounts on systems that can make changes to each agency 2019's DNS records.\ \ -1. Implement multi-factor authentication (MFA) for all accounts on systems that can make changes to each agency 2019s DNS records or, if impossible, provide CISA with the names of systems, the reasons why MFA cannot be enabled within the required timeline, and an ETA for when it can be enabled.\ +1. Implement multi-factor authentication (MFA) for all accounts on systems that can make changes to each agency's 2019 DNS records or, if impossible, provide CISA with the names of systems, the reasons why MFA cannot be enabled within the required timeline, and an ETA for when it can be enabled.\ \ 1. CISA will begin regular delivery of newly added certificates to Certificate Transparency (CT) logs for agency domains via the Cyber Hygiene service. Upon receipt, agencies must immediately begin monitoring CT log data for certificates issued that they did not request. If an agency confirms that a certificate was unauthorized, it must report the certificate to the issuing certificate authority and to CISA.Of course, it makes sense to put equivalent actions in place within your environment, as well. \ \ @@ -270,9 +270,9 @@ In DNS hijacking, the attacker assumes control over an account or makes use of a \ The searches in this Analytic Story help you detect and investigate activities that may indicate that DNS hijacking has taken place within your environment. references = ["https://blog.malwarebytes.com/cybercrime/2015/09/dns-hijacks-what-to-look-for/", "https://www.darkreading.com/attacks-breaches/dns-hijacking-the-silent-threat-thats-putting-your-network-at-risk/a/d-id/1330922", "https://blog.talosintelligence.com/2018/11/dnspionage-campaign-targets-middle-east.html", "https://www.fireeye.com/blog/threat-research/2019/01/global-dns-hijacking-campaign-dns-record-manipulation-at-scale.html", "https://www.splunk.com/blog/2019/01/25/cisa-emergency-directive-19-01-doing-things-the-easy-way-in-splunk.html"] -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 - Get DNS Server History for a host", "ESCU - Get DNS traffic ratio", "ESCU - Get Process responsible for the DNS traffic", "ESCU - Investigate Web Activity From src_ip","ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint"] +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", "ESCU - Get DNS traffic ratio", "ESCU - Get Process responsible for the DNS traffic", "ESCU - Investigate Web Activity From src_ip", "ESCU - Get History Of Email Sources","ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For User", "ESCU - Get Risk Modifiers For Endpoint","ESCU - Discover DNS records"] spec_version = 1 -last_updated = 2019-02-09 +last_updated = 2019-02-21 version = 1.0 @@ -1031,43 +1031,26 @@ known_false_positives = It is possible that these logs may be legitimately clear providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Get Process Information For Port Activity] -type = investigative -explanation = none -how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events. -known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -earliest_time_offset = 7200 -latest_time_offset = 7200 - - -[savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] +[savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] type = detection -asset_type = AWS Instance +asset_type = Endpoint +confidence = low +explanation = To detect pass the hash activity, we look at all events with event code 4624 or 4625 that specify a logon type 3 (network logons). We are looking for the NtLmSsP account, with a key length set to 0. These indicate lower level protocols that are typically used through Pass the Hash (WMI, SMB, etc.). The search also filters out events with an account name of 'Anonymous' to help reduce false positives. +how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the TA for Windows to extract EventCode, Logon_Process, Logon_Type, Key_Length and Account_Name fields from these events. +annotations = {"mitre_attack": ["Lateral Movement", "Pass the Hash"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "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"] + + +[savedsearch://ESCU - Create or delete hidden shares using net.exe - Rule] +type = detection +asset_type = Endpoint 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. -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 = {"mitre_attack": ["Exfiltration", "Command and Control"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "cis20": ["CIS 11"], "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"] +explanation = 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 "$" after the name of the share. To look for hidden shares, use a regular expression to look for a `(name_file_share)$`. 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 of hidden shares +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 = {"mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] [savedsearch://ESCU - Reg.exe used to hide files/directories via registry keys - Rule] @@ -1080,37 +1063,14 @@ known_false_positives = None at the moment providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] -type = detection -asset_type = Endpoint -confidence = low -explanation = To detect pass the hash activity, we look at all events with event code 4624 or 4625 that specify a logon type 3 (network logons). We are looking for the NtLmSsP account, with a key length set to 0. These indicate lower level protocols that are typically used through Pass the Hash (WMI, SMB, etc.). The search also filters out events with an account name of 'Anonymous' to help reduce false positives. -how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the TA for Windows to extract EventCode, Logon_Process, Logon_Type, Key_Length and Account_Name fields from these events. -annotations = {"mitre_attack": ["Lateral Movement", "Pass the Hash"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "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"] - - -[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. -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 = {"mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 8", "CIS 12", "CIS 13"], "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"] - - -[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 -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", "Palo Alto Firewall", "Bro"] +[savedsearch://ESCU - Get Process Information For Port Activity] +type = investigative +explanation = none +how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events. +known_false_positives = None at this time +providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +earliest_time_offset = 7200 +latest_time_offset = 7200 [savedsearch://ESCU - TOR Traffic - Rule] @@ -1145,12 +1105,15 @@ earliest_time_offset = 3600 latest_time_offset = 3600 -[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. -how_to_implement = To successfully implement this search you must be ingesting your backup logs. -known_false_positives = None at this time -providing_technologies = ["Netbackup"] +[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. +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 = {"mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 8", "CIS 12", "CIS 13"], "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"] [savedsearch://ESCU - Get Logon Rights Modifications For Endpoint] @@ -1163,6 +1126,14 @@ earliest_time_offset = 86400 latest_time_offset = 86400 +[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. +how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. +known_false_positives = None at this time +providing_technologies = ["Splunk Stream", "Bro"] + + [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. @@ -1171,15 +1142,33 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[savedsearch://ESCU - Create or delete hidden shares using net.exe - Rule] +[savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] type = detection -asset_type = Endpoint +asset_type = AWS Instance confidence = medium -explanation = 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 "$" after the name of the share. To look for hidden shares, use a regular expression to look for a `(name_file_share)$`. 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 of hidden shares -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 = {"mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] +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. +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 = {"mitre_attack": ["Exfiltration", "Command and Control"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "cis20": ["CIS 11"], "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"] [savedsearch://ESCU - Detect processes used for System Network Configuration Discovery - Rule] @@ -1301,14 +1290,11 @@ known_false_positives = There may be other processes in your environment that us providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[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 -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 = {"mitre_attack": ["Execution", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13", "CIS 14"], "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 +[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. +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 `NetworkACLEvents`. +known_false_positives = None at this time providing_technologies = ["AWS"] @@ -1334,15 +1320,56 @@ known_false_positives = The activity may be legitimate. For this reason, it's be providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Remote Desktop Network Bruteforce - Rule] +[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. +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 = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "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"] + + +[savedsearch://ESCU - DNS record changed - 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. -how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. -annotations = {"mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "cis20": ["CIS 12", "CIS 9", "CIS 16"], "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"] +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. +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 Investigation" 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/4.1/playbook/dns-hijack-investigation/`).\ +\ + +annotations = {"mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "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"] [savedsearch://ESCU - Web Fraud - Anomalous User Clickspeed - Rule] @@ -1356,26 +1383,25 @@ known_false_positives = As is common with many fraud-related searches, we are us providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Bro"] -[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. -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 = {"mitre_attack": ["Discovery", "System Information Discovery"], "kill_chain_phases": ["Reconnaissance"]} -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"] +[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` +how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic 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 = None at this time +providing_technologies = ["Splunk Stream", "Bro"] -[savedsearch://ESCU - SMB Traffic Spike - Rule] +[savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] type = detection -asset_type = Endpoint +asset_type = AWS Instance 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. -how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. -annotations = {"mitre_attack": ["Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] +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. +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"] [savedsearch://ESCU - Samsam Test File Write - Rule] @@ -1399,6 +1425,16 @@ earliest_time_offset = 14400 latest_time_offset = 0 +[savedsearch://ESCU - Get EC2 Instance Details by instanceId] +type = contextual +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 +providing_technologies = ["AWS"] +earliest_time_offset = 86400 +latest_time_offset = 0 + + [savedsearch://ESCU - AWS Network Access Control List Created with All Open Ports - Rule] type = detection asset_type = AWS Instance @@ -1421,6 +1457,17 @@ known_false_positives = The activity may be legitimate. PowerShell is often used providing_technologies = ["Microsoft Windows"] +[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. +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 = {"mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "kill_chain_phases": ["Delivery", "Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE", "PR.AC"]} +known_false_positives = None identified +providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] + + [savedsearch://ESCU - Detect Unauthorized Assets by MAC address - Rule] type = detection asset_type = Infrastructure @@ -1443,26 +1490,24 @@ known_false_positives = It is possible that your vulnerability scanner is not de providing_technologies = ["Nessus", "Qualys"] -[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. -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 = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -known_false_positives = These characters might be legitimately on the command-line, but it is not common. +[savedsearch://ESCU - Get Notable History] +type = contextual +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 +providing_technologies = ["Splunk Enterprise Security"] +earliest_time_offset = 864000 +latest_time_offset = 86400 + + +[savedsearch://ESCU - Get Parent Process Info] +type = investigative +explanation = none +how_to_implement = To successfully implement this search you must be ingesting endpoint data via Microsoft-Windows-Sysmon and extract the Image and Parent Image field. +known_false_positives = None at this time providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] - - -[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. -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"] +earliest_time_offset = 0 +latest_time_offset = 86400 [savedsearch://ESCU - Processes launching netsh - Rule] @@ -1506,15 +1551,12 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] -type = detection -asset_type = Windows -confidence = medium -explanation = This search looks for Windows Event Code 4663 (object access), where the process performing the access is PowerShell.exe, the target process of the access is lsass.exe, and the access mask is given as 0x10. 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. -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 = {"mitre_attack": ["Credential Access", "Credential Dumping"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "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"] +[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. +how_to_implement = To successfully implement this search you must be ingesting your backup logs. +known_false_positives = None at this time +providing_technologies = ["Netbackup"] [savedsearch://ESCU - Suspicious Reg.exe Process - Rule] @@ -1580,16 +1622,6 @@ known_false_positives = Many service accounts configured within an AWS infrastru providing_technologies = ["AWS"] -[savedsearch://ESCU - Investigate Web Activity From Host] -type = investigative -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 -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -earliest_time_offset = 3600 -latest_time_offset = 3600 - - [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. It extracts both the name of the service and the action taken by the service from the logs. It keeps only services that have entered the running state. Finally, it finds the first time the service has been seen running across the enterprise and writes that file to a lookup table. @@ -1630,21 +1662,35 @@ known_false_positives = You will encounter noise from legitimate print-monitor r providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -[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. -how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. -known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro"] +[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. +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 = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 8"], "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"] -[savedsearch://ESCU - Get All AWS Activity From Region] -type = investigative +[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 +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 = {"mitre_attack": ["Exfiltration"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 13"], "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"] + + +[savedsearch://ESCU - Get Backup Logs For Endpoint] +type = contextual 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. +how_to_implement = You must be ingesting your backup logs. known_false_positives = None at this time -providing_technologies = ["AWS"] -earliest_time_offset = 14400 +providing_technologies = ["Netbackup"] +earliest_time_offset = 604800 latest_time_offset = 0 @@ -1688,23 +1734,15 @@ known_false_positives = This technique may be legitimately used by administrator providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -[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. -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 -providing_technologies = ["AWS"] - - -[savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - Rule] +[savedsearch://ESCU - Abnormally High AWS Instances Launched by User - 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. -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 = {"mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 12", "CIS 4", "CIS 18"], "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"] +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. +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 = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "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"] [savedsearch://ESCU - Email servers sending high volume traffic to hosts - Rule] @@ -1729,6 +1767,17 @@ known_false_positives = It is possible legitimate traffic can trigger this rule. providing_technologies = ["Splunk Stream", "Bro"] +[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. +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 = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "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"] + + [savedsearch://ESCU - DNS Query Length With High Standard Deviation - Rule] type = detection asset_type = Endpoint @@ -1751,48 +1800,26 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] +[savedsearch://ESCU - Remote Desktop Network Traffic - 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. -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 = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 8"], "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"] +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. +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 = {"mitre_attack": ["Lateral Movement", "Remote Desktop Protocol", "Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "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"] -[savedsearch://ESCU - Detect Spike in AWS API Activity - Rule] +[savedsearch://ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] type = detection -asset_type = AWS Instance +asset_type = Web Server 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. -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. -annotations = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -known_false_positives = -providing_technologies = ["AWS"] +explanation = This search returns the number of times a URL associated with this type of JexBoss probe is observed. +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 = {"mitre_attack": ["Discovery", "System Information Discovery"], "kill_chain_phases": ["Reconnaissance"]} +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"] [savedsearch://ESCU - WMI Temporary Event Subscription - Rule] @@ -1852,15 +1879,15 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Attempt To Add Certificate To Untrusted Store - Rule] +[savedsearch://ESCU - Common Ransomware Extensions - 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. -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 = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "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"] +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. +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 = {"mitre_attack": [], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] [savedsearch://ESCU - Previously seen S3 bucket access by remote IP] @@ -1978,14 +2005,15 @@ known_false_positives = None identified providing_technologies = ["Microsoft Exchange"] -[savedsearch://ESCU - Get Notable History] -type = contextual -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 -providing_technologies = ["Splunk Enterprise Security"] -earliest_time_offset = 864000 -latest_time_offset = 86400 +[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. +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 = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "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"] [savedsearch://ESCU - Create a list of approved AWS service accounts] @@ -2016,14 +2044,14 @@ earliest_time_offset = 1209600 latest_time_offset = 0 -[savedsearch://ESCU - Get Backup Logs For Endpoint] +[savedsearch://ESCU - Get User Information from Identity Table] type = contextual explanation = none -how_to_implement = You must be ingesting your backup logs. +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 -providing_technologies = ["Netbackup"] -earliest_time_offset = 604800 -latest_time_offset = 0 +providing_technologies = ["Splunk Enterprise Security"] +earliest_time_offset = 864000 +latest_time_offset = 86400 [savedsearch://ESCU - Child Processes of Spoolsv.exe - Rule] @@ -2051,7 +2079,7 @@ latest_time_offset = 86400 type = detection asset_type = Endpoint confidence = medium -explanation = The search is querying an accelerated Network_Resolution data model to count and list the values of resolved domains for each DNS query and checks that against the list of Dynamic DNS providers (lookup - dynamic_dns_providers) by each host (DNS.src) +explanation = The search is querying an accelerated `Network_Resolution` data model to count and list the values of resolved domains for each DNS query and checks that against the list of Dynamic DNS providers (lookup - `dynamic_dns_providers`) by each host (DNS.src) 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 similar solutions. 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`. annotations = {"mitre_attack": ["Exfiltration", "Exfiltration Over Command and Control Channel", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 8", "CIS 12", "CIS 13"], "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. @@ -2088,25 +2116,48 @@ known_false_positives = None identified providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Get All AWS Activity From City] -type = investigative -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 -providing_technologies = ["AWS"] -earliest_time_offset = 14400 -latest_time_offset = 0 - - -[savedsearch://ESCU - Detect USB device insertion - Rule] +[savedsearch://ESCU - Detect Spike in AWS API Activity - 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 -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 = {"mitre_attack": ["Exfiltration"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 13"], "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"] +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. +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. +annotations = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +known_false_positives = +providing_technologies = ["AWS"] + + +[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 +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", "Palo Alto Firewall", "Bro"] [savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule] @@ -2120,15 +2171,15 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Abnormally High AWS Instances Launched by User - Rule] +[savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - 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. -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 = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "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"] +asset_type = Web Server +confidence = high +explanation = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers. +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 = {"mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 12", "CIS 4", "CIS 18"], "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"] [savedsearch://ESCU - Detect Excessive User Account Lockouts - Rule] @@ -2184,15 +2235,15 @@ known_false_positives = There may be legitimate reasons for system administrator providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -[savedsearch://ESCU - Unsuccessful Netbackup backups - Rule] +[savedsearch://ESCU - Detect S3 access from a new IP - 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. -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"] +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 +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 = {"mitre_attack": ["Execution", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13", "CIS 14"], "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"] [savedsearch://ESCU - WMI Permanent Event Subscription - Rule] @@ -2258,25 +2309,23 @@ earliest_time_offset = 604800 latest_time_offset = 0 -[savedsearch://ESCU - Get Process Info] -type = investigative -explanation = none -how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Application_State data model. -known_false_positives = None at this time -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -earliest_time_offset = 7200 -latest_time_offset = 7200 - - -[savedsearch://ESCU - Spike in File Writes - Rule] +[savedsearch://ESCU - Unsuccessful Netbackup backups - 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. -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 = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] +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. +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"] + + +[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. +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 = None at this time +providing_technologies = ["AWS"] [savedsearch://ESCU - Detect new API calls from user roles - Rule] @@ -2311,17 +2360,14 @@ known_false_positives = It's possible that legitimate traffic will have long URL providing_technologies = ["Splunk Stream", "Bro"] -[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. -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"] +[savedsearch://ESCU - Get Web Session Information via session_id] +type = investigative +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 +providing_technologies = ["Splunk Stream"] +earliest_time_offset = 3600 +latest_time_offset = 3600 [savedsearch://ESCU - Execution of File With Spaces Before Extension - Rule] @@ -2356,12 +2402,25 @@ known_false_positives = There are many legitimate applications that must execute providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -[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. -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 `NetworkACLEvents`. +[savedsearch://ESCU - Get All AWS Activity From Region] +type = investigative +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 providing_technologies = ["AWS"] +earliest_time_offset = 14400 +latest_time_offset = 0 + + +[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 `ec2ModificationAPIs`. 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. +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 `ec2ModificationAPIs`. +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"] [savedsearch://ESCU - AWS S3 Bucket details via bucketName] @@ -2385,6 +2444,17 @@ known_false_positives = It is possible that an administrator created and deleted providing_technologies = ["Microsoft Windows"] +[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. +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 = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "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"] + + [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule] type = detection asset_type = AWS Instance @@ -2420,6 +2490,16 @@ known_false_positives = Because these extensions are not typically used in norma providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +[savedsearch://ESCU - AWS Network Interface details via resourceId] +type = contextual +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 +providing_technologies = ["AWS"] +earliest_time_offset = 86400 +latest_time_offset = 0 + + [savedsearch://ESCU - Investigate Successful Remote Desktop Authentications] type = investigative explanation = none @@ -2430,36 +2510,6 @@ earliest_time_offset = 86400 latest_time_offset = 0 -[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. -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 = {"mitre_attack": [], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] - - -[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. -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"] - - -[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 in the Application_State data model. -how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. -known_false_positives = None at this time -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] - - [savedsearch://ESCU - Detect new user AWS Console Login - Rule] type = detection asset_type = AWS Instance @@ -2471,6 +2521,17 @@ known_false_positives = When a legitimate new user logins for the first time, th providing_technologies = ["AWS"] +[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. +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"] + + [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. @@ -2479,47 +2540,37 @@ known_false_positives = None at this time providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Remote WMI Command Attempt - Rule] +[savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] +type = detection +asset_type = Windows +confidence = medium +explanation = This search looks for Windows Event Code 4663 (object access), where the process performing the access is PowerShell.exe, the target process of the access is lsass.exe, and the access mask is given as 0x10. 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. +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 = {"mitre_attack": ["Credential Access", "Credential Dumping"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "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"] + + +[savedsearch://ESCU - Registry Keys for Creating SHIM Databases - 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. -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 = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "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"] +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. +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 = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] -[savedsearch://ESCU - Process Execution via WMI - Rule] +[savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - 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. -how_to_implement = To successfully implement this search, you must 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 = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "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"] - - -[savedsearch://ESCU - Get User Information from Identity Table] -type = contextual -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 -providing_technologies = ["Splunk Enterprise Security"] -earliest_time_offset = 864000 -latest_time_offset = 86400 - - -[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. -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 = {"mitre_attack": ["Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = It's possible that a user has legitimately deleted a network ACL. -providing_technologies = ["AWS"] +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. +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 = {"mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "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"] [savedsearch://ESCU - Get All AWS Activity From Country] @@ -2532,6 +2583,16 @@ earliest_time_offset = 14400 latest_time_offset = 0 +[savedsearch://ESCU - Get History Of Email Sources] +type = investigative +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 +providing_technologies = ["Microsoft Exchange"] +earliest_time_offset = 172800 +latest_time_offset = 86400 + + [savedsearch://ESCU - Shim Database File Creation - Rule] type = detection asset_type = Endpoint @@ -2576,15 +2637,14 @@ known_false_positives = As is common with many fraud-related searches, we are us providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Bro"] -[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. -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 = {"mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task", "Remote Services"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "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"] +[savedsearch://ESCU - Get Authentication Logs For Endpoint] +type = contextual +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 +providing_technologies = ["Microsoft Windows", "Linux", "macOS"] +earliest_time_offset = 43200 +latest_time_offset = 1 [savedsearch://ESCU - Detect web traffic to dynamic domain providers - Rule] @@ -2627,14 +2687,15 @@ known_false_positives = Some legitimate processes may be only rarely executed in providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - AWS Network Interface details via resourceId] -type = contextual -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 -providing_technologies = ["AWS"] -earliest_time_offset = 86400 -latest_time_offset = 0 +[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. +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 = {"mitre_attack": ["Command and Control", "Standard Non-Application Layer Protocol"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 9", "CIS 12"], "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"] [savedsearch://ESCU - Monitor Web Traffic For Brand Abuse - Rule] @@ -2648,23 +2709,15 @@ known_false_positives = None at this time providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -[savedsearch://ESCU - Detect PsExec With accepteula Flag - Rule] +[savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] type = detection -asset_type = Endpoint +asset_type = AWS Instance confidence = medium -explanation = In this search, we are looking for the PsExec process with `accepteula` on the command line. -how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, command-line arguments, and parent process from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon Technology Add-on (TA). -annotations = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] - - -[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. -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 = None at this time -providing_technologies = ["Splunk Stream", "Bro"] +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. +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"] [savedsearch://ESCU - Baseline of API Calls per User ARN] @@ -2686,48 +2739,15 @@ known_false_positives = Using sc.exe to manipulate Windows services is uncommon. providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] +[savedsearch://ESCU - Remote Desktop Network Bruteforce - 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. -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 = {"mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "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"] - - -[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. -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 = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "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"] +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. +how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. +annotations = {"mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "cis20": ["CIS 12", "CIS 9", "CIS 16"], "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"] [savedsearch://ESCU - Deleting Shadow Copies - Rule] @@ -2798,15 +2818,12 @@ known_false_positives = None at this time providing_technologies = ["Netbackup"] -[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. -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 = {"mitre_attack": ["Command and Control", "Standard Non-Application Layer Protocol"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 9", "CIS 12"], "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"] +[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. +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 +providing_technologies = ["AWS"] [savedsearch://ESCU - Extended Period Without Successful Netbackup Backups - Rule] @@ -2830,14 +2847,15 @@ earliest_time_offset = 3600 latest_time_offset = 0 -[savedsearch://ESCU - Get EC2 Instance Details by instanceId] -type = contextual -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 -providing_technologies = ["AWS"] -earliest_time_offset = 86400 -latest_time_offset = 0 +[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. +how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, command-line arguments, and parent process from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon Technology Add-on (TA). +annotations = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] [savedsearch://ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule] @@ -2875,26 +2893,15 @@ known_false_positives = This is a strictly behavioral search, so we define "fals providing_technologies = ["AWS"] -[savedsearch://ESCU - Processes created by netsh - Rule] +[savedsearch://ESCU - Suspicious Java Classes - 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. -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 = {"mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] - - -[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. -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 = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] +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. +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 = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 7", "CIS 12"], "nist": ["DE.AE"]} +known_false_positives = There are no known false positives. +providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Apache"] [savedsearch://ESCU - Baseline of S3 Bucket deletion activity by ARN] @@ -2905,15 +2912,24 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[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. -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 = {"mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "kill_chain_phases": ["Delivery", "Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE", "PR.AC"]} -known_false_positives = None identified -providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] +[savedsearch://ESCU - Investigate Web Activity From Host] +type = investigative +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 +providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +earliest_time_offset = 3600 +latest_time_offset = 3600 + + +[savedsearch://ESCU - Get All AWS Activity From City] +type = investigative +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 +providing_technologies = ["AWS"] +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Monitor Email For Brand Abuse - Rule] @@ -2927,6 +2943,17 @@ known_false_positives = None at this time providing_technologies = ["Microsoft Exchange", "Bro", "Splunk Stream"] +[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. +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 = {"mitre_attack": ["Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11"], "nist": ["DE.DP", "DE.AE"]} +known_false_positives = It's possible that a user has legitimately deleted a network ACL. +providing_technologies = ["AWS"] + + [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. @@ -2935,14 +2962,15 @@ known_false_positives = None at this time providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Get Authentication Logs For Endpoint] -type = contextual -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 -providing_technologies = ["Microsoft Windows", "Linux", "macOS"] -earliest_time_offset = 43200 -latest_time_offset = 1 +[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. +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 = {"mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task", "Remote Services"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "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"] [savedsearch://ESCU - Single Letter Process On Endpoint - Rule] @@ -2967,15 +2995,26 @@ known_false_positives = Some networks may use kerberized FTP or telnet servers, providing_technologies = ["Splunk Stream", "Bro"] -[savedsearch://ESCU - Suspicious Java Classes - Rule] +[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. +how_to_implement = You must 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. 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 = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "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"] + + +[savedsearch://ESCU - Processes created by netsh - 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. -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 = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 7", "CIS 12"], "nist": ["DE.AE"]} -known_false_positives = There are no known false positives. -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Apache"] +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. +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 = {"mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] [savedsearch://ESCU - Add Prohibited Processes to Enterprise Security] @@ -2997,15 +3036,14 @@ known_false_positives = None at this time providing_technologies = ["Splunk Stream", "Bro"] -[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. -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 = {"mitre_attack": ["Lateral Movement", "Remote Desktop Protocol", "Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "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"] +[savedsearch://ESCU - Get First Occurrence and Last Occurrence of a MAC Address] +type = contextual +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 +providing_technologies = ["Splunk Stream", "Bro", "Microsoft Windows"] +earliest_time_offset = 864000 +latest_time_offset = 86400 [savedsearch://ESCU - Create local admin accounts using net.exe - Rule] @@ -3059,11 +3097,14 @@ known_false_positives = None identified. providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[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. -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 = None at this time +[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. +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 = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "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"] @@ -3140,6 +3181,17 @@ earliest_time_offset = 14400 latest_time_offset = 0 +[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. +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 = {"mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "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"] + + [savedsearch://ESCU - Get DNS traffic ratio] type = investigative explanation = none @@ -3172,12 +3224,14 @@ known_false_positives = It is likely that the outbound Server Message Block (SMB providing_technologies = ["Bro", "Splunk Stream"] -[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. -how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. +[savedsearch://ESCU - Get Process Info] +type = investigative +explanation = none +how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Application_State data model. known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +earliest_time_offset = 7200 +latest_time_offset = 7200 [savedsearch://ESCU - Detect Spike in Security Group Activity - Rule] @@ -3257,15 +3311,15 @@ known_false_positives = There are many legitimate applications that must execute providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -[savedsearch://ESCU - Batch File Write to System32 - Rule] +[savedsearch://ESCU - SMB Traffic Spike - 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. -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 = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 8"], "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"] +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. +how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. +annotations = {"mitre_attack": ["Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] [savedsearch://ESCU - AWS Investigate User Activities By AccessKeyId] @@ -3311,33 +3365,23 @@ known_false_positives = If the Identity_Management data model is not updated reg providing_technologies = ["Active Directory"] -[savedsearch://ESCU - Get First Occurrence and Last Occurrence of a MAC Address] -type = contextual -explanation = none -how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server. +[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 in the Application_State data model. +how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro", "Microsoft Windows"] -earliest_time_offset = 864000 -latest_time_offset = 86400 - - -[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. -how_to_implement = You must 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. 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 = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "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"] -[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. -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 = None at this time -providing_technologies = ["AWS"] +[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. +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 = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "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"] [savedsearch://ESCU - Uncommon Processes On Endpoint - Rule] @@ -3351,26 +3395,15 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Detect New Open S3 buckets - Rule] +[savedsearch://ESCU - Process Execution via WMI - Rule] type = detection -asset_type = S3 Bucket +asset_type = Endpoint 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. -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 = {"mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "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"] - - -[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 `ec2ModificationAPIs`. 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. -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 `ec2ModificationAPIs`. -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"] +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. +how_to_implement = To successfully implement this search, you must 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 = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "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"] [savedsearch://ESCU - Remote Process Instantiation via WMI - Rule] @@ -3384,6 +3417,17 @@ known_false_positives = The wmic.exe utility is a benign Windows application. It providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +[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. +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 = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 8"], "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"] + + [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 @@ -3403,14 +3447,15 @@ known_false_positives = None identified. providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Get Parent Process Info] -type = investigative -explanation = none -how_to_implement = To successfully implement this search you must be ingesting endpoint data via Microsoft-Windows-Sysmon and extract the Image and Parent Image field. -known_false_positives = None at this time -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -earliest_time_offset = 0 -latest_time_offset = 86400 +[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. +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"] [savedsearch://ESCU - DNSTwist Domain Names] @@ -3432,14 +3477,11 @@ known_false_positives = It's possible that normal DNS traffic will exhibit this providing_technologies = ["Splunk Stream", "Bro"] -[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. -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 = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -known_false_positives = System administrators may use this option, but it's not common. +[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. +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 = None at this time providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] @@ -3464,25 +3506,20 @@ known_false_positives = None at this time providing_technologies = ["Microsoft Exchange"] -[savedsearch://ESCU - Get Web Session Information via session_id] -type = investigative -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. +[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. +how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. known_false_positives = None at this time -providing_technologies = ["Splunk Stream"] -earliest_time_offset = 3600 -latest_time_offset = 3600 +providing_technologies = ["Splunk Stream", "Bro"] -[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. -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"] +[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. +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 = None at this time +providing_technologies = ["Splunk Stream", "Bro"] [savedsearch://ESCU - Get Risk Modifiers For Endpoint] diff --git a/src/default/savedsearches.conf b/src/default/savedsearches.conf index 03bb3cadc6..e5ec63fca3 100644 --- a/src/default/savedsearches.conf +++ b/src/default/savedsearches.conf @@ -13,7 +13,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Indicator Removal o action.escu.known_false_positives = It is possible that these logs may be legitimately cleared by Administrators. action.escu.search_type = detection action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] +action.escu.analytic_story = ["Ransomware", "Windows Log Manipulation"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Windows Event Log Cleared action.notable = 1 @@ -22,7 +22,7 @@ action.notable.param.rule_description = The Event Logging System has been cleare action.notable.param.rule_title = Windows Event Log Cleared on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -47,145 +47,6 @@ schedule_window = auto is_visible = false search = ((sourcetype=*wineventlog:security) AND (EventCode=1102 OR EventCode=1100)) OR ((sourcetype=wineventlog:system OR sourcetype=XmlWinEventlog:System) AND EventCode=104) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode sourcetype host | `ctime(firstTime)` | `ctime(lastTime)` | rename host as dest -[ESCU - Get Process Information For Port Activity] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-06-25 -action.escu.modification_date = 2017-09-10 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events. -action.escu.data_models = ["Application_State"] -action.escu.full_search_name = ESCU - Get Process Information For Port Activity -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.analytic_story = ["SamSam Ransomware", "Ransomware", "Prohibited Traffic Allowed or Protocol Mismatch", "Use of Cleartext Protocols", "Command and Control"] -action.escu.fields_required = ["dest_port", "src"] -action.escu.earliest_time_offset = 7200 -action.escu.latest_time_offset = 7200 -description = This search will return information about the process associated with observed network traffic to a specific destination port from a specific host. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | from datamodel Application_State.Ports | search dest_port={dest_port} dest={src} | table dest dest_port process process_name - -[ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-04-26 -action.escu.modification_date = 2018-05-07 -action.escu.asset_at_risk = AWS Instance -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule -action.escu.mappings = {"mitre_attack": ["Exfiltration", "Command and Control"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "cis20": ["CIS 11"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command and Control"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in blocked Outbound Traffic from your AWS -action.notable = 1 -action.notable.param.nes_fields = src_ip -action.notable.param.rule_description = A spike in the blocked outbound connection is detected from source $src_ip$. -action.notable.param.rule_title = Spike in blocked outbound network connections from $src_ip$ detected. -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws: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) [search sourcetype=aws: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) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip - -[ESCU - Reg.exe used to hide files/directories via registry keys - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-10-27 -action.escu.modification_date = 2017-10-30 -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Reg.exe used to hide files/directories via registry keys - Rule -action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} -action.escu.known_false_positives = None at the moment -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Persistence Techniques", "Suspicious Windows Registry Activities"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Reg.exe used to hide files/directories via registry keys -action.notable = 1 -action.notable.param.nes_fields = dest, process, cmdline -action.notable.param.rule_description = Regedit.exe is used by attackers to hide malware files/directories in windows environments via registry key settings. This rule detects command-line arguments used to hide a file/directory -action.notable.param.rule_title = Regedit.exe used to hide a file/directory on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Registry Activities\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,cmdline -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = The search looks for command-line arguments used to hide a file or directory using the reg add command. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational add Hidden REG_DWORD | search process=*reg.exe cmdline=*add* cmdline=*Hidden* cmdline=*REG_DWORD* | regex cmdline= "(/d\s+2)" | stats count values(cmdline) min(_time) as firstTime max(_time) as lastTime by dest process | `ctime(firstTime)` |`ctime(lastTime)` - [ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] action.escu = 0 action.escu.enabled = 1 @@ -235,296 +96,6 @@ schedule_window = auto is_visible = false search = sourcetype="WinEventLog:Security" (EventCode=4624 OR EventCode=4625) Logon_Process=NtLmSsp Logon_Type=3 Account_Name !="ANONYMOUS LOGON" Key_Length=0 | table _time Source_Network_Address Account_Name Account_Domain ComputerName Workstation_Name -[ESCU - Detect Long DNS TXT Record Response - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-06-18 -action.escu.modification_date = 2017-09-18 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Network_Resolution"] -action.escu.full_search_name = ESCU - Detect Long DNS TXT Record Response - Rule -action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 8", "CIS 12", "CIS 13"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Long DNS TXT Record Response -action.notable = 1 -action.notable.param.nes_fields = src, query -action.notable.param.rule_description = A DNS TXT record response of over 100 characters was detected. -action.notable.param.rule_title = Long DNS TXT Record Response -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `ctime(firstTime)` | `ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" - -[ESCU - Web Fraud - Password Sharing Across Accounts - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-07-12 -action.escu.modification_date = 2018-10-08 -action.escu.asset_at_risk = account -action.escu.channel = webfraud -action.escu.confidence = medium -action.escu.eli5 = 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 -action.escu.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. -action.escu.full_search_name = ESCU - Web Fraud - Password Sharing Across Accounts - Rule -action.escu.mappings = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Bro"] -action.escu.analytic_story = ["Web Fraud Detection"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Web Fraud - Password Sharing Across Accounts -action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = This search is used to identify user accounts, $user$, that share common passwords -action.notable.param.rule_title = Web Fraud Detection: Password Sharing Across Accounts -action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Emails From Specific Sender\n - ESCU - Get Web Session Information via session_id\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = other -action.risk.param._risk_score = 10 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 3600s -cron_schedule = 0 * * * * -description = This search is used to identify user accounts that share a common password. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=stream:http http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?[^&|^$]+)" | rex field=form_data "login\[password\]=(?[^&|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 - -[ESCU - TOR Traffic - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-08-21 -action.escu.modification_date = 2017-09-11 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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'. -action.escu.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. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - TOR Traffic - Rule -action.escu.mappings = {"mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE"]} -action.escu.known_false_positives = None at this time -action.escu.search_type = detection -action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] -action.escu.analytic_story = ["Ransomware", "Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = TOR Traffic -action.notable = 1 -action.notable.param.nes_fields = src_ip, dest_ip -action.notable.param.rule_description = Network traffic accessing TOR detected from $src_ip$ -action.notable.param.rule_title = TOR Network Traffic Allowed from $src_ip$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 28800s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` - -[ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-10-09 -action.escu.modification_date = 2018-11-02 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.known_false_positives = Some legitimate applications may exhibit this behavior. -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Emotet Malware (TA18-201A)", "Suspicious Command-Line Executions"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Use of cmd.exe to Launch Script Interpreters -action.notable = 1 -action.notable.param.nes_fields = dest, process_name, parent_process -action.notable.param.rule_description = Potentially malicious script execution detected. -action.notable.param.rule_title = Command prompt is executing scripts on $dest$ using $process_name$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process_name -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = 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 -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `ctime(firstTime)`|`ctime(lastTime)` - -[ESCU - Investigate Web POSTs From src] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-12-06 -action.escu.modification_date = 2018-12-06 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model. -action.escu.data_models = ["Web"] -action.escu.full_search_name = ESCU - Investigate Web POSTs From src -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.analytic_story = ["Apache Struts Vulnerability"] -action.escu.fields_required = ["src"] -action.escu.earliest_time_offset = 3600 -action.escu.latest_time_offset = 3600 -description = This investigative search retrieves POST requests from a specified source IP or hostname. Identifying the POST requests, as well as their associated destination URLs and user agent(s), may help you scope and characterize the suspicious traffic. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| where like(src, "{src}") and like(http_method, "POST") - -[ESCU - Monitor Successful Backups] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-08-24 -action.escu.modification_date = 2017-09-12 -action.escu.channel = ESCU -action.escu.eli5 = This search gives you the count and the hostname of all the systems that had a successful backup each day. -action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs. -action.escu.full_search_name = ESCU - Monitor Successful Backups -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Netbackup"] -action.escu.analytic_story = ["SamSam Ransomware", "Monitor Backup Solution", "Ransomware"] -description = 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. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype="netbackup_logs" "Disk/Partition backup completed successfully." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE - -[ESCU - Get Logon Rights Modifications For Endpoint] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-08-16 -action.escu.modification_date = 2017-09-12 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting your Windows event logs -action.escu.full_search_name = ESCU - Get Logon Rights Modifications For Endpoint -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Account Monitoring and Controls"] -action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 86400 -action.escu.latest_time_offset = 86400 -description = This search allows you to retrieve any modifications to logon rights associated with a specific host. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | search sourcetype=WinEventLog:Security (EventCode=4718 OR EventCode=4717) dest={dest} | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature - -[ESCU - Baseline of Security Group Activity by ARN] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-04-17 -action.escu.modification_date = 2018-04-17 -action.escu.channel = ESCU -action.escu.eli5 = 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. -action.escu.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 `securityGroupAPIs`. -action.escu.full_search_name = ESCU - Baseline of Security Group Activity by ARN -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS User Monitoring"] -description = 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. -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail `securityGroupAPIs` | 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 - Create or delete hidden shares using net.exe - Rule] action.escu = 0 action.escu.enabled = 1 @@ -575,6 +146,387 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search (process=*share* OR process=*delete*)| regex process="\S+[$]" +[ESCU - Reg.exe used to hide files/directories via registry keys - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-10-27 +action.escu.modification_date = 2017-10-30 +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Reg.exe used to hide files/directories via registry keys - Rule +action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} +action.escu.known_false_positives = None at the moment +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Defense Evasion Tactics"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Reg.exe used to hide files/directories via registry keys +action.notable = 1 +action.notable.param.nes_fields = dest, process, cmdline +action.notable.param.rule_description = Regedit.exe is used by attackers to hide malware files/directories in windows environments via registry key settings. This rule detects command-line arguments used to hide a file/directory +action.notable.param.rule_title = Regedit.exe used to hide a file/directory on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,cmdline +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = The search looks for command-line arguments used to hide a file or directory using the reg add command. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational add Hidden REG_DWORD | search process=*reg.exe cmdline=*add* cmdline=*Hidden* cmdline=*REG_DWORD* | regex cmdline= "(/d\s+2)" | stats count values(cmdline) min(_time) as firstTime max(_time) as lastTime by dest process | `ctime(firstTime)` |`ctime(lastTime)` + +[ESCU - Get Process Information For Port Activity] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-06-25 +action.escu.modification_date = 2017-09-10 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events. +action.escu.data_models = ["Application_State"] +action.escu.full_search_name = ESCU - Get Process Information For Port Activity +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +action.escu.analytic_story = ["Ransomware", "Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch", "SamSam Ransomware", "Use of Cleartext Protocols"] +action.escu.fields_required = ["dest_port", "src"] +action.escu.earliest_time_offset = 7200 +action.escu.latest_time_offset = 7200 +description = This search will return information about the process associated with observed network traffic to a specific destination port from a specific host. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | from datamodel Application_State.Ports | search dest_port={dest_port} dest={src} | table dest dest_port process process_name + +[ESCU - TOR Traffic - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-08-21 +action.escu.modification_date = 2017-09-11 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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'. +action.escu.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. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - TOR Traffic - Rule +action.escu.mappings = {"mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE"]} +action.escu.known_false_positives = None at this time +action.escu.search_type = detection +action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] +action.escu.analytic_story = ["Ransomware", "Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = TOR Traffic +action.notable = 1 +action.notable.param.nes_fields = src_ip, dest_ip +action.notable.param.rule_description = Network traffic accessing TOR detected from $src_ip$ +action.notable.param.rule_title = TOR Network Traffic Allowed from $src_ip$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Information For Port Activity\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src_ip +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 40 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = src_ip +alert.suppress.period = 28800s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` + +[ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-10-09 +action.escu.modification_date = 2018-11-02 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Detect Use of cmd.exe to Launch Script Interpreters - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.known_false_positives = Some legitimate applications may exhibit this behavior. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Emotet Malware (TA18-201A)"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect Use of cmd.exe to Launch Script Interpreters +action.notable = 1 +action.notable.param.nes_fields = dest, process_name, parent_process +action.notable.param.rule_description = Potentially malicious script execution detected. +action.notable.param.rule_title = Command prompt is executing scripts on $dest$ using $process_name$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest, process_name +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = 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 +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="*cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `ctime(firstTime)`|`ctime(lastTime)` + +[ESCU - Investigate Web POSTs From src] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-12-06 +action.escu.modification_date = 2018-12-06 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model. +action.escu.data_models = ["Web"] +action.escu.full_search_name = ESCU - Investigate Web POSTs From src +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +action.escu.analytic_story = ["Apache Struts Vulnerability"] +action.escu.fields_required = ["src"] +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 3600 +description = This investigative search retrieves POST requests from a specified source IP or hostname. Identifying the POST requests, as well as their associated destination URLs and user agent(s), may help you scope and characterize the suspicious traffic. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| where like(src, "{src}") and like(http_method, "POST") + +[ESCU - Detect Long DNS TXT Record Response - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-06-18 +action.escu.modification_date = 2017-09-18 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Network_Resolution"] +action.escu.full_search_name = ESCU - Detect Long DNS TXT Record Response - Rule +action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 8", "CIS 12", "CIS 13"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect Long DNS TXT Record Response +action.notable = 1 +action.notable.param.nes_fields = src, query +action.notable.param.rule_description = A DNS TXT record response of over 100 characters was detected. +action.notable.param.rule_title = Long DNS TXT Record Response +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 70 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = src +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `ctime(firstTime)` | `ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" + +[ESCU - Get Logon Rights Modifications For Endpoint] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-08-16 +action.escu.modification_date = 2017-09-12 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search you must be ingesting your Windows event logs +action.escu.full_search_name = ESCU - Get Logon Rights Modifications For Endpoint +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Account Monitoring and Controls"] +action.escu.fields_required = ["dest"] +action.escu.earliest_time_offset = 86400 +action.escu.latest_time_offset = 86400 +description = This search allows you to retrieve any modifications to logon rights associated with a specific host. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | search sourcetype=WinEventLog:Security (EventCode=4718 OR EventCode=4717) dest={dest} | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature + +[ESCU - Identify Systems Creating Remote Desktop Traffic] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-04-24 +action.escu.modification_date = 2017-09-15 +action.escu.channel = ESCU +action.escu.eli5 = 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. +action.escu.how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Identify Systems Creating Remote Desktop Traffic +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["Lateral Movement"] +description = This search counts the numbers of times the system has generated remote desktop traffic. +dispatch.earliest_time = -30d@d +dispatch.latest_time = -10m@m +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `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 - Baseline of Security Group Activity by ARN] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-04-17 +action.escu.modification_date = 2018-04-17 +action.escu.channel = ESCU +action.escu.eli5 = 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. +action.escu.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 `securityGroupAPIs`. +action.escu.full_search_name = ESCU - Baseline of Security Group Activity by ARN +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS User Monitoring"] +description = 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. +dispatch.earliest_time = -90d@d +dispatch.latest_time = -10m@m +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail `securityGroupAPIs` | 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 - Detect Spike in blocked Outbound Traffic from your AWS - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-04-26 +action.escu.modification_date = 2018-05-07 +action.escu.asset_at_risk = AWS Instance +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule +action.escu.mappings = {"mitre_attack": ["Exfiltration", "Command and Control"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "cis20": ["CIS 11"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Command and Control", "AWS Network ACL Activity", "Suspicious AWS Traffic"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect Spike in blocked Outbound Traffic from your AWS +action.notable = 1 +action.notable.param.nes_fields = src_ip +action.notable.param.rule_description = A spike in the blocked outbound connection is detected from source $src_ip$. +action.notable.param.rule_title = Spike in blocked outbound network connections from $src_ip$ detected. +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src_ip +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 30 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = src_ip +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws: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) [search sourcetype=aws: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) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip + [ESCU - Detect processes used for System Network Configuration Discovery - Rule] action.escu = 0 action.escu.enabled = 1 @@ -640,7 +592,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion"], "kill_chain_phases" action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "AWS Cryptomining"] action.correlationsearch.enabled = 1 action.correlationsearch.label = EC2 Instance Started In Previously Unseen Region action.notable = 1 @@ -649,7 +601,7 @@ action.notable.param.rule_description = An AWS instance is started in a new, pre action.notable.param.rule_title = AWS instance is started in a new region action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Instance Details by instanceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Investigate AWS activities via region name\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = awsRegion @@ -834,7 +786,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "PowerShell", "Scripting"] action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Malicious PowerShell", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Malicious PowerShell"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Malicious PowerShell Process - Connect To Internet With Hidden Window action.notable = 1 @@ -843,7 +795,7 @@ action.notable.param.rule_description = The system $dest$ executed a PowerShell action.notable.param.rule_title = Malicious PowerShell Process detected on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -884,7 +836,7 @@ action.escu.mappings = {"mitre_attack": ["Command and Control", "Commonly Used P action.escu.known_false_positives = None identified action.escu.search_type = detection action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] -action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Protocol or Port Mismatch action.notable = 1 @@ -893,7 +845,7 @@ action.notable.param.rule_description = This search looks for network traffic on action.notable.param.rule_title = Protocol / Port Mismatch from $src_ip$ action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Information For Port Activity\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src_ip @@ -956,7 +908,7 @@ action.escu.mappings = {"mitre_attack": ["Persistence", "Change Default File Ass action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows File Extension and Association Abuse", "Suspicious Windows Registry Activities"] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows File Extension and Association Abuse"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Suspicious Changes to File Associations action.notable = 1 @@ -965,7 +917,7 @@ action.notable.param.rule_description = The system $dest$ had an unusual change action.notable.param.rule_title = Suspicious File Association Change on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Registry Activities\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -990,54 +942,27 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) EventCode=13 object_path=*\\Explorer\\FileExts* process!=Explorer.exe AND process!=OpenWith.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, process, object_path, Details | rename Details as value | `ctime(firstTime)`| `ctime(lastTime)` -[ESCU - Detect S3 access from a new IP - Rule] +[ESCU - Baseline of Network ACL Activity by ARN] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-06-25 -action.escu.modification_date = 2018-06-28 -action.escu.asset_at_risk = S3 Bucket +action.escu.creation_date = 2018-05-21 +action.escu.modification_date = 2018-05-21 action.escu.channel = ESCU -action.escu.confidence = low -action.escu.eli5 = 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 -action.escu.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. -action.escu.full_search_name = ESCU - Detect S3 access from a new IP - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13", "CIS 14"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -action.escu.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 -action.escu.search_type = detection +action.escu.eli5 = 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. +action.escu.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 `NetworkACLEvents`. +action.escu.full_search_name = ESCU - Baseline of Network ACL Activity by ARN +action.escu.known_false_positives = None at this time +action.escu.search_type = support action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect S3 access from a new IP -action.notable = 1 -action.notable.param.nes_fields = bucket_name, src_ip -action.notable.param.rule_description = A remote IP, $src_ip$, has made a successful connection with an S3 $bucket_name$. -action.notable.param.rule_title = S3 bucket $bucketName$ was accessed by a new $src_ip$ -action.notable.param.security_domain = network -action.notable.param.severity = low -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = bucket_name, src_ip -alert.suppress.period = 86400s -cron_schedule = 5 * * * * -description = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. -dispatch.earliest_time = -70m@m +action.escu.analytic_story = ["AWS Network ACL Activity"] +description = 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. +dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:s3:accesslogs http_status=200 [search sourcetype=aws:s3:accesslogs http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | convert ctime(firstTime) ctime(lastTime) | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri +search = sourcetype=aws:cloudtrail `NetworkACLEvents` | 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 - Remote Desktop Process Running On System - Rule] action.escu = 0 @@ -1138,44 +1063,65 @@ schedule_window = auto is_visible = false search = sourcetype=wineventlog:security EventCode=4720 OR (EventCode=4732 Group_Name= Administrators) | transaction Security_ID maxspan=180m | search EventCode=4720 EventCode=4732 | table _time user dest EventCode Security_ID Group_Name src_user Message -[ESCU - Remote Desktop Network Bruteforce - Rule] +[ESCU - Detect Spike in S3 Bucket deletion - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-12-14 -action.escu.modification_date = 2018-12-14 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-07-17 +action.escu.modification_date = 2018-11-27 +action.escu.asset_at_risk = S3 Bucket action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Remote Desktop Network Bruteforce - Rule -action.escu.mappings = {"mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "cis20": ["CIS 12", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} -action.escu.known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Detect Spike in S3 Bucket deletion - Rule +action.escu.mappings = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. action.escu.search_type = detection -action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["SamSam Ransomware"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS S3 Activities"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote Desktop Network Bruteforce +action.correlationsearch.label = Detect Spike in S3 Bucket deletion action.notable = 1 -action.notable.param.nes_fields = dest, src -action.notable.param.rule_description = Remote-desktop traffic detected from $src$ to $dest$. This activity is consistent with a brute-force attack. -action.notable.param.rule_title = Bruteforce Remote Desktop Network Traffic detected from $src$ to $dest$ +action.notable.param.nes_fields = user +action.notable.param.rule_description = A spike in the number of S3 buckets deleted by $user$ was detected. +action.notable.param.rule_title = Spike detected in S3 bucket deletion activity by $user$. action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 75 +action.risk.param._risk_object = user +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,src -alert.suppress.period = 28800s +alert.suppress.fields = user +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = 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. +description = 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. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -1186,7 +1132,72 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50| where count>(stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev +search = sourcetype=aws:cloudtrail eventName=DeleteBucket [search sourcetype=aws:cloudtrail eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user + +[ESCU - DNS record changed - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2019-02-14 +action.escu.modification_date = 2019-02-14 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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 Investigation" 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/4.1/playbook/dns-hijack-investigation/`).\ +\ + +action.escu.data_models = ["Network_Resolution"] +action.escu.full_search_name = ESCU - DNS record changed - Rule +action.escu.mappings = {"mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["DNS Hijacking"] +action.runphantomplaybook = 1 +action.runphantomplaybook.param.phantom_server = automation (hostname) +action.runphantomplaybook.param.playbook_name = community/dns_hijack_investigation +action.runphantomplaybook.param.playbook_display_name = DNS record change investigation +action.runphantomplaybook.param.playbook_url = https://my.phantom.us/4.1/playbook/dns-hijack-investigation/ +action.runphantomplaybook.param.sensitivity = green +action.runphantomplaybook.param.severity = medium +action.correlationsearch.enabled = 1 +action.correlationsearch.label = DNS record changed +action.notable = 1 +action.notable.param.nes_fields = src +action.notable.param.rule_description = The table represents a list of DNS records and their responses for corporate domains that have recently changed +action.notable.param.rule_title = DNS record changed +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|runphantomplaybook]]: Phantom playbook recommendations:\nSplunk>Phantom Response Playbook - Monitor enrichment of the Splunk>Phantom Playbook called DNS record change investigation and answer any analyst prompt in Mission Control with a response decision. Link to the playbook https://my.phantom.us/4.1/playbook/dns-hijack-investigation/\n2. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n3. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get History Of Email Sources\n - ESCU - Get Process responsible for the DNS traffic\n - ESCU - Investigate Web Activity From src_ip\n"} +action.notable.param.recommended_actions = runphantomplaybook, escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 40 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = src +alert.suppress.period = 28800s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | inputlookup discovered_dns_records.csv | rename answer as discovered_answer | join domain[|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as current_answer values(DNS.src) as src 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,src,domain,type,query,current_answer,discovered_answer | makemv current_answer | mvexpand current_answer | makemv discovered_answer | eval n=mvfind(discovered_answer, current_answer) | where isnull(n) [ESCU - Web Fraud - Anomalous User Clickspeed - Rule] action.escu = 0 @@ -1237,44 +1248,68 @@ schedule_window = auto is_visible = false search = sourcetype=stream:http http_content_type=text* | rex field=cookie "form_key=(?\w+)" | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) -[ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] +[ESCU - Discover DNS records] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-10-04 -action.escu.modification_date = 2017-09-23 -action.escu.asset_at_risk = Web Server +action.escu.creation_date = 2019-02-14 +action.escu.modification_date = 2019-02-14 +action.escu.channel = ESCU +action.escu.eli5 = 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` +action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic 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 +action.escu.data_models = ["Network_Resolution"] +action.escu.full_search_name = ESCU - Discover DNS records +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["DNS Hijacking"] +description = 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_Traffic` datamodel, then stores the output under the `discovered_dns_records.csv` lookup +dispatch.earliest_time = -30d@d +dispatch.latest_time = -10m@m +disabled=true +realtime_schedule = 0 +schedule_window = auto +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 summariesonly=true 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 - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-03-16 +action.escu.modification_date = 2018-03-16 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = This search returns the number of times a URL associated with this type of JexBoss probe is observed. -action.escu.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. -action.escu.data_models = ["Web"] -action.escu.full_search_name = ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule -action.escu.mappings = {"mitre_attack": ["Discovery", "System Information Discovery"], "kill_chain_phases": ["Reconnaissance"]} -action.escu.known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule +action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +action.escu.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. action.escu.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] -action.escu.analytic_story = ["SamSam Ransomware", "JBoss Vulnerability"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect attackers scanning for vulnerable JBoss servers +action.correlationsearch.label = AWS Cloud Provisioning From Previously Unseen IP Address action.notable = 1 -action.notable.param.nes_fields = -action.notable.param.rule_description = This search looks for specific GET/HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. -action.notable.param.rule_title = Detect attackers scanning for vulnerable JBoss servers -action.notable.param.security_domain = network +action.notable.param.nes_fields = src_ip +action.notable.param.rule_description = Your AWS infrastructure was provisioned from an IP, $src_ip$, which has never before been seen provisioning your infrastructure. +action.notable.param.rule_title = AWS Provision Activity From $src_ip$ +action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From City\n - ESCU - Get All AWS Activity From Country\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get All AWS Activity From Region\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest +action.risk.param._risk_object = src_ip action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,url -alert.suppress.period = 86400s +alert.suppress.fields = src_ip +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = 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. +description = 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." dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -1285,57 +1320,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `ctime(firstTime)` | `ctime(lastTime)` - -[ESCU - SMB Traffic Spike - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-08-20 -action.escu.modification_date = 2017-09-10 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - SMB Traffic Spike - Rule -action.escu.mappings = {"mitre_attack": ["Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} -action.escu.known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. -action.escu.search_type = detection -action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["Hidden Cobra Malware", "DHS Report TA18-074A", "Ransomware", "Emotet Malware (TA18-201A)"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = SMB Traffic Spike -action.notable = 1 -action.notable.param.nes_fields = src -action.notable.param.rule_description = There was a spike in SMB traffic from $src$. -action.notable.param.rule_title = SMB Traffic Spike from $src$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src -alert.suppress.period = 28800s -cron_schedule = 0 * * * * -description = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. -dispatch.earliest_time = -7d@d -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `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=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count +search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode [ESCU - Samsam Test File Write - Rule] action.escu = 0 @@ -1399,7 +1384,7 @@ action.escu.full_search_name = ESCU - Investigate AWS activities via region name action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS S3 Activities", "Suspicious AWS EC2 Activities"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "Suspicious AWS S3 Activities"] action.escu.fields_required = ["awsRegion"] action.escu.earliest_time_offset = 14400 action.escu.latest_time_offset = 0 @@ -1410,6 +1395,29 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:cloudtrail awsRegion={awsRegion}| rename requestParameters.instancesSet.items{}.instanceId as instanceId| stats values(eventName) by userName instanceId +[ESCU - Get EC2 Instance Details by instanceId] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-02-12 +action.escu.modification_date = 2018-02-12 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.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. +action.escu.full_search_name = ESCU - Get EC2 Instance Details by instanceId +action.escu.known_false_positives = None at this time +action.escu.search_type = contextual +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "Unusual AWS EC2 Modifications"] +action.escu.fields_required = ["instanceId"] +action.escu.earliest_time_offset = 86400 +action.escu.latest_time_offset = 0 +description = This search queries AWS description logs and returns all the information about a specific instance via the instanceId field +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | search sourcetype="aws:description" source="*:ec2_instances"| dedup id sortby -_time | search id={instanceId} | spath output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC + [ESCU - AWS Network Access Control List Created with All Open Ports - Rule] action.escu = 0 action.escu.enabled = 1 @@ -1508,6 +1516,56 @@ schedule_window = auto is_visible = false search = sourcetype=wineventlog:security EventCode=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process | `ctime(firstTime)`| `ctime(lastTime)` +[ESCU - Prohibited Network Traffic Allowed - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-04-18 +action.escu.modification_date = 2017-09-11 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Prohibited Network Traffic Allowed - Rule +action.escu.mappings = {"mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "kill_chain_phases": ["Delivery", "Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE", "PR.AC"]} +action.escu.known_false_positives = None identified +action.escu.search_type = detection +action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] +action.escu.analytic_story = ["Ransomware", "Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Prohibited Network Traffic Allowed +action.notable = 1 +action.notable.param.nes_fields = src_ip, dest_ip +action.notable.param.rule_description = This search looks for network traffic defined by port and transport in the ES lookup table "lookup_interesting_ports", that is marked as prohibited, and yet has an 'allow' action in the Network_Traffic data model. This should help to identify areas where a network device is not properly configured. +action.notable.param.rule_title = Prohibited Network Traffic Allowed from $src_ip$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Information For Port Activity\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src_ip +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 40 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest_ip,src_ip +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` + [ESCU - Detect Unauthorized Assets by MAC address - Rule] action.escu = 0 action.escu.enabled = 1 @@ -1608,104 +1666,51 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Vulnerabilities where Vulnerabilities.cve ="CVE-2017-5753" OR Vulnerabilities.cve ="CVE-2017-5715" OR Vulnerabilities.cve ="CVE-2017-5754" by Vulnerabilities.dest -[ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule] +[ESCU - Get Notable History] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-04-25 -action.escu.modification_date = 2018-12-03 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2017-03-15 +action.escu.modification_date = 2017-09-20 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -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 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. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -action.escu.known_false_positives = These characters might be legitimately on the command-line, but it is not common. -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Malicious PowerShell"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Malicious PowerShell Process With Obfuscation Techniques -action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name, process -action.notable.param.rule_description = The system $dest$ executed a PowerShell process that has evidence of obfuscation on the command-line -action.notable.param.rule_title = PowerShell process with an obfuscation techniques detected on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,process_name,process -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.eli5 = none +action.escu.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. +action.escu.full_search_name = ESCU - Get Notable History +action.escu.known_false_positives = None at this time +action.escu.search_type = contextual +action.escu.providing_technologies = ["Splunk Enterprise Security"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Windows Service Abuse", "Host Redirection", "Suspicious AWS EC2 Activities", "DHS Report TA18-074A", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "ColdRoot MacOS RAT", "Monitor Backup Solution", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Unusual Processes", "DNS Amplification Attacks", "Suspicious WMI Use", "Asset Tracking", "Ransomware", "Command and Control", "Suspicious AWS Login Activities", "Windows Privilege Escalation", "Windows Log Manipulation", "Windows File Extension and Association Abuse", "AWS Network ACL Activity", "Prohibited Traffic Allowed or Protocol Mismatch", "Brand Monitoring", "SamSam Ransomware", "Data Protection", "Router & Infrastructure Security", "Malicious PowerShell", "Disabling Security Tools", "SQL Injection", "Windows Defense Evasion Tactics", "Emotet Malware (TA18-201A)", "Hidden Cobra Malware", "Suspicious Emails", "JBoss Vulnerability", "Suspicious DNS Traffic", "Spectre And Meltdown Vulnerabilities", "Credential Dumping", "Suspicious AWS Traffic", "Collection and Staging", "Netsh Abuse", "Account Monitoring and Controls", "Suspicious AWS S3 Activities", "Monitor for Updates", "Use of Cleartext Protocols", "Lateral Movement", "Dynamic DNS", "AWS User Monitoring", "Web Fraud Detection", "DNS Hijacking", "Splunk Enterprise Vulnerability", "Unusual AWS EC2 Modifications", "AWS Cross Account Activity", "Apache Struts Vulnerability", "Suspicious MSHTA Activity", "Splunk Enterprise Vulnerability CVE-2018-11409", "Monitor for Unauthorized Software"] +action.escu.fields_required = ["dest"] +action.escu.earliest_time_offset = 864000 +action.escu.latest_time_offset = 86400 +description = This search queries the notable index and returns all the Notable Events for the particular destination host, giving the analyst an overview of the incidents that may have occurred with the host under investigation. disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process, "`"))-1) + (mvcount(split(process, "^"))-1) | search num_obfuscation > 0 +search = | search `notable` | search dest={dest} | table _time, rule_name, owner, priority, severity, status_description -[ESCU - EC2 Instance Started With Previously Unseen AMI - Rule] +[ESCU - Get Parent Process Info] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-12 -action.escu.modification_date = 2018-03-12 -action.escu.asset_at_risk = AWS Instance +action.escu.creation_date = 2017-08-22 +action.escu.modification_date = 2017-09-10 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen AMI - Rule -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cryptomining"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Started With Previously Unseen AMI -action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The EC2 instance $dest$ was created with previously unused AMI $amiID$ -action.notable.param.rule_title = EC2 Instance Type $dest$ Created With New AMI -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for EC2 instances being created with previously unseen AMIs. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data via Microsoft-Windows-Sysmon and extract the Image and Parent Image field. +action.escu.full_search_name = ESCU - Get Parent Process Info +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Windows Service Abuse", "DHS Report TA18-074A", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Ransomware", "Windows Privilege Escalation", "Windows File Extension and Association Abuse", "SamSam Ransomware", "Disabling Security Tools", "Windows Defense Evasion Tactics", "Emotet Malware (TA18-201A)", "Hidden Cobra Malware", "Credential Dumping", "Collection and Staging", "Netsh Abuse", "Suspicious MSHTA Activity"] +action.escu.fields_required = ["process", "dest"] +action.escu.earliest_time_offset = 0 +action.escu.latest_time_offset = 86400 +description = This search queries the Application State data model to give you details about the parent process of a process running on a host which is under investigation. Enter the values of the process name in question and the dest_ip disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(earliest) as earliest max(latest) as latest by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | convert ctime(earliest) ctime(latest) | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table _time, arn, amiID, dest, instanceType +search = | search sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational process={process} dest={dest} | table parent_process parent_process_id [ESCU - Processes launching netsh - Rule] action.escu = 0 @@ -1723,7 +1728,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface", action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Netsh Abuse", "DHS Report TA18-074A", "Disabling Security Tools"] +action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Netsh Abuse"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Processes launching netsh action.notable = 1 @@ -1732,7 +1737,7 @@ action.notable.param.rule_description = A process detected on $dest$ is launchin action.notable.param.rule_title = Process launching netsh.exe detected on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -1869,7 +1874,7 @@ action.escu.full_search_name = ESCU - Previously Seen EC2 Launches By User action.escu.known_false_positives = None at this time action.escu.search_type = support action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "AWS Cryptomining"] description = This search builds a table of previously seen ARNs that have launched a EC2 instance. dispatch.earliest_time = -90d@d dispatch.latest_time = -10m@m @@ -1879,54 +1884,27 @@ schedule_window = auto is_visible = false search = sourcetype=aws: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 - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] +[ESCU - Monitor Successful Backups] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-08-28 -action.escu.modification_date = 2018-08-28 -action.escu.asset_at_risk = Windows +action.escu.creation_date = 2017-08-24 +action.escu.modification_date = 2017-09-12 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search looks for Windows Event Code 4663 (object access), where the process performing the access is PowerShell.exe, the target process of the access is lsass.exe, and the access mask is given as 0x10. 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. -action.escu.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. -action.escu.full_search_name = ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule -action.escu.mappings = {"mitre_attack": ["Credential Access", "Credential Dumping"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Credential Dumping"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Mimikatz Via PowerShell And EventCode 4663 -action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = Possible attempt at credential dumping via PowerShell was detected on $dest$ by $user$. -action.notable.param.rule_title = Event ID 4663 Specifying PowerShell Reading From LSASS.exe Identified on $dest$. -action.notable.param.security_domain = access -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user, dest, process -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = This search looks for PowerShell reading lsass memory consistent with credential dumping. -dispatch.earliest_time = -70m@m +action.escu.eli5 = This search gives you the count and the hostname of all the systems that had a successful backup each day. +action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs. +action.escu.full_search_name = ESCU - Monitor Successful Backups +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["Netbackup"] +action.escu.analytic_story = ["Monitor Backup Solution", "Ransomware", "SamSam Ransomware"] +description = 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. +dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=wineventlog:security EventCode=4663 Process_Name=*powershell.exe Object_Name=*lsass.exe Access_Mask=0x10 | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, Process_ID, Message | rename Process_Name as process | `ctime(firstTime)`| `ctime(lastTime)` +search = sourcetype="netbackup_logs" "Disk/Partition backup completed successfully." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE [ESCU - Suspicious Reg.exe Process - Rule] action.escu = 0 @@ -1943,7 +1921,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Modify Registry", " action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Windows Defense Evasion Tactics", "Disabling Security Tools"] +action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Windows Defense Evasion Tactics"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Suspicious Reg.exe Process action.notable = 1 @@ -1952,7 +1930,7 @@ action.notable.param.rule_description = The system $dest$ had reg.exe process ru action.notable.param.rule_title = Suspicious reg.exe process detected on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -1993,7 +1971,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "PowerShell", "Scripting"] action.escu.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" as this search is designed to identify would be unusual. Hits should be reviewed and investigated as appropriate. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Credential Dumping", "Malicious PowerShell"] +action.escu.analytic_story = ["Malicious PowerShell", "Credential Dumping"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Attempt To Set Default PowerShell Execution Policy To Unrestricted action.notable = 1 @@ -2002,7 +1980,7 @@ action.notable.param.rule_description = An attempt to modify the default PowerSh action.notable.param.rule_title = Attempt To Set PowerShell Execution Policy To "Unrestricted" On $dest$. action.notable.param.security_domain = endpoint action.notable.param.severity = High -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -2197,30 +2175,6 @@ schedule_window = auto is_visible = false search = sourcetype=aws:cloudtrail userIdentity.sessionContext.attributes.mfaAuthenticated=false | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity as user]| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) by userIdentity.arn userIdentity.type user | `ctime(firstTime)` | `ctime(lastTime)` -[ESCU - Investigate Web Activity From Host] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-04-21 -action.escu.modification_date = 2017-11-09 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting your web traffic and populating the Web data model. -action.escu.data_models = ["Web"] -action.escu.full_search_name = ESCU - Investigate Web Activity From Host -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.analytic_story = ["Unusual Processes", "Netsh Abuse", "SamSam Ransomware", "Orangeworm Attack Group", "Ransomware", "Emotet Malware (TA18-201A)", "Host Redirection", "Suspicious Command-Line Executions", "Brand Monitoring", "Credential Dumping", "Suspicious Emails", "Monitor for Unauthorized Software"] -action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 3600 -action.escu.latest_time_offset = 3600 -description = This search allows you to find all the web activity from a specific host. During an investigation, it is important to profile web activity to characterize user or host activity. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | from datamodel Web.Web | search src={dest} - [ESCU - Previously Seen Running Windows Services] action.escu = 0 action.escu.enabled = 1 @@ -2259,7 +2213,7 @@ action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration", action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Suspicious DNS Traffic", "DNS Hijacking", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic", "DNS Hijacking"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Clients Connecting to Multiple DNS Servers action.notable = 1 @@ -2268,7 +2222,7 @@ action.notable.param.rule_description = This search allows you to identify the e action.notable.param.rule_title = Client $src$ Connecting to Multiple DNS Servers action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get History Of Email Sources\n - ESCU - Get Process responsible for the DNS traffic\n - ESCU - Investigate Web Activity From src_ip\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -2332,7 +2286,7 @@ action.escu.mappings = {"mitre_attack": ["Persistence", "Privilege Escalation", action.escu.known_false_positives = You will encounter noise from legitimate print-monitor registry entries. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Windows Persistence Techniques", "Suspicious Windows Registry Activities"] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Monitor Registry Keys for Print Monitors action.notable = 1 @@ -2341,7 +2295,7 @@ action.notable.param.rule_description = A registry key associated with adding pr action.notable.param.rule_title = Registry Key changes for Print Monitors detected on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Registry Activities\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -2366,51 +2320,128 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.status Registry.user Registry.registry_path Registry.action | `drop_dm_object_name(Registry)` -[ESCU - Identify Systems Creating Remote Desktop Traffic] +[ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-04-24 -action.escu.modification_date = 2017-09-15 +action.escu.creation_date = 2017-10-07 +action.escu.modification_date = 2018-11-15 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = 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. -action.escu.how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Identify Systems Creating Remote Desktop Traffic -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Lateral Movement"] -description = This search counts the numbers of times the system has generated remote desktop traffic. -dispatch.earliest_time = -30d@d +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect Prohibited Applications Spawning cmd.exe +action.notable = 1 +action.notable.param.nes_fields = dest, process, parent_process +action.notable.param.rule_description = A prohibited application from prohibited_apps_launching_cmd.csv was leveraged to launch cmd.exe +action.notable.param.rule_title = Prohibited application($parent_process$) used to launch cmd.exe on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Registry Activities\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 80 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest, parent_process +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")` | sort - count +search = | tstats `summariesonly` count values(Processes.user) as user values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] -[ESCU - Get All AWS Activity From Region] +[ESCU - Detect USB device insertion - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-19 -action.escu.modification_date = 2018-03-19 +action.escu.creation_date = 2017-08-03 +action.escu.modification_date = 2017-11-27 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = low +action.escu.eli5 = 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 +action.escu.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. +action.escu.data_models = ["Change_Analysis"] +action.escu.full_search_name = ESCU - Detect USB device insertion - Rule +action.escu.mappings = {"mitre_attack": ["Exfiltration"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.PT", "PR.DS"]} +action.escu.known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. +action.escu.search_type = detection +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Data Protection"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect USB device insertion +action.notable = 1 +action.notable.param.nes_fields = dest +action.notable.param.rule_description = Read/Write attempt to a USB was detected on this host +action.notable.param.rule_title = Read/Write attempt to a USB detected on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = low +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 20 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `ctime(earliest)`| `ctime(latest)` + +[ESCU - Get Backup Logs For Endpoint] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-08-24 +action.escu.modification_date = 2017-09-14 action.escu.channel = ESCU action.escu.eli5 = none -action.escu.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. -action.escu.full_search_name = ESCU - Get All AWS Activity From Region +action.escu.how_to_implement = You must be ingesting your backup logs. +action.escu.full_search_name = ESCU - Get Backup Logs For Endpoint action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -action.escu.fields_required = ["Region"] -action.escu.earliest_time_offset = 14400 +action.escu.search_type = contextual +action.escu.providing_technologies = ["Netbackup"] +action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] +action.escu.fields_required = ["dest"] +action.escu.earliest_time_offset = 604800 action.escu.latest_time_offset = 0 -description = This search retrieves all the activity from a specific geographic region and will create a table containing the time, geographic region, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +description = This search will tell you the backup status from your netbackup_logs of a specific endpoint for the last week. disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Region={Region} | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Region, user, userName, userType, src_ip, awsRegion, eventName, errorCode +search = | search sourcetype="netbackup_logs" COMPUTERNAME={dest} | rename COMPUTERNAME as dest, MESSAGE as signature | table _time, dest, signature [ESCU - Get DNS Server History for a host] action.escu = 0 @@ -2424,7 +2455,7 @@ action.escu.full_search_name = ESCU - Get DNS Server History for a host action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Data Protection", "Dynamic DNS", "Suspicious DNS Traffic", "DNS Hijacking", "Host Redirection", "Brand Monitoring", "Command and Control"] +action.escu.analytic_story = ["Host Redirection", "Command and Control", "Brand Monitoring", "Data Protection", "Suspicious DNS Traffic", "Dynamic DNS", "DNS Hijacking"] action.escu.fields_required = ["src_ip"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 86400 @@ -2523,7 +2554,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Persistence", "Late action.escu.known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Persistence Techniques", "Suspicious Windows Registry Activities", "Lateral Movement"] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Defense Evasion Tactics", "Lateral Movement"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Remote Registry Key modifications action.notable = 1 @@ -2557,67 +2588,44 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.status, Registry.user | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(Registry)` -[ESCU - Previously Seen AWS Provisioning Activity Sources] +[ESCU - Abnormally High AWS Instances Launched by User - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-16 -action.escu.modification_date = 2018-03-16 +action.escu.creation_date = 2018-02-26 +action.escu.modification_date = 2018-02-26 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Previously Seen AWS Provisioning Activity Sources -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -description = 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. -dispatch.earliest_time = -90d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws: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 - Detect malicious requests to exploit JBoss servers - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2016-10-04 -action.escu.modification_date = 2017-09-23 -action.escu.asset_at_risk = Web Server -action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers. -action.escu.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 -action.escu.data_models = ["Web"] -action.escu.full_search_name = ESCU - Detect malicious requests to exploit JBoss servers - Rule -action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 12", "CIS 4", "CIS 18"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} -action.escu.known_false_positives = No known false positives for this detection. +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Abnormally High AWS Instances Launched by User - Rule +action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} +action.escu.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. action.escu.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] -action.escu.analytic_story = ["SamSam Ransomware", "JBoss Vulnerability"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "AWS Cryptomining"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect malicious requests to exploit JBoss servers +action.correlationsearch.label = Abnormally High AWS Instances Launched by User action.notable = 1 -action.notable.param.nes_fields = src, dest_ip -action.notable.param.rule_description = A search for detecting malicious requests made to exploit jmx-console in JBoss servers. The bad requests have a long url length since it serves the payload via the url -action.notable.param.rule_title = Detected malicious requests to exploit JBoss servers +action.notable.param.nes_fields = userName +action.notable.param.rule_description = An abnormally high number of instances were launched by a user within in a 10-minute window +action.notable.param.rule_title = High Number of instances launched by $userName$ action.notable.param.security_domain = network -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 +action.risk.param._risk_object = userName +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,url,src -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -70m@m +alert.suppress.fields = userName +alert.suppress.period = 3600s +cron_schedule = */10 * * * * +description = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. +dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -2627,7 +2635,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `ctime(firstTime)` | `ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime +search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev [ESCU - Email servers sending high volume traffic to hosts - Rule] action.escu = 0 @@ -2695,7 +2703,7 @@ action.escu.mappings = {"mitre_attack": ["Exfiltration", "Exfiltration Over Alte action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Excessive DNS Failures action.notable = 1 @@ -2704,7 +2712,7 @@ action.notable.param.rule_description = This search identifies DNS query failure action.notable.param.rule_title = Excessive DNS Failures action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -2729,6 +2737,56 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"| `drop_dm_object_name("DNS")`| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain| where isnull(domain)| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank| where isnull(rank)| stats sum(count) as count mode(queries) as queries by src| `get_asset(src)`| where count>50 +[ESCU - Attempt To Add Certificate To Untrusted Store - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-04-09 +action.escu.modification_date = 2018-11-15 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = high +action.escu.eli5 = 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. +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 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. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Attempt To Add Certificate To Untrusted Store - Rule +action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Disabling Security Tools"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Attempt To Add Certificate To Untrusted Store +action.notable = 1 +action.notable.param.nes_fields = dest, user, process_name +action.notable.param.rule_description = Attempt to add a certificate to the untrusted certificate store +action.notable.param.rule_title = Attempt To Add Certificate to Untrusted Store +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = process, dest +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = Attempt to add a certificate to the untrusted certificate store +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe (Processes.process=*-addstore* AND Processes.process=*disallowed* ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `ctime(firstTime)`|`ctime(lastTime)` + [ESCU - DNS Query Length With High Standard Deviation - Rule] action.escu = 0 action.escu.enabled = 1 @@ -2745,7 +2803,7 @@ action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration", action.escu.known_false_positives = It's possible there can be long domain names that are legitimate. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "Hidden Cobra Malware", "Suspicious DNS Traffic"] action.correlationsearch.enabled = 1 action.correlationsearch.label = DNS Query Length With High Standard Deviation action.notable = 1 @@ -2754,7 +2812,7 @@ action.notable.param.rule_description = Filter DNS requests and compute the stan action.notable.param.rule_title = DNS query length with high standard deviation action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -2795,7 +2853,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Indicator Removal o action.escu.known_false_positives = None identified action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] +action.escu.analytic_story = ["Ransomware", "Windows Log Manipulation"] action.correlationsearch.enabled = 1 action.correlationsearch.label = USN Journal Deletion action.notable = 1 @@ -2804,7 +2862,7 @@ action.notable.param.rule_description = The system $dest$ deleted its NTFS journ action.notable.param.rule_title = File System Journal Deleted on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -2829,44 +2887,94 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" -[ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] +[ESCU - Remote Desktop Network Traffic - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-10-07 -action.escu.modification_date = 2018-11-15 +action.escu.creation_date = 2016-09-13 +action.escu.modification_date = 2017-09-15 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.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. +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Remote Desktop Network Traffic - Rule +action.escu.mappings = {"mitre_attack": ["Lateral Movement", "Remote Desktop Protocol", "Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +action.escu.known_false_positives = Remote Desktop may be used legitimately by users on the network. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity"] +action.escu.providing_technologies = ["Bro", "Splunk Stream"] +action.escu.analytic_story = ["SamSam Ransomware", "Hidden Cobra Malware", "Lateral Movement"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Prohibited Applications Spawning cmd.exe +action.correlationsearch.label = Remote Desktop Network Traffic action.notable = 1 -action.notable.param.nes_fields = dest, process, parent_process -action.notable.param.rule_description = A prohibited application from prohibited_apps_launching_cmd.csv was leveraged to launch cmd.exe -action.notable.param.rule_title = Prohibited application($parent_process$) used to launch cmd.exe on $dest$ -action.notable.param.security_domain = endpoint +action.notable.param.nes_fields = dest, src +action.notable.param.rule_description = Remote Desktop Traffic detected between $src$ and $dest$. These two systems typically do not communicate with RDP +action.notable.param.rule_title = Uncommon Remote Desktop Network Traffic between $src$ and $dest$ +action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Registry Activities\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,src +alert.suppress.period = 28800s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `ctime(firstTime)`| `ctime(lastTime)` + +[ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2016-10-04 +action.escu.modification_date = 2017-09-23 +action.escu.asset_at_risk = Web Server +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = This search returns the number of times a URL associated with this type of JexBoss probe is observed. +action.escu.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. +action.escu.data_models = ["Web"] +action.escu.full_search_name = ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule +action.escu.mappings = {"mitre_attack": ["Discovery", "System Information Discovery"], "kill_chain_phases": ["Reconnaissance"]} +action.escu.known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. +action.escu.search_type = detection +action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +action.escu.analytic_story = ["SamSam Ransomware", "JBoss Vulnerability"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect attackers scanning for vulnerable JBoss servers +action.notable = 1 +action.notable.param.nes_fields = +action.notable.param.rule_description = This search looks for specific GET/HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. +action.notable.param.rule_title = Detect attackers scanning for vulnerable JBoss servers +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 +action.risk.param._risk_score = 20 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest, parent_process +alert.suppress.fields = dest,url alert.suppress.period = 86400s cron_schedule = 0 * * * * -description = 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. +description = 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. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -2877,78 +2985,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count values(Processes.user) as user values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] - -[ESCU - Detect Spike in AWS API Activity - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-03-12 -action.escu.modification_date = 2018-04-09 -action.escu.asset_at_risk = AWS Instance -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Detect Spike in AWS API Activity - Rule -action.escu.mappings = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -action.escu.known_false_positives = -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS User Monitoring"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in AWS API Activity -action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A spike in the number of AWS API calls by $user$ was detected. -action.notable.param.rule_title = Spike in AWS API activity detected by $user$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Investigate AWS User Activities by user field\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventType=AwsApiCall [search sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `ctime(firstTime)` | `ctime(lastTime)` [ESCU - WMI Temporary Event Subscription - Rule] action.escu = 0 @@ -3015,7 +3052,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Modify Registry"], action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities"] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Disabling Remote User Account Control action.notable = 1 @@ -3024,7 +3061,7 @@ action.notable.param.rule_description = The registry key SOFTWARE\Microsoft\Wind action.notable.param.rule_title = Registry Key Associated With Disabling Remote UAC Modified on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Registry Activities\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -3164,7 +3201,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Masquerading"], "ki action.escu.known_false_positives = None identified action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Unusual Processes", "Ransomware", "Suspicious Command-Line Executions"] +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Unusual Processes", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = System Processes Run From Unexpected Locations action.notable = 1 @@ -3173,7 +3210,7 @@ action.notable.param.rule_description = The system $dest$ has a process that nor action.notable.param.rule_title = System Processes Run From Unexpected Location on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -3198,44 +3235,44 @@ schedule_window = auto is_visible = false search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational EventCode=1 NOT Image="C:\\Windows\\System32*" NOT Image="C:\\Windows\\SysWOW64*" | rex field=Image .*\\\(?\S+)\s?$ | `isWindowsSystemFile` | rename Image as process | table _time, dest, user, process, process_id, parent_process -[ESCU - Attempt To Add Certificate To Untrusted Store - Rule] +[ESCU - Common Ransomware Extensions - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-04-09 +action.escu.creation_date = 2017-08-21 action.escu.modification_date = 2018-11-15 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = high -action.escu.eli5 = 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. -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 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. +action.escu.eli5 = 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. +action.escu.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. action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Attempt To Add Certificate To Untrusted Store - Rule -action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -action.escu.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. +action.escu.full_search_name = ESCU - Common Ransomware Extensions - Rule +action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.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. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Disabling Security Tools"] +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Attempt To Add Certificate To Untrusted Store +action.correlationsearch.label = Common Ransomware Extensions action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = Attempt to add a certificate to the untrusted certificate store -action.notable.param.rule_title = Attempt To Add Certificate to Untrusted Store +action.notable.param.nes_fields = dest, file_name +action.notable.param.rule_description = A file modification was detected on $dest$ with an extension commonly used by ransomware. +action.notable.param.rule_title = Ransomware Extension detected on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 +action.risk.param._risk_score = 80 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = process, dest -alert.suppress.period = 86400s +alert.suppress.fields = dest,file_name +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = Attempt to add a certificate to the untrusted certificate store +description = The search looks for file modifications with extensions commonly used by Ransomware dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -3246,7 +3283,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe (Processes.process=*-addstore* AND Processes.process=*disallowed* ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `ctime(firstTime)`|`ctime(lastTime)` +search = | tstats `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)` | `ctime(lastTime)` | `ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` [ESCU - Previously seen S3 bucket access by remote IP] action.escu = 0 @@ -3286,7 +3323,7 @@ action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Actions on Ob action.escu.known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] +action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Common Ransomware Notes action.notable = 1 @@ -3295,7 +3332,7 @@ action.notable.param.rule_description = A file modification associated with a ra action.notable.param.rule_title = Ransomware Note File detected on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -3482,7 +3519,7 @@ action.escu.full_search_name = ESCU - Get Emails From Specific Sender action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Microsoft Exchange"] -action.escu.analytic_story = ["Web Fraud Detection", "Brand Monitoring", "Suspicious Emails"] +action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails", "Web Fraud Detection"] action.escu.fields_required = ["src_user"] action.escu.earliest_time_offset = 86400 action.escu.latest_time_offset = 86400 @@ -3506,7 +3543,7 @@ action.escu.full_search_name = ESCU - Get Update Logs For Endpoint action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["Microsoft Windows", "Linux", "macOS"] -action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] +action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 604800 action.escu.latest_time_offset = 0 @@ -3631,7 +3668,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "Scripting", "Persistence" action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Hidden Cobra Malware", "DHS Report TA18-074A", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "DHS Report TA18-074A", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Hidden Cobra Malware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = First time seen command line argument action.notable = 1 @@ -3640,7 +3677,7 @@ action.notable.param.rule_description = The system $dest$ executed a command-lin action.notable.param.rule_title = First-time seen command-line argument was detected on $dest$. action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Outbound Emails to Hidden Cobra Threat Actors\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -3715,28 +3752,55 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` -[ESCU - Get Notable History] +[ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-03-15 -action.escu.modification_date = 2017-09-20 +action.escu.creation_date = 2017-04-25 +action.escu.modification_date = 2018-12-03 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.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. -action.escu.full_search_name = ESCU - Get Notable History -action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Windows Privilege Escalation", "Web Fraud Detection", "AWS Network ACL Activity", "Hidden Cobra Malware", "Suspicious AWS S3 Activities", "Windows File Extension and Association Abuse", "Monitor for Updates", "Unusual Processes", "ColdRoot MacOS RAT", "Account Monitoring and Controls", "Netsh Abuse", "DHS Report TA18-074A", "Data Protection", "Unusual AWS EC2 Modifications", "DNS Amplification Attacks", "SamSam Ransomware", "Windows Defense Evasion Tactics", "Dynamic DNS", "Apache Struts Vulnerability", "Monitor Backup Solution", "Windows Service Abuse", "Suspicious AWS Traffic", "Windows Log Manipulation", "Orangeworm Attack Group", "Disabling Security Tools", "JBoss Vulnerability", "Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious WMI Use", "Suspicious DNS Traffic", "Emotet Malware (TA18-201A)", "DNS Hijacking", "Host Redirection", "Suspicious AWS EC2 Activities", "Suspicious Command-Line Executions", "Brand Monitoring", "Splunk Enterprise Vulnerability", "Credential Dumping", "Prohibited Traffic Allowed or Protocol Mismatch", "Asset Tracking", "Use of Cleartext Protocols", "Windows Persistence Techniques", "Command and Control", "Suspicious Emails", "AWS Cross Account Activity", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious Windows Registry Activities", "Suspicious AWS Login Activities", "Router & Infrastructure Security", "Malicious PowerShell", "SQL Injection", "Lateral Movement", "Collection and Staging", "Monitor for Unauthorized Software", "AWS User Monitoring", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] -action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 864000 -action.escu.latest_time_offset = 86400 -description = This search queries the notable index and returns all the Notable Events for the particular destination host, giving the analyst an overview of the incidents that may have occurred with the host under investigation. +action.escu.confidence = medium +action.escu.eli5 = 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. +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 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. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.known_false_positives = These characters might be legitimately on the command-line, but it is not common. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Malicious PowerShell"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Malicious PowerShell Process With Obfuscation Techniques +action.notable = 1 +action.notable.param.nes_fields = dest, user, process_name, process +action.notable.param.rule_description = The system $dest$ executed a PowerShell process that has evidence of obfuscation on the command-line +action.notable.param.rule_title = PowerShell process with an obfuscation techniques detected on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 60 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,process_name,process +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | search `notable` | search dest={dest} | table _time, rule_name, owner, priority, severity, status_description +search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| eval num_obfuscation = (mvcount(split(process, "`"))-1) + (mvcount(split(process, "^"))-1) | search num_obfuscation > 0 [ESCU - Create a list of approved AWS service accounts] action.escu = 0 @@ -3772,7 +3836,7 @@ action.escu.full_search_name = ESCU - Get All AWS Activity From IP Address action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS S3 Activities", "AWS Suspicious Provisioning Activities", "Suspicious AWS Traffic", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "AWS Suspicious Provisioning Activities", "Suspicious AWS Traffic", "Suspicious AWS S3 Activities"] action.escu.fields_required = ["src_ip"] action.escu.earliest_time_offset = 14400 action.escu.latest_time_offset = 0 @@ -3806,28 +3870,28 @@ schedule_window = auto is_visible = false search = | search sourcetype="netbackup_logs" dest={dest} -[ESCU - Get Backup Logs For Endpoint] +[ESCU - Get User Information from Identity Table] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-08-24 -action.escu.modification_date = 2017-09-14 +action.escu.creation_date = 2017-04-10 +action.escu.modification_date = 2017-09-20 action.escu.channel = ESCU action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting your backup logs. -action.escu.full_search_name = ESCU - Get Backup Logs For Endpoint +action.escu.how_to_implement = To successfully implement this search you must have populated the identity table with information about your users. +action.escu.full_search_name = ESCU - Get User Information from Identity Table action.escu.known_false_positives = None at this time action.escu.search_type = contextual -action.escu.providing_technologies = ["Netbackup"] -action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] -action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 604800 -action.escu.latest_time_offset = 0 -description = This search will tell you the backup status from your netbackup_logs of a specific endpoint for the last week. +action.escu.providing_technologies = ["Splunk Enterprise Security"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Windows Service Abuse", "Host Redirection", "Suspicious AWS EC2 Activities", "DHS Report TA18-074A", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "ColdRoot MacOS RAT", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Unusual Processes", "Suspicious WMI Use", "Asset Tracking", "Ransomware", "Command and Control", "Suspicious AWS Login Activities", "Windows Privilege Escalation", "Windows Log Manipulation", "Windows File Extension and Association Abuse", "AWS Network ACL Activity", "Brand Monitoring", "SamSam Ransomware", "Data Protection", "Router & Infrastructure Security", "Malicious PowerShell", "Disabling Security Tools", "SQL Injection", "Windows Defense Evasion Tactics", "Emotet Malware (TA18-201A)", "Hidden Cobra Malware", "Suspicious Emails", "JBoss Vulnerability", "Suspicious DNS Traffic", "Spectre And Meltdown Vulnerabilities", "Credential Dumping", "Collection and Staging", "Netsh Abuse", "Account Monitoring and Controls", "Suspicious AWS S3 Activities", "Monitor for Updates", "Use of Cleartext Protocols", "Lateral Movement", "Dynamic DNS", "DNS Hijacking", "Splunk Enterprise Vulnerability", "Apache Struts Vulnerability", "Suspicious MSHTA Activity", "Monitor for Unauthorized Software"] +action.escu.fields_required = ["user"] +action.escu.earliest_time_offset = 864000 +action.escu.latest_time_offset = 86400 +description = Gather more information about the user identified in the Notable Event. disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = | search sourcetype="netbackup_logs" COMPUTERNAME={dest} | rename COMPUTERNAME as dest, MESSAGE as signature | table _time, dest, signature +search = | `identities` | search identity={user} | table _time, identity, first, last, email, category, watchlist [ESCU - Child Processes of Spoolsv.exe - Rule] action.escu = 0 @@ -3892,7 +3956,7 @@ action.escu.full_search_name = ESCU - Get Process responsible for the DNS traffi action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Data Protection", "Dynamic DNS", "Suspicious DNS Traffic", "DNS Hijacking", "Host Redirection", "Brand Monitoring", "Command and Control"] +action.escu.analytic_story = ["Host Redirection", "Command and Control", "Brand Monitoring", "Data Protection", "Suspicious DNS Traffic", "Dynamic DNS", "DNS Hijacking"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 @@ -3911,7 +3975,7 @@ action.escu.modification_date = 2018-07-24 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = The search is querying an accelerated Network_Resolution data model to count and list the values of resolved domains for each DNS query and checks that against the list of Dynamic DNS providers (lookup - dynamic_dns_providers) by each host (DNS.src) +action.escu.eli5 = The search is querying an accelerated `Network_Resolution` data model to count and list the values of resolved domains for each DNS query and checks that against the list of Dynamic DNS providers (lookup - `dynamic_dns_providers`) by each host (DNS.src) action.escu.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 similar solutions. 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`. action.escu.data_models = ["Network_Resolution"] action.escu.full_search_name = ESCU - Detect hosts connecting to dynamic domain providers - Rule @@ -3919,7 +3983,7 @@ action.escu.mappings = {"mitre_attack": ["Exfiltration", "Exfiltration Over Comm action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Data Protection", "Dynamic DNS", "Suspicious DNS Traffic", "DNS Hijacking", "Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch", "Data Protection", "Suspicious DNS Traffic", "Dynamic DNS", "DNS Hijacking"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Detect hosts connecting to dynamic domain providers action.notable = 1 @@ -3928,7 +3992,7 @@ action.notable.param.rule_description = The search has detected a host making ou action.notable.param.rule_title = Host $src$ detected to make a query to a Dynamic DNS provider action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get History Of Email Sources\n - ESCU - Get Process responsible for the DNS traffic\n - ESCU - Investigate Web Activity From src_ip\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -4067,67 +4131,65 @@ schedule_window = auto is_visible = false search = | tstats summariesonly=true allow_old_summaries=true latest(_time) as latestTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(latestTime <= relative_time(now(), "-60d@d"), 1, 0) | `ctime(latestTime)` | search isOutlier=1 | rename latestTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" -[ESCU - Get All AWS Activity From City] +[ESCU - Detect Spike in AWS API Activity - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-19 -action.escu.modification_date = 2018-03-19 +action.escu.creation_date = 2018-03-12 +action.escu.modification_date = 2018-04-09 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.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. -action.escu.full_search_name = ESCU - Get All AWS Activity From City -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -action.escu.fields_required = ["City"] -action.escu.earliest_time_offset = 14400 -action.escu.latest_time_offset = 0 -description = This search retrieves all the activity from a specific city and will create a table containing the time, city, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search City={City} | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, City, user, userName, userType, src_ip, awsRegion, eventName, errorCode - -[ESCU - Detect USB device insertion - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-08-03 -action.escu.modification_date = 2017-11-27 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = low -action.escu.eli5 = 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 -action.escu.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. -action.escu.data_models = ["Change_Analysis"] -action.escu.full_search_name = ESCU - Detect USB device insertion - Rule -action.escu.mappings = {"mitre_attack": ["Exfiltration"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.PT", "PR.DS"]} -action.escu.known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Detect Spike in AWS API Activity - Rule +action.escu.mappings = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.known_false_positives = action.escu.search_type = detection -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Data Protection"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS User Monitoring"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect USB device insertion +action.correlationsearch.label = Detect Spike in AWS API Activity action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = Read/Write attempt to a USB was detected on this host -action.notable.param.rule_title = Read/Write attempt to a USB detected on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = low -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.nes_fields = user +action.notable.param.rule_description = A spike in the number of AWS API calls by $user$ was detected. +action.notable.param.rule_title = Spike in AWS API activity detected by $user$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Investigate AWS User Activities by user field\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 +action.risk.param._risk_object = user +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s +alert.suppress.fields = user +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = 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. +description = 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. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -4138,7 +4200,56 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `ctime(earliest)`| `ctime(latest)` +search = sourcetype=aws:cloudtrail eventType=AwsApiCall [search sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user + +[ESCU - Web Fraud - Password Sharing Across Accounts - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-07-12 +action.escu.modification_date = 2018-10-08 +action.escu.asset_at_risk = account +action.escu.channel = webfraud +action.escu.confidence = medium +action.escu.eli5 = 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 +action.escu.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. +action.escu.full_search_name = ESCU - Web Fraud - Password Sharing Across Accounts - Rule +action.escu.mappings = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Bro"] +action.escu.analytic_story = ["Web Fraud Detection"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Web Fraud - Password Sharing Across Accounts +action.notable = 1 +action.notable.param.nes_fields = user +action.notable.param.rule_description = This search is used to identify user accounts, $user$, that share common passwords +action.notable.param.rule_title = Web Fraud Detection: Password Sharing Across Accounts +action.notable.param.security_domain = threat +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Emails From Specific Sender\n - ESCU - Get Web Session Information via session_id\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = user +action.risk.param._risk_object_type = other +action.risk.param._risk_score = 10 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = user +alert.suppress.period = 3600s +cron_schedule = 0 * * * * +description = This search is used to identify user accounts that share a common password. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=stream:http http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?[^&|^$]+)" | rex field=form_data "login\[password\]=(?[^&|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 [ESCU - Shim Database Installation With Suspicious Parameters - Rule] action.escu = 0 @@ -4189,44 +4300,45 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*sdbinst* cmdline="*-p*" cmdline="*-q*" | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` -[ESCU - Abnormally High AWS Instances Launched by User - Rule] +[ESCU - Detect malicious requests to exploit JBoss servers - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-02-26 -action.escu.modification_date = 2018-02-26 -action.escu.asset_at_risk = AWS Instance +action.escu.creation_date = 2016-10-04 +action.escu.modification_date = 2017-09-23 +action.escu.asset_at_risk = Web Server action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Abnormally High AWS Instances Launched by User - Rule -action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} -action.escu.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. +action.escu.confidence = high +action.escu.eli5 = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers. +action.escu.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 +action.escu.data_models = ["Web"] +action.escu.full_search_name = ESCU - Detect malicious requests to exploit JBoss servers - Rule +action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 12", "CIS 4", "CIS 18"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} +action.escu.known_false_positives = No known false positives for this detection. action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] +action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +action.escu.analytic_story = ["SamSam Ransomware", "JBoss Vulnerability"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Abnormally High AWS Instances Launched by User +action.correlationsearch.label = Detect malicious requests to exploit JBoss servers action.notable = 1 -action.notable.param.nes_fields = userName -action.notable.param.rule_description = An abnormally high number of instances were launched by a user within in a 10-minute window -action.notable.param.rule_title = High Number of instances launched by $userName$ +action.notable.param.nes_fields = src, dest_ip +action.notable.param.rule_description = A search for detecting malicious requests made to exploit jmx-console in JBoss servers. The bad requests have a long url length since it serves the payload via the url +action.notable.param.rule_title = Detected malicious requests to exploit JBoss servers action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Instance Details by instanceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Investigate AWS activities via region name\n"} +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = userName -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 80 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = userName -alert.suppress.period = 3600s -cron_schedule = */10 * * * * -description = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. -dispatch.earliest_time = -30d@d +alert.suppress.fields = dest,url,src +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -4236,7 +4348,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `ctime(firstTime)` | `ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime [ESCU - Detect Excessive User Account Lockouts - Rule] action.escu = 0 @@ -4300,7 +4412,7 @@ action.escu.full_search_name = ESCU - Get Vulnerability Logs For Endpoint action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["Nessus"] -action.escu.analytic_story = ["ColdRoot MacOS RAT", "SamSam Ransomware", "Ransomware", "DNS Hijacking"] +action.escu.analytic_story = ["ColdRoot MacOS RAT", "Ransomware", "SamSam Ransomware", "DNS Hijacking"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 604800 action.escu.latest_time_offset = 0 @@ -4372,7 +4484,7 @@ action.escu.full_search_name = ESCU - Get Notable Info action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Windows Privilege Escalation", "Web Fraud Detection", "AWS Network ACL Activity", "Hidden Cobra Malware", "Suspicious AWS S3 Activities", "Windows File Extension and Association Abuse", "Monitor for Updates", "Account Monitoring and Controls", "DHS Report TA18-074A", "Data Protection", "DNS Amplification Attacks", "Windows Defense Evasion Tactics", "Dynamic DNS", "Apache Struts Vulnerability", "Windows Service Abuse", "Suspicious AWS Traffic", "Windows Log Manipulation", "Orangeworm Attack Group", "Disabling Security Tools", "JBoss Vulnerability", "Spectre And Meltdown Vulnerabilities", "Suspicious WMI Use", "Suspicious DNS Traffic", "Emotet Malware (TA18-201A)", "Host Redirection", "Suspicious AWS EC2 Activities", "Suspicious Command-Line Executions", "Brand Monitoring", "Splunk Enterprise Vulnerability", "Credential Dumping", "Asset Tracking", "Use of Cleartext Protocols", "Windows Persistence Techniques", "Command and Control", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious AWS Login Activities", "Router & Infrastructure Security", "Malicious PowerShell", "SQL Injection", "Lateral Movement", "Collection and Staging", "AWS User Monitoring", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Windows Service Abuse", "Host Redirection", "Suspicious AWS EC2 Activities", "DHS Report TA18-074A", "Windows Persistence Techniques", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "DNS Amplification Attacks", "Suspicious WMI Use", "Asset Tracking", "Command and Control", "Suspicious AWS Login Activities", "Windows Privilege Escalation", "Windows Log Manipulation", "Windows File Extension and Association Abuse", "AWS Network ACL Activity", "Brand Monitoring", "Data Protection", "Router & Infrastructure Security", "Malicious PowerShell", "Disabling Security Tools", "SQL Injection", "Windows Defense Evasion Tactics", "Emotet Malware (TA18-201A)", "Hidden Cobra Malware", "JBoss Vulnerability", "Suspicious DNS Traffic", "Spectre And Meltdown Vulnerabilities", "Credential Dumping", "Suspicious AWS Traffic", "Collection and Staging", "Account Monitoring and Controls", "Suspicious AWS S3 Activities", "Monitor for Updates", "Use of Cleartext Protocols", "Lateral Movement", "Dynamic DNS", "AWS User Monitoring", "Web Fraud Detection", "Splunk Enterprise Vulnerability", "Apache Struts Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409"] action.escu.fields_required = ["event_id"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 3600 @@ -4433,44 +4545,44 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest | `ctime(lastTime)` | `ctime(firstTime)` | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)` -[ESCU - Unsuccessful Netbackup backups - Rule] +[ESCU - Detect S3 access from a new IP - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-06-15 -action.escu.modification_date = 2017-09-12 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-06-25 +action.escu.modification_date = 2018-06-28 +action.escu.asset_at_risk = S3 Bucket action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure. -action.escu.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. -action.escu.full_search_name = ESCU - Unsuccessful Netbackup backups - Rule -action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} -action.escu.known_false_positives = None identified +action.escu.confidence = low +action.escu.eli5 = 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 +action.escu.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. +action.escu.full_search_name = ESCU - Detect S3 access from a new IP - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13", "CIS 14"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.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 action.escu.search_type = detection -action.escu.providing_technologies = ["Netbackup"] -action.escu.analytic_story = ["Monitor Backup Solution"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS S3 Activities"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Unsuccessful Netbackup backups +action.correlationsearch.label = Detect S3 access from a new IP action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The system $dest$ attempted a backup but encountered an error. -action.notable.param.rule_title = Failed backup attempt by $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - All backup logs for host\n"} +action.notable.param.nes_fields = bucket_name, src_ip +action.notable.param.rule_description = A remote IP, $src_ip$, has made a successful connection with an S3 $bucket_name$. +action.notable.param.rule_title = S3 bucket $bucketName$ was accessed by a new $src_ip$ +action.notable.param.security_domain = network +action.notable.param.severity = low +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest +action.risk.param._risk_object = src_ip action.risk.param._risk_object_type = system -action.risk.param._risk_score = 10 +action.risk.param._risk_score = 20 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest +alert.suppress.fields = bucket_name, src_ip alert.suppress.period = 86400s -cron_schedule = 0 7 * * * -description = This search gives you the hosts where a backup was attempted and then failed. -dispatch.earliest_time = -24h@h +cron_schedule = 5 * * * * +description = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -4480,7 +4592,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype="netbackup_logs" | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature +search = sourcetype=aws:s3:accesslogs http_status=200 [search sourcetype=aws:s3:accesslogs http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | convert ctime(firstTime) ctime(lastTime) | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri [ESCU - WMI Permanent Event Subscription - Rule] action.escu = 0 @@ -4690,7 +4802,7 @@ action.escu.full_search_name = ESCU - Get Risk Modifiers For User action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Windows Privilege Escalation", "Hidden Cobra Malware", "Windows File Extension and Association Abuse", "Monitor for Updates", "Unusual Processes", "ColdRoot MacOS RAT", "Account Monitoring and Controls", "Netsh Abuse", "DHS Report TA18-074A", "Data Protection", "DNS Amplification Attacks", "SamSam Ransomware", "Dynamic DNS", "Apache Struts Vulnerability", "Monitor Backup Solution", "Windows Service Abuse", "Windows Log Manipulation", "Orangeworm Attack Group", "Disabling Security Tools", "JBoss Vulnerability", "Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious WMI Use", "Suspicious DNS Traffic", "Emotet Malware (TA18-201A)", "DNS Hijacking", "Host Redirection", "Brand Monitoring", "Splunk Enterprise Vulnerability", "Credential Dumping", "Prohibited Traffic Allowed or Protocol Mismatch", "Asset Tracking", "Use of Cleartext Protocols", "Windows Persistence Techniques", "Command and Control", "Suspicious Emails", "Suspicious Windows Registry Activities", "Router & Infrastructure Security", "Malicious PowerShell", "SQL Injection", "Lateral Movement", "Collection and Staging", "Monitor for Unauthorized Software", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Windows Service Abuse", "Host Redirection", "DHS Report TA18-074A", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "ColdRoot MacOS RAT", "Monitor Backup Solution", "Orangeworm Attack Group", "Unusual Processes", "DNS Amplification Attacks", "Suspicious WMI Use", "Asset Tracking", "Ransomware", "Command and Control", "Windows Privilege Escalation", "Windows Log Manipulation", "Windows File Extension and Association Abuse", "Prohibited Traffic Allowed or Protocol Mismatch", "Brand Monitoring", "SamSam Ransomware", "Data Protection", "Router & Infrastructure Security", "Malicious PowerShell", "Disabling Security Tools", "SQL Injection", "Emotet Malware (TA18-201A)", "Hidden Cobra Malware", "Suspicious Emails", "JBoss Vulnerability", "Suspicious DNS Traffic", "Spectre And Meltdown Vulnerabilities", "Credential Dumping", "Collection and Staging", "Netsh Abuse", "Account Monitoring and Controls", "Monitor for Updates", "Use of Cleartext Protocols", "Lateral Movement", "Dynamic DNS", "DNS Hijacking", "Splunk Enterprise Vulnerability", "Apache Struts Vulnerability", "Suspicious MSHTA Activity", "Monitor for Unauthorized Software"] action.escu.fields_required = ["user"] action.escu.earliest_time_offset = 604800 action.escu.latest_time_offset = 0 @@ -4701,69 +4813,44 @@ schedule_window = auto is_visible = false search = | from datamodel:Risk.All_Risk | search risk_object_type=user risk_object={user} | stats count sum(risk_score) as risk_score values(search_name) min(_time) as firstTime max(_time) as lastTime by risk_object |`ctime(firstTime)` |`ctime(lastTime)` -[ESCU - Get Process Info] +[ESCU - Unsuccessful Netbackup backups - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-03-15 -action.escu.modification_date = 2017-11-09 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Application_State data model. -action.escu.data_models = ["Application_State"] -action.escu.full_search_name = ESCU - Get Process Info -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Privilege Escalation", "Hidden Cobra Malware", "Windows File Extension and Association Abuse", "Unusual Processes", "Netsh Abuse", "DHS Report TA18-074A", "SamSam Ransomware", "Windows Defense Evasion Tactics", "Windows Service Abuse", "Windows Log Manipulation", "Orangeworm Attack Group", "Disabling Security Tools", "Ransomware", "Suspicious WMI Use", "Emotet Malware (TA18-201A)", "Suspicious Command-Line Executions", "Credential Dumping", "Windows Persistence Techniques", "Command and Control", "Suspicious Windows Registry Activities", "Malicious PowerShell", "Lateral Movement", "Collection and Staging", "Monitor for Unauthorized Software", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] -action.escu.fields_required = ["process", "dest"] -action.escu.earliest_time_offset = 7200 -action.escu.latest_time_offset = 7200 -description = This search queries the Application State data model to give you details about the process running on a host which is under investigation. To gather the process info, enter the values for the process name in question and the destination IP address. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | from datamodel Application_State.Processes | search process=*{process}* dest={dest} - -[ESCU - Spike in File Writes - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-08-20 -action.escu.modification_date = 2018-12-03 +action.escu.creation_date = 2017-06-15 +action.escu.modification_date = 2017-09-12 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = low -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Spike in File Writes - Rule -action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} -action.escu.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. +action.escu.confidence = high +action.escu.eli5 = This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure. +action.escu.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. +action.escu.full_search_name = ESCU - Unsuccessful Netbackup backups - Rule +action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} +action.escu.known_false_positives = None identified action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] +action.escu.providing_technologies = ["Netbackup"] +action.escu.analytic_story = ["Monitor Backup Solution"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Spike in File Writes +action.correlationsearch.label = Unsuccessful Netbackup backups action.notable = 1 action.notable.param.nes_fields = dest -action.notable.param.rule_description = A sharp increase in file writes was detected on $dest -action.notable.param.rule_title = Spike in file writes on $dest$ +action.notable.param.rule_description = The system $dest$ attempted a backup but encountered an error. +action.notable.param.rule_title = Failed backup attempt by $dest$ action.notable.param.security_domain = endpoint -action.notable.param.severity = low -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - All backup logs for host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 +action.risk.param._risk_score = 10 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 alert.suppress.fields = dest -alert.suppress.period = 7200s -cron_schedule = 0 * * * * -description = The search looks for a sharp increase in the number of files written to a particular host -dispatch.earliest_time = -7d@d +alert.suppress.period = 86400s +cron_schedule = 0 7 * * * +description = This search gives you the hosts where a backup was attempted and then failed. +dispatch.earliest_time = -24h@h dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -4773,7 +4860,29 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 +search = sourcetype="netbackup_logs" | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature + +[ESCU - Previously seen API call per user roles in CloudTrail] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-04-01 +action.escu.modification_date = 2018-04-16 +action.escu.channel = ESCU +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Previously seen API call per user roles in CloudTrail +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS User Monitoring"] +description = 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. +dispatch.earliest_time = -30d@d +dispatch.latest_time = -10m@m +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws: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 - Detect new API calls from user roles - Rule] action.escu = 0 @@ -4839,7 +4948,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Persistence"], "kil action.escu.known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Persistence Techniques"] +action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Defense Evasion Tactics"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Hiding Files And Directories With Attrib.exe action.notable = 1 @@ -4848,7 +4957,7 @@ action.notable.param.rule_description = Attrib.exe is often used by attackers to action.notable.param.rule_title = Suspicious usage of attrib.exe on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -4923,56 +5032,28 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 -[ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] +[ESCU - Get Web Session Information via session_id] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-16 -action.escu.modification_date = 2018-03-16 -action.escu.asset_at_risk = AWS Instance +action.escu.creation_date = 2018-10-08 +action.escu.modification_date = 2018-10-08 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Cloud Provisioning From Previously Unseen IP Address -action.notable = 1 -action.notable.param.nes_fields = src_ip -action.notable.param.rule_description = Your AWS infrastructure was provisioned from an IP, $src_ip$, which has never before been seen provisioning your infrastructure. -action.notable.param.rule_title = AWS Provision Activity From $src_ip$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From City\n - ESCU - Get All AWS Activity From Country\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get All AWS Activity From Region\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = 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." -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.eli5 = none +action.escu.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. +action.escu.full_search_name = ESCU - Get Web Session Information via session_id +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["Splunk Stream"] +action.escu.analytic_story = ["Web Fraud Detection"] +action.escu.fields_required = ["session_id"] +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 3600 +description = This search helps an analyst investigate a notable event to find out more about a specific web session. The search looks for a specific web session ID in the HTTP web traffic and outputs the URL and user agents, grouped by source IP address and HTTP status code. disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode +search = | search sourcetype=stream:http {session_id} | stats values(url) values(http_user_agent) by src_ip status [ESCU - Execution of File With Spaces Before Extension - Rule] action.escu = 0 @@ -5064,7 +5145,7 @@ action.escu.mappings = {"mitre_attack": ["Privilege Escalation", "Persistence", action.escu.known_false_positives = There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Windows Privilege Escalation", "Suspicious Windows Registry Activities"] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Privilege Escalation"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Registry Keys Used For Privilege Escalation action.notable = 1 @@ -5073,7 +5154,7 @@ action.notable.param.rule_description = A registry key used for privilege escala action.notable.param.rule_title = Registry Key Associated With Privilege Escalation Modified on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Registry Activities\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -5098,27 +5179,77 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") by Registry.dest , Registry.status, Registry.user | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(Registry)` -[ESCU - Baseline of Network ACL Activity by ARN] +[ESCU - Get All AWS Activity From Region] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-05-21 -action.escu.modification_date = 2018-05-21 +action.escu.creation_date = 2018-03-19 +action.escu.modification_date = 2018-03-19 action.escu.channel = ESCU -action.escu.eli5 = 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. -action.escu.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 `NetworkACLEvents`. -action.escu.full_search_name = ESCU - Baseline of Network ACL Activity by ARN +action.escu.eli5 = none +action.escu.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. +action.escu.full_search_name = ESCU - Get All AWS Activity From Region action.escu.known_false_positives = None at this time -action.escu.search_type = support +action.escu.search_type = investigative action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Network ACL Activity"] -description = 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. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m +action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] +action.escu.fields_required = ["Region"] +action.escu.earliest_time_offset = 14400 +action.escu.latest_time_offset = 0 +description = This search retrieves all the activity from a specific geographic region and will create a table containing the time, geographic region, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail `NetworkACLEvents` | 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 +search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Region={Region} | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Region, user, userName, userType, src_ip, awsRegion, eventName, errorCode + +[ESCU - EC2 Instance Modified With Previously Unseen User - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-04-09 +action.escu.modification_date = 2018-04-09 +action.escu.asset_at_risk = AWS Instance +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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 `ec2ModificationAPIs`. 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. +action.escu.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 `ec2ModificationAPIs`. +action.escu.full_search_name = ESCU - EC2 Instance Modified With Previously Unseen User - Rule +action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Unusual AWS EC2 Modifications"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = EC2 Instance Modified With Previously Unseen User +action.notable = 1 +action.notable.param.nes_fields = user, dest +action.notable.param.rule_description = The EC2 instance $dest$ was modified by $user$. This user has never modified an EC2 instance before. +action.notable.param.rule_title = EC2 Instance Modified By Previously Unseen User $user$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Instance Details by instanceId\n - ESCU - Get Notable History\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 30 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = user, dest +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search looks for EC2 instances being modified by users who have not previously modified them. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail `ec2ModificationAPIs` [search sourcetype=aws:cloudtrail `ec2ModificationAPIs` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `ctime(firstTime)` | `ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest [ESCU - AWS S3 Bucket details via bucketName] action.escu = 0 @@ -5193,6 +5324,56 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change_Analysis where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by All_Changes.result_id All_Changes.user All_Changes.dest | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | transaction user maxspan=240m | search result_id=4720 result_id=4726 +[ESCU - Remote WMI Command Attempt - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-01-13 +action.escu.modification_date = 2018-12-03 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +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 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. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Remote WMI Command Attempt - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.known_false_positives = Administrators may use this legitimately to gather info from remote systems. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Suspicious WMI Use"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Remote WMI Command Attempt +action.notable = 1 +action.notable.param.nes_fields = dest,user,process_name +action.notable.param.rule_description = This search looks for wmic.exe being launched with parameters to operate on remote systems. +action.notable.param.rule_title = Endpoint - Remote WMI command attempt +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Get Sysmon WMI Activity for Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 30 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,user,process_name +alert.suppress.period = 28800s +cron_schedule = 50 * * * * +description = This search looks for wmic.exe being launched with parameters to operate on remote systems. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` + [ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule] action.escu = 0 action.escu.enabled = 1 @@ -5344,6 +5525,29 @@ schedule_window = auto is_visible = false search = | tstats `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)` | `ctime(lastTime)` | `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 +[ESCU - AWS Network Interface details via resourceId] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-05-07 +action.escu.modification_date = 2018-05-07 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.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 +action.escu.full_search_name = ESCU - AWS Network Interface details via resourceId +action.escu.known_false_positives = None at this time +action.escu.search_type = contextual +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Command and Control", "AWS Network ACL Activity", "Suspicious AWS Traffic"] +action.escu.fields_required = ["resourceId"] +action.escu.earliest_time_offset = 86400 +action.escu.latest_time_offset = 0 +description = This search queries AWS configuration logs and returns the information about a specific network interface via network interface ID. The information will include the ARN of the network interface, its relationships with other AWS resources, the public and the private IP associated with the network interface. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | search sourcetype=aws:config resourceId={resourceId} | table _time ARN relationships{}.resourceType relationships{}.name relationships{}.resourceId configuration.privateIpAddresses{}.privateIpAddress configuration.privateIpAddresses{}.association.publicIp + [ESCU - Investigate Successful Remote Desktop Authentications] action.escu = 0 action.escu.enabled = 1 @@ -5368,128 +5572,6 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature_id=4624 Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app Authentication.user Authentication.signature Authentication.src_nt_domain | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name("Authentication")`| table firstTime lastTime src src_nt_domain dest user app count | sort count -[ESCU - Common Ransomware Extensions - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-08-21 -action.escu.modification_date = 2018-11-15 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Common Ransomware Extensions - Rule -action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Common Ransomware Extensions -action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A file modification was detected on $dest$ with an extension commonly used by ransomware. -action.notable.param.rule_title = Ransomware Extension detected on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = The search looks for file modifications with extensions commonly used by Ransomware -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `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)` | `ctime(lastTime)` | `ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` - -[ESCU - EC2 Instance Started With Previously Unseen User - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-03-15 -action.escu.modification_date = 2018-03-12 -action.escu.asset_at_risk = AWS Instance -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen User - Rule -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Started With Previously Unseen User -action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = The EC2 instance $dest$ was created by $user$. This user has never created an EC2 instance before. -action.notable.param.rule_title = EC2 Instance Created By Previously Unseen User $user$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Instance Details by instanceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Investigate AWS activities via region name\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user, dest -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for EC2 instances being created by users who have not created them before. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `ctime(firstTime)` | `ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType - -[ESCU - Identify Systems Using Remote Desktop] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-04-18 -action.escu.modification_date = 2017-09-15 -action.escu.channel = ESCU -action.escu.eli5 = 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 in the Application_State data model. -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. -action.escu.data_models = ["Application_State"] -action.escu.full_search_name = ESCU - Identify Systems Using Remote Desktop -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Lateral Movement"] -description = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count from datamodel=Application_State where All_Application_State.process="*mstsc.exe*" by All_Application_State.dest All_Application_State.process | `drop_dm_object_name("All_Application_State")` | sort - count - [ESCU - Detect new user AWS Console Login - Rule] action.escu = 0 action.escu.enabled = 1 @@ -5539,6 +5621,55 @@ schedule_window = auto is_visible = false search = sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as arn |stats earliest(_time) as earliest latest(_time) as latest by arn | inputlookup append=t previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by arn | outputlookup previously_seen_users_console_logins.csv | eval userStatus=if(earliest >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | convert ctime(earliest) ctime(latest) | where userStatus ="First Time Logging into AWS Console" +[ESCU - EC2 Instance Started With Previously Unseen User - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-03-15 +action.escu.modification_date = 2018-03-12 +action.escu.asset_at_risk = AWS Instance +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen User - Rule +action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "AWS Cryptomining"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = EC2 Instance Started With Previously Unseen User +action.notable = 1 +action.notable.param.nes_fields = user, dest +action.notable.param.rule_description = The EC2 instance $dest$ was created by $user$. This user has never created an EC2 instance before. +action.notable.param.rule_title = EC2 Instance Created By Previously Unseen User $user$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 30 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = user, dest +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search looks for EC2 instances being created by users who have not created them before. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `ctime(firstTime)` | `ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType + [ESCU - Previously seen command line arguments] action.escu = 0 action.escu.enabled = 1 @@ -5551,7 +5682,7 @@ action.escu.full_search_name = ESCU - Previously seen command line arguments action.escu.known_false_positives = None at this time action.escu.search_type = support action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Hidden Cobra Malware", "DHS Report TA18-074A", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "DHS Report TA18-074A", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Hidden Cobra Malware"] description = 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. dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m @@ -5561,32 +5692,81 @@ schedule_window = auto is_visible = false search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational process=cmd.exe cmdline="* /c *" | stats earliest(_time) as firstTime latest(_time) as lastTime by cmdline | outputlookup previously_seen_cmd_line_arguments | stats count -[ESCU - Remote WMI Command Attempt - Rule] +[ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-01-13 -action.escu.modification_date = 2018-12-03 +action.escu.creation_date = 2018-08-28 +action.escu.modification_date = 2018-08-28 +action.escu.asset_at_risk = Windows +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = This search looks for Windows Event Code 4663 (object access), where the process performing the access is PowerShell.exe, the target process of the access is lsass.exe, and the access mask is given as 0x10. 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. +action.escu.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. +action.escu.full_search_name = ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule +action.escu.mappings = {"mitre_attack": ["Credential Access", "Credential Dumping"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Credential Dumping"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect Mimikatz Via PowerShell And EventCode 4663 +action.notable = 1 +action.notable.param.nes_fields = user, dest +action.notable.param.rule_description = Possible attempt at credential dumping via PowerShell was detected on $dest$ by $user$. +action.notable.param.rule_title = Event ID 4663 Specifying PowerShell Reading From LSASS.exe Identified on $dest$. +action.notable.param.security_domain = access +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 40 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = user, dest, process +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search looks for PowerShell reading lsass memory consistent with credential dumping. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=wineventlog:security EventCode=4663 Process_Name=*powershell.exe Object_Name=*lsass.exe Access_Mask=0x10 | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, Process_ID, Message | rename Process_Name as process | `ctime(firstTime)`| `ctime(lastTime)` + +[ESCU - Registry Keys for Creating SHIM Databases - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-08-27 +action.escu.modification_date = 2017-09-15 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = 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. -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 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. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Remote WMI Command Attempt - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -action.escu.known_false_positives = Administrators may use this legitimately to gather info from remote systems. +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Change_Analysis"] +action.escu.full_search_name = ESCU - Registry Keys for Creating SHIM Databases - Rule +action.escu.mappings = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious WMI Use"] +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote WMI Command Attempt +action.correlationsearch.label = Registry Keys for Creating SHIM Databases action.notable = 1 -action.notable.param.nes_fields = dest,user,process_name -action.notable.param.rule_description = This search looks for wmic.exe being launched with parameters to operate on remote systems. -action.notable.param.rule_title = Endpoint - Remote WMI command attempt +action.notable.param.nes_fields = dest, user +action.notable.param.rule_description = A registry key that is used for persistence on Windows was modified on $dest$ by $user$ +action.notable.param.rule_title = Registry Key Associated With SHIM databases on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Get Sysmon WMI Activity for Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -5595,10 +5775,10 @@ action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,user,process_name -alert.suppress.period = 28800s -cron_schedule = 50 * * * * -description = This search looks for wmic.exe being launched with parameters to operate on remote systems. +alert.suppress.fields = dest,object_path +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -5609,45 +5789,46 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` +search = | tstats `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="*CurrentVersion\\AppCompatFlags\\Custom*" OR All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\InstalledSDB*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `drop_dm_object_name("All_Changes")` -[ESCU - Process Execution via WMI - Rule] +[ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-10-23 -action.escu.modification_date = 2018-10-23 +action.escu.creation_date = 2017-07-08 +action.escu.modification_date = 2017-09-18 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.how_to_implement = To successfully implement this search, you must 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. -action.escu.full_search_name = ESCU - Process Execution via WMI - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -action.escu.known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes. +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Network_Resolution"] +action.escu.full_search_name = ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule +action.escu.mappings = {"mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +action.escu.known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious WMI Use"] +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic", "DNS Hijacking"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Process Execution via WMI +action.correlationsearch.label = DNS Query Requests Resolved by Unauthorized DNS Servers action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = This search looks for child processes of WmiPrvSE.exe, which indicates that a process was launched via WMI. -action.notable.param.rule_title = Process launched via WMI on $dest$ -action.notable.param.security_domain = endpoint +action.notable.param.nes_fields = dest, src +action.notable.param.rule_description = The table represents a list of unauthorized DNS servers interacting with hosts in your network +action.notable.param.rule_title = DNS requests resolved by unauthorized DNS servers +action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Get Sysmon WMI Activity for Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get History Of Email Sources\n - ESCU - Get Process responsible for the DNS traffic\n - ESCU - Investigate Web Activity From src_ip\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest +action.risk.param._risk_object = src action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 +action.risk.param._risk_score = 40 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,user +alert.suppress.fields = dest,src alert.suppress.period = 28800s cron_schedule = 0 * * * * -description = This search looks for processes launched via WMI. +description = 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. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -5658,79 +5839,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) parent_process=*WmiPrvSE.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, parent_process, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` - -[ESCU - Get User Information from Identity Table] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-04-10 -action.escu.modification_date = 2017-09-20 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must have populated the identity table with information about your users. -action.escu.full_search_name = ESCU - Get User Information from Identity Table -action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Windows Privilege Escalation", "AWS Network ACL Activity", "Hidden Cobra Malware", "Suspicious AWS S3 Activities", "Windows File Extension and Association Abuse", "Monitor for Updates", "Unusual Processes", "ColdRoot MacOS RAT", "Account Monitoring and Controls", "Netsh Abuse", "DHS Report TA18-074A", "Data Protection", "SamSam Ransomware", "Windows Defense Evasion Tactics", "Dynamic DNS", "Apache Struts Vulnerability", "Windows Service Abuse", "Windows Log Manipulation", "Orangeworm Attack Group", "Disabling Security Tools", "JBoss Vulnerability", "Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious WMI Use", "Suspicious DNS Traffic", "Emotet Malware (TA18-201A)", "DNS Hijacking", "Host Redirection", "Suspicious AWS EC2 Activities", "Suspicious Command-Line Executions", "Brand Monitoring", "Splunk Enterprise Vulnerability", "Credential Dumping", "Asset Tracking", "Use of Cleartext Protocols", "Windows Persistence Techniques", "Command and Control", "Suspicious Emails", "Suspicious Windows Registry Activities", "Suspicious AWS Login Activities", "Router & Infrastructure Security", "Malicious PowerShell", "SQL Injection", "Lateral Movement", "Collection and Staging", "Monitor for Unauthorized Software", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] -action.escu.fields_required = ["user"] -action.escu.earliest_time_offset = 864000 -action.escu.latest_time_offset = 86400 -description = Gather more information about the user identified in the Notable Event. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | `identities` | search identity={user} | table _time, identity, first, last, email, category, watchlist - -[ESCU - AWS Network Access Control List Deleted - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-01-08 -action.escu.modification_date = 2017-01-10 -action.escu.asset_at_risk = AWS Instance -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - AWS Network Access Control List Deleted - Rule -action.escu.mappings = {"mitre_attack": ["Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11"], "nist": ["DE.DP", "DE.AE"]} -action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Network ACL Activity"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Network Access Control List Deleted -action.notable = 1 -action.notable.param.nes_fields = src, src_user, eventName -action.notable.param.rule_description = AWS network ACL has been deleted by $src_user. -action.notable.param.rule_title = AWS Network ACL deleted by $src_user$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network ACL Details from ID\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_user -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -1d@d -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `ctime(lastTime)` | `ctime(firstTime)` +search = | tstats `summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` [ESCU - Get All AWS Activity From Country] action.escu = 0 @@ -5755,6 +5864,30 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Country={Country} | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Country, user, userName, userType, src_ip, awsRegion, eventName, errorCode +[ESCU - Get History Of Email Sources] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2019-02-21 +action.escu.modification_date = 2019-02-21 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.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. +action.escu.data_models = ["Email"] +action.escu.full_search_name = ESCU - Get History Of Email Sources +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["Microsoft Exchange"] +action.escu.analytic_story = ["DNS Hijacking"] +action.escu.fields_required = ["src"] +action.escu.earliest_time_offset = 172800 +action.escu.latest_time_offset = 86400 +description = This search returns a list of all email sources seen in the 48 hours prior to the notable event to 24 hours after, and the number of emails from each source. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = |tstats `summariesonly` values(All_Email.dest) as dest values(All_Email.recipient) as recepient min(_time) as firstTime max(_time) as lastTime count from datamodel=Email.All_Email by All_Email.src |`drop_dm_object_name(All_Email)` | `ctime(firstTime)` | `ctime(lastTime)` + [ESCU - Shim Database File Creation - Rule] action.escu = 0 action.escu.enabled = 1 @@ -5952,54 +6085,29 @@ schedule_window = auto is_visible = false search = sourcetype=stream:http http_content_type=text* uri="/magento2/customer/account/loginPost/" | rex field=cookie "form_key=(?\w+)" | rex field=form_data "login\[username\]=(?[^&|^$]+)" | search Username=* | rex field=Username "@(?.*)"|stats dc(Username) as UniqueUsernames list(Username) as src_user by email_domain|where UniqueUsernames> 25 -[ESCU - Schtasks scheduling job on remote system - Rule] +[ESCU - Get Authentication Logs For Endpoint] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-09-13 -action.escu.modification_date = 2017-09-15 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2017-04-10 +action.escu.modification_date = 2017-11-01 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Schtasks scheduling job on remote system - Rule -action.escu.mappings = {"mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task", "Remote Services"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "nist": ["PR.IP"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Lateral Movement"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Schtasks scheduling job on remote system -action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. -action.notable.param.rule_title = Schtasks scheduling job on remote system -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,cmdline -alert.suppress.period = 28800s -cron_schedule = 0 * * * * -description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.eli5 = none +action.escu.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. +action.escu.data_models = ["Authentication"] +action.escu.full_search_name = ESCU - Get Authentication Logs For Endpoint +action.escu.known_false_positives = None at this time +action.escu.search_type = contextual +action.escu.providing_technologies = ["Microsoft Windows", "Linux", "macOS"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Windows Service Abuse", "Host Redirection", "DHS Report TA18-074A", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "ColdRoot MacOS RAT", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Unusual Processes", "Suspicious WMI Use", "Asset Tracking", "Ransomware", "Command and Control", "Windows Privilege Escalation", "Windows Log Manipulation", "Windows File Extension and Association Abuse", "Prohibited Traffic Allowed or Protocol Mismatch", "Brand Monitoring", "SamSam Ransomware", "Data Protection", "Router & Infrastructure Security", "Malicious PowerShell", "Disabling Security Tools", "SQL Injection", "Windows Defense Evasion Tactics", "Emotet Malware (TA18-201A)", "Hidden Cobra Malware", "Suspicious Emails", "JBoss Vulnerability", "Suspicious DNS Traffic", "Spectre And Meltdown Vulnerabilities", "Credential Dumping", "Collection and Staging", "Netsh Abuse", "Account Monitoring and Controls", "Monitor for Updates", "Lateral Movement", "Dynamic DNS", "DNS Hijacking", "Splunk Enterprise Vulnerability", "Apache Struts Vulnerability", "Suspicious MSHTA Activity", "Monitor for Unauthorized Software"] +action.escu.fields_required = ["dest"] +action.escu.earliest_time_offset = 43200 +action.escu.latest_time_offset = 1 +description = This search returns all users that have attempted to access a particular endpoint. disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*schtasks.exe* cmdline="*/create*" cmdline="* /s *" | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` +search = | tstats count from datamodel=Authentication where Authentication.dest={dest} by _time, Authentication.dest, Authentication.user, Authentication.app, Authentication.action | `drop_dm_object_name("Authentication")` [ESCU - Detect web traffic to dynamic domain providers - Rule] action.escu = 0 @@ -6065,7 +6173,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Indicator Removal o action.escu.known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] +action.escu.analytic_story = ["Ransomware", "Windows Log Manipulation"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Suspicious wevtutil Usage action.notable = 1 @@ -6074,7 +6182,7 @@ action.notable.param.rule_description = wevtutil is the windows event log tool. action.notable.param.rule_title = Suspicious wevtutil Usage action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -6171,28 +6279,55 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name | rename Processes.process_name as process | rex field=user "(?.*)\\\\(?.*)" | `ctime(firstTime)`| `ctime(lastTime)`| search [| tstats count from datamodel=Endpoint.Processes by Processes.process_name | rare Processes.process_name limit=30 | rename Processes.process_name as process| `filter_rare_process_whitelist`| table process ] -[ESCU - AWS Network Interface details via resourceId] +[ESCU - Detect Large Outbound ICMP Packets - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-05-07 -action.escu.modification_date = 2018-05-07 +action.escu.creation_date = 2018-06-01 +action.escu.modification_date = 2018-06-01 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.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 -action.escu.full_search_name = ESCU - AWS Network Interface details via resourceId -action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command and Control"] -action.escu.fields_required = ["resourceId"] -action.escu.earliest_time_offset = 86400 -action.escu.latest_time_offset = 0 -description = This search queries AWS configuration logs and returns the information about a specific network interface via network interface ID. The information will include the ARN of the network interface, its relationships with other AWS resources, the public and the private IP associated with the network interface. +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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 +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Detect Large Outbound ICMP Packets - Rule +action.escu.mappings = {"mitre_attack": ["Command and Control", "Standard Non-Application Layer Protocol"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["Bro", "Splunk Stream", "Palo Alto Firewall"] +action.escu.analytic_story = ["Command and Control"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect Large Outbound ICMP Packets +action.notable = 1 +action.notable.param.nes_fields = src_ip, dest_ip +action.notable.param.rule_description = Large outbound ICMP packet detected. +action.notable.param.rule_title = Large ICMP packet from $src_ip$ to $dest_ip$ detected +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src_ip +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = src_ip +alert.suppress.period = 28800s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | search sourcetype=aws:config resourceId={resourceId} | table _time ARN relationships{}.resourceType relationships{}.name relationships{}.resourceId configuration.privateIpAddresses{}.privateIpAddress configuration.privateIpAddresses{}.association.publicIp +search = | tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | convert ctime(earliest) ctime(latest) [ESCU - Monitor Web Traffic For Brand Abuse - Rule] action.escu = 0 @@ -6244,43 +6379,43 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `ctime(firstTime)` | `brand_abuse_web` -[ESCU - Detect PsExec With accepteula Flag - Rule] +[ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-28 -action.escu.modification_date = 2018-03-28 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-03-12 +action.escu.modification_date = 2018-03-12 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = In this search, we are looking for the PsExec process with `accepteula` on the command line. -action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, command-line arguments, and parent process from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon Technology Add-on (TA). -action.escu.full_search_name = ESCU - Detect PsExec With accepteula Flag - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.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 +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule +action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +action.escu.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. action.escu.search_type = detection -action.escu.providing_technologies = ["Sysmon"] -action.escu.analytic_story = ["DHS Report TA18-074A", "SamSam Ransomware"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Cryptomining"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect PsExec With accepteula Flag +action.correlationsearch.label = EC2 Instance Started With Previously Unseen Instance Type action.notable = 1 -action.notable.param.nes_fields = dest,parent_process -action.notable.param.rule_description = The process pssxec.exe was run with the -accepteula flag on $dest$ by $user$. -action.notable.param.rule_title = PsExec executed with accepteula flag on $dest$. +action.notable.param.nes_fields = instanceType +action.notable.param.rule_description = The EC2 instance type $instanceType$ was used for the first time to create $dest$. +action.notable.param.rule_title = New EC2 Instance Type $instanceType$ detected action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 75 +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest, parent_process -alert.suppress.period = 86400s +alert.suppress.fields = dest +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = 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. +description = This search looks for EC2 instances being created with previously unseen instance types. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -6291,30 +6426,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=xmlwineventlog:microsoft-windows-sysmon/operational process=PsExec.exe accepteula | search cmdline=*accepteula* | stats count values(cmdline) as cmdlines, min(_time) as firstTime, max(_time) as lastTime by dest, user, parent_process | `ctime(firstTime)`| `ctime(lastTime)` | table firstTime, lastTime, count, dest, user, parent_process, cmdlines - -[ESCU - Identify Systems Receiving Remote Desktop Traffic] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-04-24 -action.escu.modification_date = 2017-09-15 -action.escu.channel = ESCU -action.escu.eli5 = This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic. -action.escu.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. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Identify Systems Receiving Remote Desktop Traffic -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Lateral Movement"] -description = This search counts the numbers of times the system has created remote desktop traffic -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - count +search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | convert ctime(earliest) ctime(latest) | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType [ESCU - Baseline of API Calls per User ARN] action.escu = 0 @@ -6353,7 +6465,7 @@ action.escu.mappings = {"mitre_attack": ["Persistence", "Privilege Escalation", action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Windows Service Abuse", "Orangeworm Attack Group", "Disabling Security Tools", "Windows Persistence Techniques"] +action.escu.analytic_story = ["Windows Service Abuse", "DHS Report TA18-074A", "Windows Persistence Techniques", "Orangeworm Attack Group", "Disabling Security Tools"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Sc.exe Manipulating Windows Services action.notable = 1 @@ -6387,44 +6499,44 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*sc.exe* AND (cmdline="* create *" OR cmdline="* config *") | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` -[ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] +[ESCU - Remote Desktop Network Bruteforce - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-07-08 -action.escu.modification_date = 2017-09-18 +action.escu.creation_date = 2018-12-14 +action.escu.modification_date = 2018-12-14 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Network_Resolution"] -action.escu.full_search_name = ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule -action.escu.mappings = {"mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} -action.escu.known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. +action.escu.eli5 = 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. +action.escu.how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Remote Desktop Network Bruteforce - Rule +action.escu.mappings = {"mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "cis20": ["CIS 12", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +action.escu.known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. action.escu.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Suspicious DNS Traffic", "DNS Hijacking", "Command and Control"] +action.escu.providing_technologies = ["Bro", "Splunk Stream"] +action.escu.analytic_story = ["SamSam Ransomware"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = DNS Query Requests Resolved by Unauthorized DNS Servers +action.correlationsearch.label = Remote Desktop Network Bruteforce action.notable = 1 action.notable.param.nes_fields = dest, src -action.notable.param.rule_description = The table represents a list of unauthorized DNS servers interacting with hosts in your network -action.notable.param.rule_title = DNS requests resolved by unauthorized DNS servers +action.notable.param.rule_description = Remote-desktop traffic detected from $src$ to $dest$. This activity is consistent with a brute-force attack. +action.notable.param.rule_title = Bruteforce Remote Desktop Network Traffic detected from $src$ to $dest$ action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 +action.risk.param._risk_score = 75 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 alert.suppress.fields = dest,src alert.suppress.period = 28800s cron_schedule = 0 * * * * -description = 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. +description = 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. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -6435,78 +6547,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` - -[ESCU - Detect Spike in S3 Bucket deletion - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-07-17 -action.escu.modification_date = 2018-11-27 -action.escu.asset_at_risk = S3 Bucket -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Detect Spike in S3 Bucket deletion - Rule -action.escu.mappings = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -action.escu.known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in S3 Bucket deletion -action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A spike in the number of S3 buckets deleted by $user$ was detected. -action.notable.param.rule_title = Spike detected in S3 bucket deletion activity by $user$. -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=DeleteBucket [search sourcetype=aws:cloudtrail eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50| where count>(stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev [ESCU - Deleting Shadow Copies - Rule] action.escu = 0 @@ -6524,7 +6565,7 @@ action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Ac action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["SamSam Ransomware", "Windows Log Manipulation", "Ransomware"] +action.escu.analytic_story = ["Ransomware", "Windows Log Manipulation", "SamSam Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Deleting Shadow Copies action.notable = 1 @@ -6533,7 +6574,7 @@ action.notable.param.rule_description = Using $process_name$ to delete shadow co action.notable.param.rule_title = Deleting Shadow Copies on $dest$ with $process_name$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -6593,7 +6634,7 @@ action.escu.full_search_name = ESCU - AWS Investigate User Activities By ARN action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS S3 Activities", "Unusual AWS EC2 Modifications", "Suspicious AWS EC2 Activities", "Suspicious AWS Login Activities"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "Suspicious AWS Login Activities", "AWS Network ACL Activity", "Suspicious AWS S3 Activities", "Unusual AWS EC2 Modifications"] action.escu.fields_required = ["arn"] action.escu.earliest_time_offset = 14400 action.escu.latest_time_offset = 0 @@ -6619,7 +6660,7 @@ action.escu.mappings = {"mitre_attack": ["Persistence", "Execution", "Scheduled action.escu.known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "Windows Persistence Techniques"] +action.escu.analytic_story = ["Windows Persistence Techniques", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Schtasks used for forcing a reboot action.notable = 1 @@ -6628,7 +6669,7 @@ action.notable.param.rule_description = This search looks for flags passed to sc action.notable.param.rule_title = Schtasks used for scheduling a force reboot action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -6712,7 +6753,7 @@ action.escu.full_search_name = ESCU - Monitor Unsuccessful Backups action.escu.known_false_positives = None at this time action.escu.search_type = support action.escu.providing_technologies = ["Netbackup"] -action.escu.analytic_story = ["SamSam Ransomware", "Monitor Backup Solution", "Ransomware"] +action.escu.analytic_story = ["Monitor Backup Solution", "Ransomware", "SamSam Ransomware"] description = 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. dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m @@ -6722,55 +6763,27 @@ schedule_window = auto is_visible = false search = sourcetype="netbackup_logs" "An error occurred, failed to backup." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE -[ESCU - Detect Large Outbound ICMP Packets - Rule] +[ESCU - Previously Seen AWS Provisioning Activity Sources] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-06-01 -action.escu.modification_date = 2018-06-01 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-03-16 +action.escu.modification_date = 2018-03-16 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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 -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Detect Large Outbound ICMP Packets - Rule -action.escu.mappings = {"mitre_attack": ["Command and Control", "Standard Non-Application Layer Protocol"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["Bro", "Splunk Stream", "Palo Alto Firewall"] -action.escu.analytic_story = ["Command and Control"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Large Outbound ICMP Packets -action.notable = 1 -action.notable.param.nes_fields = src_ip, dest_ip -action.notable.param.rule_description = Large outbound ICMP packet detected. -action.notable.param.rule_title = Large ICMP packet from $src_ip$ to $dest_ip$ detected -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 28800s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -70m@m +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Previously Seen AWS Provisioning Activity Sources +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] +description = 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. +dispatch.earliest_time = -90d@d dispatch.latest_time = -10m@m disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | convert ctime(earliest) ctime(latest) +search = sourcetype=aws: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 - Extended Period Without Successful Netbackup Backups - Rule] action.escu = 0 @@ -6844,28 +6857,54 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:description id={networkAclId} | table id account_id vpc_id network_acl_entries{}.* -[ESCU - Get EC2 Instance Details by instanceId] +[ESCU - Detect PsExec With accepteula Flag - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-02-12 -action.escu.modification_date = 2018-02-12 +action.escu.creation_date = 2018-03-28 +action.escu.modification_date = 2018-03-28 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.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. -action.escu.full_search_name = ESCU - Get EC2 Instance Details by instanceId -action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Unusual AWS EC2 Modifications", "Suspicious AWS EC2 Activities"] -action.escu.fields_required = ["instanceId"] -action.escu.earliest_time_offset = 86400 -action.escu.latest_time_offset = 0 -description = This search queries AWS description logs and returns all the information about a specific instance via the instanceId field +action.escu.confidence = medium +action.escu.eli5 = In this search, we are looking for the PsExec process with `accepteula` on the command line. +action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, command-line arguments, and parent process from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon Technology Add-on (TA). +action.escu.full_search_name = ESCU - Detect PsExec With accepteula Flag - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.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 +action.escu.search_type = detection +action.escu.providing_technologies = ["Sysmon"] +action.escu.analytic_story = ["DHS Report TA18-074A", "SamSam Ransomware"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect PsExec With accepteula Flag +action.notable = 1 +action.notable.param.nes_fields = dest,parent_process +action.notable.param.rule_description = The process pssxec.exe was run with the -accepteula flag on $dest$ by $user$. +action.notable.param.rule_title = PsExec executed with accepteula flag on $dest$. +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 75 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest, parent_process +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | search sourcetype="aws:description" source="*:ec2_instances"| dedup id sortby -_time | search id={instanceId} | spath output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC +search = sourcetype=xmlwineventlog:microsoft-windows-sysmon/operational process=PsExec.exe accepteula | search cmdline=*accepteula* | stats count values(cmdline) as cmdlines, min(_time) as firstTime, max(_time) as lastTime by dest, user, parent_process | `ctime(firstTime)`| `ctime(lastTime)` | table firstTime, lastTime, count, dest, user, parent_process, cmdlines [ESCU - Reg.exe Manipulating Windows Services Registry Keys - Rule] action.escu = 0 @@ -7017,44 +7056,43 @@ schedule_window = auto is_visible = false search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by Country | eval newCountry=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCountry=1 | table Country] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, Country, eventName, errorCode -[ESCU - Processes created by netsh - Rule] +[ESCU - Suspicious Java Classes - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-01-04 -action.escu.modification_date = 2018-11-02 +action.escu.creation_date = 2018-12-06 +action.escu.modification_date = 2018-12-06 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Processes created by netsh - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.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. +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Suspicious Java Classes - Rule +action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 7", "CIS 12"], "nist": ["DE.AE"]} +action.escu.known_false_positives = There are no known false positives. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Netsh Abuse"] +action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Apache"] +action.escu.analytic_story = ["Apache Struts Vulnerability"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Processes created by netsh +action.correlationsearch.label = Suspicious Java Classes action.notable = 1 -action.notable.param.nes_fields = dest, process, parent_process -action.notable.param.rule_description = A process, $process$, is spawned by netsh.exe. It is highly unlikely for netsh to have any child processes. -action.notable.param.rule_title = Process spawned by netsh.exe detected on $dest$ -action.notable.param.security_domain = endpoint +action.notable.param.nes_fields = src, url, http_user_agent +action.notable.param.rule_description = The host $src$ with user agent $http_user_agent$ is sending web traffic to $url$, which contains suspicious Java classes. These classes may be indicative of remote code execution in Java frameworks, such as Apache Struts. +action.notable.param.rule_title = Suspicious Java Classes: Possible RCE against Struts or similar Java framework from $src$ +action.notable.param.security_domain = threat action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Investigate Suspicious Strings in HTTP Header\n - ESCU - Investigate Web POSTs From src\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest +action.risk.param._risk_object = src action.risk.param._risk_object_type = system action.risk.param._risk_score = 50 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest, process -alert.suppress.period = 86400s +alert.suppress.fields = src, url, http_user_agent +alert.suppress.period = 3600s cron_schedule = 0 * * * * -description = 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. +description = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -7065,57 +7103,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="C:\Windows\System32\netsh.exe" by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `ctime(firstTime)`|`ctime(lastTime)` - -[ESCU - Registry Keys for Creating SHIM Databases - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-08-27 -action.escu.modification_date = 2017-09-15 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Change_Analysis"] -action.escu.full_search_name = ESCU - Registry Keys for Creating SHIM Databases - Rule -action.escu.mappings = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Windows Persistence Techniques", "Suspicious Windows Registry Activities"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Registry Keys for Creating SHIM Databases -action.notable = 1 -action.notable.param.nes_fields = dest, user -action.notable.param.rule_description = A registry key that is used for persistence on Windows was modified on $dest$ by $user$ -action.notable.param.rule_title = Registry Key Associated With SHIM databases on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Registry Activities\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,object_path -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `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="*CurrentVersion\\AppCompatFlags\\Custom*" OR All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\InstalledSDB*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `drop_dm_object_name("All_Changes")` +search = sourcetype="stream:http" http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | convert ctime(firstTime) ctime(lastTime) [ESCU - Baseline of S3 Bucket deletion activity by ARN] action.escu = 0 @@ -7139,55 +7127,52 @@ schedule_window = auto is_visible = false search = sourcetype=aws: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 - Prohibited Network Traffic Allowed - Rule] +[ESCU - Investigate Web Activity From Host] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-04-18 -action.escu.modification_date = 2017-09-11 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2017-04-21 +action.escu.modification_date = 2017-11-09 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Prohibited Network Traffic Allowed - Rule -action.escu.mappings = {"mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "kill_chain_phases": ["Delivery", "Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE", "PR.AC"]} -action.escu.known_false_positives = None identified -action.escu.search_type = detection -action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] -action.escu.analytic_story = ["Ransomware", "Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Prohibited Network Traffic Allowed -action.notable = 1 -action.notable.param.nes_fields = src_ip, dest_ip -action.notable.param.rule_description = This search looks for network traffic defined by port and transport in the ES lookup table "lookup_interesting_ports", that is marked as prohibited, and yet has an 'allow' action in the Network_Traffic data model. This should help to identify areas where a network device is not properly configured. -action.notable.param.rule_title = Prohibited Network Traffic Allowed from $src_ip$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest_ip,src_ip -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search you must be ingesting your web traffic and populating the Web data model. +action.escu.data_models = ["Web"] +action.escu.full_search_name = ESCU - Investigate Web Activity From Host +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +action.escu.analytic_story = ["Host Redirection", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Unusual Processes", "Ransomware", "Brand Monitoring", "SamSam Ransomware", "Emotet Malware (TA18-201A)", "Suspicious Emails", "Credential Dumping", "Netsh Abuse", "Monitor for Unauthorized Software"] +action.escu.fields_required = ["dest"] +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 3600 +description = This search allows you to find all the web activity from a specific host. During an investigation, it is important to profile web activity to characterize user or host activity. disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` +search = | from datamodel Web.Web | search src={dest} + +[ESCU - Get All AWS Activity From City] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-03-19 +action.escu.modification_date = 2018-03-19 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.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. +action.escu.full_search_name = ESCU - Get All AWS Activity From City +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] +action.escu.fields_required = ["City"] +action.escu.earliest_time_offset = 14400 +action.escu.latest_time_offset = 0 +description = This search retrieves all the activity from a specific city and will create a table containing the time, city, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search City={City} | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, City, user, userName, userType, src_ip, awsRegion, eventName, errorCode [ESCU - Monitor Email For Brand Abuse - Rule] action.escu = 0 @@ -7239,6 +7224,55 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id | `drop_dm_object_name("All_Email")` | `ctime(firstTime)` | `ctime(lastTime)` | eval temp=split(src_user, "@") | eval email_domain=mvindex(temp, 1) | lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse | search domain_abuse=true | table message_id, src_user, email_domain, recipients, firstTime, lastTime +[ESCU - AWS Network Access Control List Deleted - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-01-08 +action.escu.modification_date = 2017-01-10 +action.escu.asset_at_risk = AWS Instance +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - AWS Network Access Control List Deleted - Rule +action.escu.mappings = {"mitre_attack": ["Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11"], "nist": ["DE.DP", "DE.AE"]} +action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Network ACL Activity"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = AWS Network Access Control List Deleted +action.notable = 1 +action.notable.param.nes_fields = src, src_user, eventName +action.notable.param.rule_description = AWS network ACL has been deleted by $src_user. +action.notable.param.rule_title = AWS Network ACL deleted by $src_user$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network ACL Details from ID\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src_user +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 80 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = src_user +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -1d@d +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `ctime(lastTime)` | `ctime(firstTime)` + [ESCU - Windows Updates Install Failures] action.escu = 0 action.escu.enabled = 1 @@ -7252,7 +7286,7 @@ action.escu.full_search_name = ESCU - Windows Updates Install Failures action.escu.known_false_positives = None at this time action.escu.search_type = support action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] +action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] description = 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. dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m @@ -7262,29 +7296,54 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure by _time span=1d -[ESCU - Get Authentication Logs For Endpoint] +[ESCU - Schtasks scheduling job on remote system - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-04-10 -action.escu.modification_date = 2017-11-01 +action.escu.creation_date = 2016-09-13 +action.escu.modification_date = 2017-09-15 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.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. -action.escu.data_models = ["Authentication"] -action.escu.full_search_name = ESCU - Get Authentication Logs For Endpoint -action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["Microsoft Windows", "Linux", "macOS"] -action.escu.analytic_story = ["Windows Privilege Escalation", "Hidden Cobra Malware", "Windows File Extension and Association Abuse", "Monitor for Updates", "Unusual Processes", "ColdRoot MacOS RAT", "Account Monitoring and Controls", "Netsh Abuse", "DHS Report TA18-074A", "Data Protection", "SamSam Ransomware", "Windows Defense Evasion Tactics", "Dynamic DNS", "Apache Struts Vulnerability", "Windows Service Abuse", "Windows Log Manipulation", "Orangeworm Attack Group", "Disabling Security Tools", "JBoss Vulnerability", "Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious WMI Use", "Suspicious DNS Traffic", "Emotet Malware (TA18-201A)", "DNS Hijacking", "Host Redirection", "Suspicious Command-Line Executions", "Brand Monitoring", "Splunk Enterprise Vulnerability", "Credential Dumping", "Prohibited Traffic Allowed or Protocol Mismatch", "Asset Tracking", "Windows Persistence Techniques", "Command and Control", "Suspicious Emails", "Suspicious Windows Registry Activities", "Router & Infrastructure Security", "Malicious PowerShell", "SQL Injection", "Lateral Movement", "Collection and Staging", "Monitor for Unauthorized Software", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] -action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 43200 -action.escu.latest_time_offset = 1 -description = This search returns all users that have attempted to access a particular endpoint. +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Schtasks scheduling job on remote system - Rule +action.escu.mappings = {"mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task", "Remote Services"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "nist": ["PR.IP"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Lateral Movement"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Schtasks scheduling job on remote system +action.notable = 1 +action.notable.param.nes_fields = dest, user, process +action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. +action.notable.param.rule_title = Schtasks scheduling job on remote system +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,cmdline +alert.suppress.period = 28800s +cron_schedule = 0 * * * * +description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats count from datamodel=Authentication where Authentication.dest={dest} by _time, Authentication.dest, Authentication.user, Authentication.app, Authentication.action | `drop_dm_object_name("Authentication")` +search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*schtasks.exe* cmdline="*/create*" cmdline="* /s *" | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` [ESCU - Single Letter Process On Endpoint - Rule] action.escu = 0 @@ -7386,31 +7445,31 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.protocol="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) groupby All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` -[ESCU - Suspicious Java Classes - Rule] +[ESCU - Attempt To Stop Security Service - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-12-06 -action.escu.modification_date = 2018-12-06 +action.escu.creation_date = 2018-04-09 +action.escu.modification_date = 2017-09-15 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Suspicious Java Classes - Rule -action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 7", "CIS 12"], "nist": ["DE.AE"]} -action.escu.known_false_positives = There are no known false positives. +action.escu.confidence = high +action.escu.eli5 = 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. +action.escu.how_to_implement = You must 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. 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., +action.escu.full_search_name = ESCU - Attempt To Stop Security Service - Rule +action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. action.escu.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Apache"] -action.escu.analytic_story = ["Apache Struts Vulnerability"] +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Disabling Security Tools"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious Java Classes +action.correlationsearch.label = Attempt To Stop Security Service action.notable = 1 -action.notable.param.nes_fields = src, url, http_user_agent -action.notable.param.rule_description = The host $src$ with user agent $http_user_agent$ is sending web traffic to $url$, which contains suspicious Java classes. These classes may be indicative of remote code execution in Java frameworks, such as Apache Struts. -action.notable.param.rule_title = Suspicious Java Classes: Possible RCE against Struts or similar Java framework from $src$ -action.notable.param.security_domain = threat -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Investigate Suspicious Strings in HTTP Header\n - ESCU - Investigate Web POSTs From src\n"} +action.notable.param.nes_fields = dest, process, user +action.notable.param.rule_description = Attempt to stop a security-related service on $dest$ +action.notable.param.rule_title = Attempt to Stop Security Service On $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -7419,10 +7478,10 @@ action.risk.param._risk_score = 50 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = src, url, http_user_agent -alert.suppress.period = 3600s +alert.suppress.fields = dest, user +alert.suppress.period = 86400s cron_schedule = 0 * * * * -description = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. +description = This search looks for attempts to stop security-related services on the endpoint. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -7433,7 +7492,57 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype="stream:http" http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | convert ctime(firstTime) ctime(lastTime) +search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational (process=net.exe OR process=sc.exe) cmdline="* stop *" | lookup security_services_lookup service as cmdline OUTPUTNEW category, description | search category=security | table _time, dest, user, parent_process, cmdline, description + +[ESCU - Processes created by netsh - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-01-04 +action.escu.modification_date = 2018-11-02 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Processes created by netsh - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Netsh Abuse"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Processes created by netsh +action.notable = 1 +action.notable.param.nes_fields = dest, process, parent_process +action.notable.param.rule_description = A process, $process$, is spawned by netsh.exe. It is highly unlikely for netsh to have any child processes. +action.notable.param.rule_title = Process spawned by netsh.exe detected on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest, process +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="C:\Windows\System32\netsh.exe" by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `ctime(firstTime)`|`ctime(lastTime)` [ESCU - Add Prohibited Processes to Enterprise Security] action.escu = 0 @@ -7508,55 +7617,29 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `ctime(firstTime)`| `brand_abuse_dns` -[ESCU - Remote Desktop Network Traffic - Rule] +[ESCU - Get First Occurrence and Last Occurrence of a MAC Address] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-09-13 -action.escu.modification_date = 2017-09-15 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2017-06-14 +action.escu.modification_date = 2017-09-13 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Remote Desktop Network Traffic - Rule -action.escu.mappings = {"mitre_attack": ["Lateral Movement", "Remote Desktop Protocol", "Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} -action.escu.known_false_positives = Remote Desktop may be used legitimately by users on the network. -action.escu.search_type = detection -action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["Hidden Cobra Malware", "SamSam Ransomware", "Lateral Movement"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote Desktop Network Traffic -action.notable = 1 -action.notable.param.nes_fields = dest, src -action.notable.param.rule_description = Remote Desktop Traffic detected between $src$ and $dest$. These two systems typically do not communicate with RDP -action.notable.param.rule_title = Uncommon Remote Desktop Network Traffic between $src$ and $dest$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,src -alert.suppress.period = 28800s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server. +action.escu.data_models = ["Network_Sessions"] +action.escu.full_search_name = ESCU - Get First Occurrence and Last Occurrence of a MAC Address +action.escu.known_false_positives = None at this time +action.escu.search_type = contextual +action.escu.providing_technologies = ["Splunk Stream", "Bro", "Microsoft Windows"] +action.escu.analytic_story = ["Asset Tracking"] +action.escu.fields_required = ["src_mac"] +action.escu.earliest_time_offset = 864000 +action.escu.latest_time_offset = 86400 +description = This search allows you to gather more context around a notable which has detected a new device connecting to your network. Use this search to determine the first and last occurrences of the suspicious device attempting to connect with your network. disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `ctime(firstTime)`| `ctime(lastTime)` +search = | tstats allow_old_summaries=true count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST All_Sessions.All_Sessions.src_mac= {src_mac} by All_Sessions.src_ip All_Sessions.user | `ctime(lastTime)` | `ctime(firstTime)` [ESCU - Create local admin accounts using net.exe - Rule] action.escu = 0 @@ -7753,28 +7836,55 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=reg.exe by Processes.user Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search process=*save* (process=*HKLM\\sam* OR process=*HKLM\\system*) -[ESCU - Systems Ready for Spectre-Meltdown Windows Patch] +[ESCU - Malicious PowerShell Process - Encoded Command - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-01-08 -action.escu.modification_date = 2018-01-08 +action.escu.creation_date = 2016-09-18 +action.escu.modification_date = 2018-12-03 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Change_Analysis"] -action.escu.full_search_name = ESCU - Systems Ready for Spectre-Meltdown Windows Patch -action.escu.known_false_positives = None at this time -action.escu.search_type = support +action.escu.confidence = medium +action.escu.eli5 = 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. +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 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. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Malicious PowerShell Process - Encoded Command - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.known_false_positives = System administrators may use this option, but it's not common. +action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Spectre And Meltdown Vulnerabilities"] -description = 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. -dispatch.earliest_time = -1d@d +action.escu.analytic_story = ["Malicious PowerShell"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Malicious PowerShell Process - Encoded Command +action.notable = 1 +action.notable.param.nes_fields = dest, user, process_name +action.notable.param.rule_description = The system $dest$ executed a PowerShell process that has an encoded command on the command-line +action.notable.param.rule_title = PowerShell process with an encoded command detected on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 20 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest, user, process_name +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = 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. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `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 | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name("All_Changes")` +search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search process=*-EncodedCommand* OR process=*-enc* [ESCU - Get EC2 Launch Details] action.escu = 0 @@ -7878,7 +7988,7 @@ action.escu.full_search_name = ESCU - Previously Seen AWS Regions action.escu.known_false_positives = None at this time action.escu.search_type = support action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "AWS Cryptomining"] description = 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 dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m @@ -7900,7 +8010,7 @@ action.escu.full_search_name = ESCU - Baseline of blocked outbound traffic from action.escu.known_false_positives = None at this time action.escu.search_type = support action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "AWS Network ACL Activity", "Suspicious AWS Traffic"] description = 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. dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m @@ -7955,6 +8065,55 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:cloudtrail userIdentity.arn={src_user} | spath output=user path=userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage +[ESCU - Detect New Open S3 buckets - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-07-25 +action.escu.modification_date = 2018-07-25 +action.escu.asset_at_risk = S3 Bucket +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Detect New Open S3 buckets - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS S3 Activities"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect New Open S3 buckets +action.notable = 1 +action.notable.param.nes_fields = user +action.notable.param.rule_description = An open/public S3 bucket, $bucketName$, was created by $user$. +action.notable.param.rule_title = Public S3 bucket $bucketName$ created by $user$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = user +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 70 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = user,bucketName +alert.suppress.period = 86400s +cron_schedule = 5 * * * * +description = This search looks for CloudTrail events where a user has created an open/public S3 bucket. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail AllUsers eventName=PutBucketAcl | spath output=userIdentityArn path=userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | spath output=aclControlList path=requestParameters.AccessControlPolicy.AccessControlList | spath input=aclControlList output=grantee path=Grant{} | mvexpand grantee | spath input=grantee | search Grantee.URI=*AllUsers | rename userIdentityArn as user| table _time, src,awsRegion Permission, Grantee.URI, bucketName, user + [ESCU - Get DNS traffic ratio] action.escu = 0 action.escu.enabled = 1 @@ -7968,7 +8127,7 @@ action.escu.full_search_name = ESCU - Get DNS traffic ratio action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Dynamic DNS", "Suspicious DNS Traffic", "DNS Hijacking", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic", "Dynamic DNS", "DNS Hijacking"] action.escu.fields_required = ["src_ip", "dest_ip"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 86400 @@ -8045,7 +8204,7 @@ action.escu.mappings = {"mitre_attack": ["Commonly Used Port", "Credential Acces action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["Hidden Cobra Malware", "DHS Report TA18-074A"] +action.escu.analytic_story = ["DHS Report TA18-074A", "Hidden Cobra Malware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Detect Outbound SMB Traffic action.notable = 1 @@ -8054,7 +8213,7 @@ action.notable.param.rule_description = Outbound SMB network traffic detected. action.notable.param.rule_title = Outbound SMB traffic from $src_ip$ to $dest_ip$ detected action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Outbound Emails to Hidden Cobra Threat Actors\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src_ip @@ -8079,28 +8238,29 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb) by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | convert ctime(earliest) ctime(latest) -[ESCU - Count of Unique IPs Connecting to Ports] +[ESCU - Get Process Info] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-06-24 -action.escu.modification_date = 2017-09-13 +action.escu.creation_date = 2017-03-15 +action.escu.modification_date = 2017-11-09 action.escu.channel = ESCU -action.escu.eli5 = 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. -action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Count of Unique IPs Connecting to Ports +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data and populating the Application_State data model. +action.escu.data_models = ["Application_State"] +action.escu.full_search_name = ESCU - Get Process Info action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch"] -description = 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. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m +action.escu.search_type = investigative +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Windows Service Abuse", "DHS Report TA18-074A", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Unusual Processes", "Suspicious WMI Use", "Ransomware", "Command and Control", "Windows Privilege Escalation", "Windows Log Manipulation", "Windows File Extension and Association Abuse", "SamSam Ransomware", "Malicious PowerShell", "Disabling Security Tools", "Windows Defense Evasion Tactics", "Emotet Malware (TA18-201A)", "Hidden Cobra Malware", "Credential Dumping", "Collection and Staging", "Netsh Abuse", "Lateral Movement", "Suspicious MSHTA Activity", "Monitor for Unauthorized Software"] +action.escu.fields_required = ["process", "dest"] +action.escu.earliest_time_offset = 7200 +action.escu.latest_time_offset = 7200 +description = This search queries the Application State data model to give you details about the process running on a host which is under investigation. To gather the process info, enter the values for the process name in question and the destination IP address. disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `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 +search = | from datamodel Application_State.Processes | search process=*{process}* dest={dest} [ESCU - Detect Spike in Security Group Activity - Rule] action.escu = 0 @@ -8337,7 +8497,7 @@ action.escu.mappings = {"mitre_attack": ["Persistence", "Registry Run Keys / Sta action.escu.known_false_positives = There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Ransomware", "Emotet Malware (TA18-201A)", "Windows Persistence Techniques", "Suspicious Windows Registry Activities", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "DHS Report TA18-074A", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Ransomware", "Emotet Malware (TA18-201A)", "Suspicious MSHTA Activity"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Registry Keys Used For Persistence action.notable = 1 @@ -8346,7 +8506,7 @@ action.notable.param.rule_description = A registry key that is used for persiste action.notable.param.rule_title = Registry Key Associated With Persistence Modified on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Registry Activities\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -8371,45 +8531,45 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest , Registry.status, Registry.user | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(Registry)` -[ESCU - Batch File Write to System32 - Rule] +[ESCU - SMB Traffic Spike - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-12-14 -action.escu.modification_date = 2018-12-14 +action.escu.creation_date = 2017-08-20 +action.escu.modification_date = 2017-09-10 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = 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. -action.escu.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. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Batch File Write to System32 - Rule -action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.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. +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - SMB Traffic Spike - Rule +action.escu.mappings = {"mitre_attack": ["Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} +action.escu.known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["SamSam Ransomware"] +action.escu.providing_technologies = ["Bro", "Splunk Stream"] +action.escu.analytic_story = ["DHS Report TA18-074A", "Ransomware", "Emotet Malware (TA18-201A)", "Hidden Cobra Malware"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Batch File Write to System32 +action.correlationsearch.label = SMB Traffic Spike action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A batch file was written to the system directory on $dest$. -action.notable.param.rule_title = Batch file write to system32 detected on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.nes_fields = src +action.notable.param.rule_description = There was a spike in SMB traffic from $src$. +action.notable.param.rule_title = SMB Traffic Spike from $src$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Outbound Emails to Hidden Cobra Threat Actors\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest +action.risk.param._risk_object = src action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 +action.risk.param._risk_score = 50 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 14400s +alert.suppress.fields = src +alert.suppress.period = 28800s cron_schedule = 0 * * * * -description = The search looks for a batch file (.bat) written to the Windows system directory tree. -dispatch.earliest_time = -70m@m +description = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. +dispatch.earliest_time = -7d@d dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -8419,7 +8579,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat +search = | tstats `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=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count [ESCU - AWS Investigate User Activities By AccessKeyId] action.escu = 0 @@ -8594,68 +8754,68 @@ schedule_window = auto 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"| `ctime(endDate)` | `ctime(startDate)`| table identity empStatus endDate startDate -[ESCU - Get First Occurrence and Last Occurrence of a MAC Address] +[ESCU - Identify Systems Using Remote Desktop] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-06-14 -action.escu.modification_date = 2017-09-13 +action.escu.creation_date = 2017-04-18 +action.escu.modification_date = 2017-09-15 action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server. -action.escu.data_models = ["Network_Sessions"] -action.escu.full_search_name = ESCU - Get First Occurrence and Last Occurrence of a MAC Address +action.escu.eli5 = 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 in the Application_State data model. +action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. +action.escu.data_models = ["Application_State"] +action.escu.full_search_name = ESCU - Identify Systems Using Remote Desktop action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Microsoft Windows"] -action.escu.analytic_story = ["Asset Tracking"] -action.escu.fields_required = ["src_mac"] -action.escu.earliest_time_offset = 864000 -action.escu.latest_time_offset = 86400 -description = This search allows you to gather more context around a notable which has detected a new device connecting to your network. Use this search to determine the first and last occurrences of the suspicious device attempting to connect with your network. +action.escu.search_type = support +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Lateral Movement"] +description = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. +dispatch.earliest_time = -30d@d +dispatch.latest_time = -10m@m disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats allow_old_summaries=true count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST All_Sessions.All_Sessions.src_mac= {src_mac} by All_Sessions.src_ip All_Sessions.user | `ctime(lastTime)` | `ctime(firstTime)` +search = | tstats `summariesonly` count from datamodel=Application_State where All_Application_State.process="*mstsc.exe*" by All_Application_State.dest All_Application_State.process | `drop_dm_object_name("All_Application_State")` | sort - count -[ESCU - Attempt To Stop Security Service - Rule] +[ESCU - Spike in File Writes - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-04-09 -action.escu.modification_date = 2017-09-15 +action.escu.creation_date = 2017-08-20 +action.escu.modification_date = 2018-12-03 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = 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. -action.escu.how_to_implement = You must 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. 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., -action.escu.full_search_name = ESCU - Attempt To Stop Security Service - Rule -action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -action.escu.known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. +action.escu.confidence = low +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Spike in File Writes - Rule +action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} +action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Disabling Security Tools"] +action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Attempt To Stop Security Service +action.correlationsearch.label = Spike in File Writes action.notable = 1 -action.notable.param.nes_fields = dest, process, user -action.notable.param.rule_description = Attempt to stop a security-related service on $dest$ -action.notable.param.rule_title = Attempt to Stop Security Service On $dest$ +action.notable.param.nes_fields = dest +action.notable.param.rule_description = A sharp increase in file writes was detected on $dest +action.notable.param.rule_title = Spike in file writes on $dest$ action.notable.param.security_domain = endpoint -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.severity = low +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = src +action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest, user -alert.suppress.period = 86400s +alert.suppress.fields = dest +alert.suppress.period = 7200s cron_schedule = 0 * * * * -description = This search looks for attempts to stop security-related services on the endpoint. -dispatch.earliest_time = -70m@m +description = The search looks for a sharp increase in the number of files written to a particular host +dispatch.earliest_time = -7d@d dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -8665,29 +8825,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational (process=net.exe OR process=sc.exe) cmdline="* stop *" | lookup security_services_lookup service as cmdline OUTPUTNEW category, description | search category=security | table _time, dest, user, parent_process, cmdline, description - -[ESCU - Previously seen API call per user roles in CloudTrail] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-04-01 -action.escu.modification_date = 2018-04-16 -action.escu.channel = ESCU -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Previously seen API call per user roles in CloudTrail -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS User Monitoring"] -description = 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. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws: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 +search = | tstats `summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest | `drop_dm_object_name(Filesystem)` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time upperBound) AND num_data_samples >=20, 1, 0) | search isOutlier=1 [ESCU - Uncommon Processes On Endpoint - Rule] action.escu = 0 @@ -8705,7 +8843,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "Accessibility Features"], action.escu.known_false_positives = None identified action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Privilege Escalation", "Unusual Processes"] +action.escu.analytic_story = ["Unusual Processes", "Windows Privilege Escalation"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Uncommon Processes On Endpoint action.notable = 1 @@ -8714,7 +8852,7 @@ action.notable.param.rule_description = Prohibited software $process_name$ has b action.notable.param.rule_title = Prohibited Software Detected On $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -8739,92 +8877,43 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Application_State by All_Application_State.dest All_Application_State.user All_Application_State.process | `ctime(firstTime)`| `ctime(lastTime)` | `drop_dm_object_name("All_Application_State")` | `uncommon_processes` -[ESCU - Detect New Open S3 buckets - Rule] +[ESCU - Process Execution via WMI - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-07-25 -action.escu.modification_date = 2018-07-25 -action.escu.asset_at_risk = S3 Bucket +action.escu.creation_date = 2018-10-23 +action.escu.modification_date = 2018-10-23 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Detect New Open S3 buckets - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -action.escu.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. +action.escu.eli5 = 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. +action.escu.how_to_implement = To successfully implement this search, you must 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. +action.escu.full_search_name = ESCU - Process Execution via WMI - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes. action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS S3 Activities"] +action.escu.providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Suspicious WMI Use"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect New Open S3 buckets +action.correlationsearch.label = Process Execution via WMI action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = An open/public S3 bucket, $bucketName$, was created by $user$. -action.notable.param.rule_title = Public S3 bucket $bucketName$ created by $user$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user,bucketName -alert.suppress.period = 86400s -cron_schedule = 5 * * * * -description = This search looks for CloudTrail events where a user has created an open/public S3 bucket. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail AllUsers eventName=PutBucketAcl | spath output=userIdentityArn path=userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | spath output=aclControlList path=requestParameters.AccessControlPolicy.AccessControlList | spath input=aclControlList output=grantee path=Grant{} | mvexpand grantee | spath input=grantee | search Grantee.URI=*AllUsers | rename userIdentityArn as user| table _time, src,awsRegion Permission, Grantee.URI, bucketName, user - -[ESCU - EC2 Instance Modified With Previously Unseen User - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-04-09 -action.escu.modification_date = 2018-04-09 -action.escu.asset_at_risk = AWS Instance -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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 `ec2ModificationAPIs`. 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. -action.escu.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 `ec2ModificationAPIs`. -action.escu.full_search_name = ESCU - EC2 Instance Modified With Previously Unseen User - Rule -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Unusual AWS EC2 Modifications"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Modified With Previously Unseen User -action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = The EC2 instance $dest$ was modified by $user$. This user has never modified an EC2 instance before. -action.notable.param.rule_title = EC2 Instance Modified By Previously Unseen User $user$ +action.notable.param.nes_fields = dest, user, process +action.notable.param.rule_description = This search looks for child processes of WmiPrvSE.exe, which indicates that a process was launched via WMI. +action.notable.param.rule_title = Process launched via WMI on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Instance Details by instanceId\n - ESCU - Get Notable History\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Get Sysmon WMI Activity for Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 +action.risk.param._risk_score = 70 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = user, dest -alert.suppress.period = 14400s +alert.suppress.fields = dest,user +alert.suppress.period = 28800s cron_schedule = 0 * * * * -description = This search looks for EC2 instances being modified by users who have not previously modified them. +description = This search looks for processes launched via WMI. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -8835,7 +8924,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail `ec2ModificationAPIs` [search sourcetype=aws:cloudtrail `ec2ModificationAPIs` errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `ctime(firstTime)` | `ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest +search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) parent_process=*WmiPrvSE.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, parent_process, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` [ESCU - Remote Process Instantiation via WMI - Rule] action.escu = 0 @@ -8852,7 +8941,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "Windows Management Instru action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "Suspicious WMI Use"] +action.escu.analytic_story = ["Suspicious WMI Use", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Remote Process Instantiation via WMI action.notable = 1 @@ -8861,7 +8950,7 @@ action.notable.param.rule_description = This search looks for wmic.exe being lau action.notable.param.rule_title = Remote process instantiation via WMI on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Get Sysmon WMI Activity for Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -8886,6 +8975,56 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*wmic* cmdline="*/node*" cmdline="*process*" cmdline="*call*" cmdline="*create*" | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` +[ESCU - Batch File Write to System32 - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-12-14 +action.escu.modification_date = 2018-12-14 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = high +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Batch File Write to System32 - Rule +action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.analytic_story = ["SamSam Ransomware"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Batch File Write to System32 +action.notable = 1 +action.notable.param.nes_fields = dest, file_name +action.notable.param.rule_description = A batch file was written to the system directory on $dest$. +action.notable.param.rule_title = Batch file write to system32 detected on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 80 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,file_name +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = The search looks for a batch file (.bat) written to the Windows system directory tree. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat + [ESCU - Windows Updates Install Successes] action.escu = 0 action.escu.enabled = 1 @@ -8899,7 +9038,7 @@ action.escu.full_search_name = ESCU - Windows Updates Install Successes action.escu.known_false_positives = None at this time action.escu.search_type = support action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["SamSam Ransomware", "Ransomware"] +action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] description = 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. dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m @@ -8959,28 +9098,54 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name(Processes)` -[ESCU - Get Parent Process Info] +[ESCU - EC2 Instance Started With Previously Unseen AMI - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-08-22 -action.escu.modification_date = 2017-09-10 +action.escu.creation_date = 2018-03-12 +action.escu.modification_date = 2018-03-12 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data via Microsoft-Windows-Sysmon and extract the Image and Parent Image field. -action.escu.full_search_name = ESCU - Get Parent Process Info -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Privilege Escalation", "Hidden Cobra Malware", "Windows File Extension and Association Abuse", "Netsh Abuse", "DHS Report TA18-074A", "SamSam Ransomware", "Windows Defense Evasion Tactics", "Windows Service Abuse", "Orangeworm Attack Group", "Disabling Security Tools", "Ransomware", "Emotet Malware (TA18-201A)", "Suspicious Command-Line Executions", "Credential Dumping", "Windows Persistence Techniques", "Suspicious Windows Registry Activities", "Collection and Staging", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] -action.escu.fields_required = ["process", "dest"] -action.escu.earliest_time_offset = 0 -action.escu.latest_time_offset = 86400 -description = This search queries the Application State data model to give you details about the parent process of a process running on a host which is under investigation. Enter the values of the process name in question and the dest_ip +action.escu.confidence = medium +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen AMI - Rule +action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +action.escu.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. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Cryptomining"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = EC2 Instance Started With Previously Unseen AMI +action.notable = 1 +action.notable.param.nes_fields = dest +action.notable.param.rule_description = The EC2 instance $dest$ was created with previously unused AMI $amiID$ +action.notable.param.rule_title = EC2 Instance Type $dest$ Created With New AMI +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 30 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search looks for EC2 instances being created with previously unseen AMIs. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | search sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational process={process} dest={dest} | table parent_process parent_process_id +search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instancesSet.items{}.imageId | rename requestParameters.instancesSet.items{}.imageId as amiID | inputlookup append=t previously_seen_ec2_amis.csv | stats min(earliest) as earliest max(latest) as latest by amiID | outputlookup previously_seen_ec2_amis.csv | eval newAMI=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | convert ctime(earliest) ctime(latest) | where newAMI=1 | rename amiID as requestParameters.instancesSet.items{}.imageId | table requestParameters.instancesSet.items{}.imageId] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as arn, requestParameters.instancesSet.items{}.imageId as amiID | table _time, arn, amiID, dest, instanceType [ESCU - DNSTwist Domain Names] action.escu = 0 @@ -9020,7 +9185,7 @@ action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration", action.escu.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. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Data Protection", "Suspicious DNS Traffic", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "Data Protection", "Suspicious DNS Traffic"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Detection of DNS Tunnels action.notable = 1 @@ -9029,7 +9194,7 @@ action.notable.param.rule_description = Potential DNS tunnel detected from $src$ action.notable.param.rule_title = DNS tunnel detected on $src$ action.notable.param.security_domain = network action.notable.param.severity = low -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -9054,55 +9219,28 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 -[ESCU - Malicious PowerShell Process - Encoded Command - Rule] +[ESCU - Systems Ready for Spectre-Meltdown Windows Patch] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-09-18 -action.escu.modification_date = 2018-12-03 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-01-08 +action.escu.modification_date = 2018-01-08 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -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 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. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Malicious PowerShell Process - Encoded Command - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -action.escu.known_false_positives = System administrators may use this option, but it's not common. -action.escu.search_type = detection +action.escu.eli5 = 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. +action.escu.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. +action.escu.data_models = ["Change_Analysis"] +action.escu.full_search_name = ESCU - Systems Ready for Spectre-Meltdown Windows Patch +action.escu.known_false_positives = None at this time +action.escu.search_type = support action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Malicious PowerShell"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Malicious PowerShell Process - Encoded Command -action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = The system $dest$ executed a PowerShell process that has an encoded command on the command-line -action.notable.param.rule_title = PowerShell process with an encoded command detected on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user, process_name -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = 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. -dispatch.earliest_time = -70m@m +action.escu.analytic_story = ["Spectre And Meltdown Vulnerabilities"] +description = 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. +dispatch.earliest_time = -1d@d dispatch.latest_time = -10m@m disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search process=*-EncodedCommand* OR process=*-enc* +search = | tstats `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 | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name("All_Changes")` [ESCU - Unusually Long Command Line - Rule] action.escu = 0 @@ -9118,7 +9256,7 @@ action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Ac action.escu.known_false_positives = Some legitimate applications start with long command-lines. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Unusual Processes", "Ransomware", "Suspicious Command-Line Executions", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Unusual Processes", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Unusually Long Command Line action.notable = 1 @@ -9127,7 +9265,7 @@ action.notable.param.rule_description = An unusually long command-line $cmdline$ action.notable.param.rule_title = Unusually Long Command-Line on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -9202,77 +9340,51 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?.*)@" -[ESCU - Get Web Session Information via session_id] +[ESCU - Count of Unique IPs Connecting to Ports] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-10-08 -action.escu.modification_date = 2018-10-08 +action.escu.creation_date = 2017-06-24 +action.escu.modification_date = 2017-09-13 action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.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. -action.escu.full_search_name = ESCU - Get Web Session Information via session_id +action.escu.eli5 = 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. +action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Count of Unique IPs Connecting to Ports action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Splunk Stream"] -action.escu.analytic_story = ["Web Fraud Detection"] -action.escu.fields_required = ["session_id"] -action.escu.earliest_time_offset = 3600 -action.escu.latest_time_offset = 3600 -description = This search helps an analyst investigate a notable event to find out more about a specific web session. The search looks for a specific web session ID in the HTTP web traffic and outputs the URL and user agents, grouped by source IP address and HTTP status code. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | search sourcetype=stream:http {session_id} | stats values(url) values(http_user_agent) by src_ip status - -[ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-03-12 -action.escu.modification_date = 2018-03-12 -action.escu.asset_at_risk = AWS Instance -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.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. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cryptomining"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Started With Previously Unseen Instance Type -action.notable = 1 -action.notable.param.nes_fields = instanceType -action.notable.param.rule_description = The EC2 instance type $instanceType$ was used for the first time to create $dest$. -action.notable.param.rule_title = New EC2 Instance Type $instanceType$ detected -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for EC2 instances being created with previously unseen instance types. -dispatch.earliest_time = -70m@m +action.escu.search_type = support +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch"] +description = 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. +dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | convert ctime(earliest) ctime(latest) | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType +search = | tstats `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 - Identify Systems Receiving Remote Desktop Traffic] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-04-24 +action.escu.modification_date = 2017-09-15 +action.escu.channel = ESCU +action.escu.eli5 = This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic. +action.escu.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. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Identify Systems Receiving Remote Desktop Traffic +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["Lateral Movement"] +description = This search counts the numbers of times the system has created remote desktop traffic +dispatch.earliest_time = -30d@d +dispatch.latest_time = -10m@m +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `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 - Get Risk Modifiers For Endpoint] action.escu = 0 @@ -9287,11 +9399,11 @@ action.escu.full_search_name = ESCU - Get Risk Modifiers For Endpoint action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Windows Privilege Escalation", "Hidden Cobra Malware", "Windows File Extension and Association Abuse", "Monitor for Updates", "Unusual Processes", "ColdRoot MacOS RAT", "Account Monitoring and Controls", "Netsh Abuse", "DHS Report TA18-074A", "Data Protection", "DNS Amplification Attacks", "SamSam Ransomware", "Dynamic DNS", "Apache Struts Vulnerability", "Monitor Backup Solution", "Windows Service Abuse", "Windows Log Manipulation", "Orangeworm Attack Group", "Disabling Security Tools", "JBoss Vulnerability", "Ransomware", "Spectre And Meltdown Vulnerabilities", "Suspicious WMI Use", "Suspicious DNS Traffic", "Emotet Malware (TA18-201A)", "DNS Hijacking", "Host Redirection", "Brand Monitoring", "Splunk Enterprise Vulnerability", "Credential Dumping", "Prohibited Traffic Allowed or Protocol Mismatch", "Asset Tracking", "Use of Cleartext Protocols", "Windows Persistence Techniques", "Command and Control", "Suspicious Emails", "Splunk Enterprise Vulnerability CVE-2018-11409", "Suspicious Windows Registry Activities", "Router & Infrastructure Security", "Malicious PowerShell", "SQL Injection", "Lateral Movement", "Collection and Staging", "Monitor for Unauthorized Software", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Windows Service Abuse", "Host Redirection", "DHS Report TA18-074A", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "ColdRoot MacOS RAT", "Monitor Backup Solution", "Orangeworm Attack Group", "Unusual Processes", "DNS Amplification Attacks", "Suspicious WMI Use", "Asset Tracking", "Ransomware", "Command and Control", "Windows Privilege Escalation", "Windows Log Manipulation", "Windows File Extension and Association Abuse", "Prohibited Traffic Allowed or Protocol Mismatch", "Brand Monitoring", "SamSam Ransomware", "Data Protection", "Router & Infrastructure Security", "Malicious PowerShell", "Disabling Security Tools", "SQL Injection", "Emotet Malware (TA18-201A)", "Hidden Cobra Malware", "Suspicious Emails", "JBoss Vulnerability", "Suspicious DNS Traffic", "Spectre And Meltdown Vulnerabilities", "Credential Dumping", "Collection and Staging", "Netsh Abuse", "Account Monitoring and Controls", "Monitor for Updates", "Use of Cleartext Protocols", "Lateral Movement", "Dynamic DNS", "DNS Hijacking", "Splunk Enterprise Vulnerability", "Apache Struts Vulnerability", "Suspicious MSHTA Activity", "Splunk Enterprise Vulnerability CVE-2018-11409", "Monitor for Unauthorized Software"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 604800 action.escu.latest_time_offset = 0 -description = For the last 7 days, the search will query the Risk data model in Splunk Enterprise Security and calculate the count, sum of the risk_scores, names of the correlation searches that contributed to create a risk score for a specific endpoint(machine_name) +description = For the last 7 days, the search will query the Risk data model in Splunk Enterprise Security and calculate the count, sum of the risk\_scores, names of the correlation searches that contributed to create a risk score for a specific endpoint(machine\_name) disabled=true realtime_schedule = 0 schedule_window = auto