From 804e2e8248ea3ea05099ca888f7cb8abc6a0610a Mon Sep 17 00:00:00 2001 From: Patrick Bareiss Date: Mon, 11 May 2020 17:40:06 +0200 Subject: [PATCH] bug in generate --- bin/generate.py | 15 + bin/jinja2_templates/use_case_library.j2 | 2 +- package/default/analytic_stories.conf | 118 +++--- package/default/macros.conf | 2 +- package/default/savedsearches.conf | 2 +- package/default/transforms.conf | 2 +- package/default/use_case_library.conf | 506 +++++++++++------------ 7 files changed, 331 insertions(+), 316 deletions(-) diff --git a/bin/generate.py b/bin/generate.py index ca27cfde35..66868a86cc 100644 --- a/bin/generate.py +++ b/bin/generate.py @@ -156,6 +156,21 @@ def generate_use_case_library_conf(stories, detections, response_tasks, baseline else: story['searches'] = story['detections'] + for detection in detections: + + keys = ['mitre_attack', 'kill_chain_phases', 'cis20', 'nist'] + mappings = {} + for key in keys: + if key == 'mitre_attack': + if 'mitre_attack_id' in detection['tags']: + mappings[key] = detection['tags']['mitre_attack_id'] + else: + if key in detection['tags']: + mappings[key] = detection['tags'][key] + + + detection['mappings'] = mappings + utc_time = datetime.datetime.utcnow().replace(microsecond=0).isoformat() j2_env = Environment(loader=FileSystemLoader('bin/jinja2_templates'), diff --git a/bin/jinja2_templates/use_case_library.j2 b/bin/jinja2_templates/use_case_library.j2 index f9af4c57ff..1aa262ca2b 100644 --- a/bin/jinja2_templates/use_case_library.j2 +++ b/bin/jinja2_templates/use_case_library.j2 @@ -37,7 +37,7 @@ how_to_implement = {{ detection.how_to_implement }} {% else %} how_to_implement = none {% endif %} -annotations = {{ detection.tags | tojson }} +annotations = {{ detection.mappings | tojson }} known_false_positives = {{ detection.known_false_positives }} providing_technologies = none diff --git a/package/default/analytic_stories.conf b/package/default/analytic_stories.conf index 6441c13abb..539460661d 100644 --- a/package/default/analytic_stories.conf +++ b/package/default/analytic_stories.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-11T12:39:19 UTC +# On Date: 2020-05-11T15:39:38 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -16,7 +16,7 @@ version = 1 reference = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/"] detection_searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule"] mappings = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By Source User", "ESCU - AWS Investigate User Activities By AccessKeyId"] +investigative_searches = ["ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History"] support_searches = [] data_models = [] providing_technologies = none @@ -34,7 +34,7 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule"] mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] +investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History", "ESCU - Get Notable Info"] support_searches = [] data_models = [] providing_technologies = none @@ -53,7 +53,7 @@ version = 2 reference = ["https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html", "https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/"] detection_searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule"] mappings = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC"]} -investigative_searches = ["ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Notable History", "ESCU - Get Process Responsible For The DNS Traffic"] +investigative_searches = ["ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID"] support_searches = [] data_models = [] providing_technologies = none @@ -69,7 +69,7 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule"] mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -investigative_searches = ["ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From City"] +investigative_searches = ["ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Region"] support_searches = [] data_models = [] providing_technologies = none @@ -105,7 +105,7 @@ version = 1 reference = ["https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf"] detection_searches = ["ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule"] mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078", "T1136"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint"] support_searches = [] data_models = ["Change"] providing_technologies = none @@ -121,7 +121,7 @@ version = 1 reference = ["https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf"] detection_searches = ["ESCU - Suspicious Java Classes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Web Servers Executing Suspicious Processes - Rule"] mappings = {"cis20": ["CIS 12", "CIS 18", "CIS 3", "CIS 4"], "kill_chain_phases": ["Actions on Objectives", "Delivery", "Exploitation"], "mitre_attack": ["T1082"], "nist": ["DE.AE", "DE.CM", "ID.RA", "PR.IP", "PR.MA", "PR.PT", "RS.MI"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Investigate Web POSTs From src", "ESCU - Get Notable Info", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web POSTs From src", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Suspicious Strings in HTTP Header"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -150,7 +150,7 @@ version = 1 reference = ["https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/"] detection_searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule"] mappings = {"cis20": ["CIS 1"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS"]} -investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address"] +investigative_searches = ["ESCU - Get Notable History", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address", "ESCU - Get Notable Info"] support_searches = [] data_models = ["Network_Sessions"] providing_technologies = none @@ -166,7 +166,7 @@ version = 1 reference = ["https://www.zerofox.com/blog/what-is-digital-risk-monitoring/", "https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/", "https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/"] detection_searches = ["ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule"] mappings = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives", "Delivery"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Email Info", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Email Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] support_searches = [] data_models = ["Email", "Network_Resolution", "Web"] providing_technologies = none @@ -184,7 +184,7 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule"] mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Get Notable Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] +investigative_searches = ["ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info"] support_searches = [] data_models = ["Cloud_Infrastructure"] providing_technologies = none @@ -203,7 +203,7 @@ version = 1 reference = ["https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/", "https://objective-see.com/blog/blog_0x2A.html", "https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/"] detection_searches = ["ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Processes Tapping Keyboard Events - Rule"] mappings = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From src ip", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Authentication Logs For Endpoint"] support_searches = [] data_models = [] providing_technologies = none @@ -221,7 +221,7 @@ version = 1 reference = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"] detection_searches = ["ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Suspicious writes to windows Recycle Bin - Rule"] mappings = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043", "T1074", "T1114"], "nist": ["DE.AE", "DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none @@ -239,7 +239,7 @@ version = 1 reference = ["https://attack.mitre.org/wiki/Command_and_Control", "https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware"] detection_searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule"] mappings = {"cis20": ["CIS 1", "CIS 11", "CIS 12", "CIS 13", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["T1043", "T1048", "T1095"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Notable History", "ESCU - Get Process Responsible For The DNS Traffic"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID"] support_searches = [] data_models = ["Network_Resolution", "Network_Traffic"] providing_technologies = none @@ -289,7 +289,7 @@ version = 3 reference = ["https://attack.mitre.org/wiki/Technique/T1003", "https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html"] detection_searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule"] mappings = {"cis20": ["CIS 16", "CIS 3", "CIS 5", "CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1003", "T1064", "T1086"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP"]} -investigative_searches = ["ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Previous Unseen User"] +investigative_searches = ["ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Pass the Hash Attempts"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -307,7 +307,7 @@ version = 2 reference = ["https://www.us-cert.gov/ncas/alerts/TA18-074A"] detection_searches = ["ESCU - Create local admin accounts using net exe - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Suspicious Reg exe Process - Rule"] mappings = {"cis20": ["CIS 12", "CIS 16", "CIS 2", "CIS 3", "CIS 5", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["T1031", "T1043", "T1050", "T1053", "T1059", "T1064", "T1078", "T1086", "T1089", "T1103", "T1112", "T1131"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.AT", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Process File Activity", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Registry Activity", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Process Registry Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process File Activity"] support_searches = [] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none @@ -326,7 +326,7 @@ version = 1 reference = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.imperva.com/learn/application-security/dns-amplification/"] detection_searches = ["ESCU - Large Volume of DNS ANY Queries - Rule"] mappings = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User"] +investigative_searches = ["ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info"] support_searches = [] data_models = ["Network_Resolution"] providing_technologies = none @@ -367,7 +367,7 @@ version = 1 reference = ["https://www.cisecurity.org/controls/data-protection/", "https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/"] detection_searches = ["ESCU - Detect USB device insertion - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule"] mappings = {"cis20": ["CIS 12", "CIS 13", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE", "DE.CM", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Responsible For The DNS Traffic"] +investigative_searches = ["ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] support_searches = [] data_models = ["Change_Analysis", "Network_Resolution"] providing_technologies = none @@ -383,7 +383,7 @@ version = 2 reference = ["https://attack.mitre.org/wiki/Technique/T1089", "https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf"] detection_searches = ["ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Unload Sysmon Filter Driver - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1031", "T1050", "T1059", "T1089", "T1112"], "nist": ["DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -399,7 +399,7 @@ version = 2 reference = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] detection_searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule"] mappings = {"cis20": ["CIS 13", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1041"], "nist": ["DE.CM", "DE.DP", "PR.IP"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Process Responsible For The DNS Traffic"] +investigative_searches = ["ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From src ip", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint"] support_searches = [] data_models = ["Network_Resolution", "Web"] providing_technologies = none @@ -415,7 +415,7 @@ version = 1 reference = ["https://www.us-cert.gov/ncas/alerts/TA18-201A", "https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf", "https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html"] detection_searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule"] mappings = {"cis20": ["CIS 12", "CIS 2", "CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery", "Exploitation", "Installation"], "mitre_attack": ["T1043", "T1059", "T1072", "T1087", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Process Information For Port Activity", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Email", "Endpoint", "Network_Traffic"] providing_technologies = none @@ -433,7 +433,7 @@ version = 2 reference = ["https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf"] detection_searches = ["ESCU - Create or delete windows shares using net exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious File Write - Rule"] mappings = {"cis20": ["CIS 12", "CIS 16", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043", "T1059", "T1064", "T1076"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Responsible For The DNS Traffic"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint", "Network_Resolution", "Network_Traffic"] providing_technologies = none @@ -452,7 +452,7 @@ version = 1 reference = ["https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/"] detection_searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule"] mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13", "CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.AC", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint"] support_searches = [] data_models = ["Network_Resolution"] providing_technologies = none @@ -468,7 +468,7 @@ version = 1 reference = ["http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html"] detection_searches = ["ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule"] mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery", "Reconnaissance"], "mitre_attack": ["T1082"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Investigate Web Activity From Host"] support_searches = [] data_models = ["Web"] providing_technologies = none @@ -498,7 +498,7 @@ version = 1 reference = ["https://github.com/splunk/cloud-datamodel-security-research"] detection_searches = ["ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule"] mappings = {"kill_chain_phases": ["Reconnaissance"]} -investigative_searches = ["ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - GCP Kubernetes activity by src ip", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - GCP Kubernetes activity by src ip", "ESCU - Get Notable Info"] support_searches = [] data_models = [] providing_technologies = none @@ -514,7 +514,7 @@ version = 2 reference = ["https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] detection_searches = ["ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Schtasks scheduling job on remote system - Rule"] mappings = {"cis20": ["CIS 16", "CIS 3", "CIS 9"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053", "T1075", "T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} -investigative_searches = ["ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none @@ -534,7 +534,7 @@ version = 4 reference = ["https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/", "https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] detection_searches = ["ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule"] mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1064", "T1086"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -559,7 +559,7 @@ version = 1 reference = ["https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/"] detection_searches = ["ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Unsuccessful Netbackup backups - Rule"] mappings = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get Risk Modifiers For Endpoint", "ESCU - All backup logs for host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User"] +investigative_searches = ["ESCU - All backup logs for host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User"] support_searches = [] data_models = [] providing_technologies = none @@ -575,7 +575,7 @@ version = 1 reference = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] detection_searches = ["ESCU - Prohibited Software On Endpoint - Rule"] mappings = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -592,7 +592,7 @@ version = 1 reference = ["https://learn.cisecurity.org/20-controls-download"] detection_searches = ["ESCU - No Windows Updates in a time frame - Rule"] mappings = {"cis20": ["CIS 18"], "nist": ["PR.MA"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info"] support_searches = [] data_models = ["Updates"] providing_technologies = none @@ -610,7 +610,7 @@ version = 1 reference = ["https://technet.microsoft.com/library/bb490939.aspx", "https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html", "http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html"] detection_searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule"] mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059", "T1089"], "nist": ["DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -627,7 +627,7 @@ version = 2 reference = ["https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia", "https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/"] detection_searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["T1031", "T1050", "T1059", "T1064", "T1089"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -670,7 +670,7 @@ version = 1 reference = ["https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/", "http://blog.amossys.fr/badflick-is-not-so-bad.html"] detection_searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule"] mappings = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1059", "T1064", "T1086", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -713,7 +713,7 @@ version = 1 reference = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"] detection_searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule"] mappings = {"cis20": ["CIS 12", "CIS 13", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} -investigative_searches = ["ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] support_searches = [] data_models = ["Network_Resolution", "Network_Traffic"] providing_technologies = none @@ -729,7 +729,7 @@ version = 1 reference = ["https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/", "https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html"] detection_searches = ["ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - TOR Traffic - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Windows Event Log Cleared - Rule"] mappings = {"cis20": ["CIS 10", "CIS 12", "CIS 3", "CIS 5", "CIS 6", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery"], "mitre_attack": ["T1036", "T1043", "T1047", "T1048", "T1053", "T1070", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Process Information For Port Activity", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint", "Network_Traffic"] providing_technologies = none @@ -745,7 +745,7 @@ version = 1 reference = ["https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html", "https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html"] detection_searches = ["ESCU - Detect New Login Attempts to Routers - Rule"] mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.IP"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint"] support_searches = [] data_models = ["Authentication"] providing_technologies = none @@ -762,7 +762,7 @@ version = 1 reference = ["https://capec.mitre.org/data/definitions/66.html", "https://www.incapsula.com/web-application-security/sql-injection.html"] detection_searches = ["ESCU - SQL Injection with Long URLs - Rule"] mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info"] support_searches = [] data_models = ["Web"] providing_technologies = none @@ -779,7 +779,7 @@ version = 1 reference = ["https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/", "https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/", "https://thehackernews.com/2018/07/samsam-ransomware-attacks.html"] detection_searches = ["ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Spike in File Writes - Rule"] mappings = {"cis20": ["CIS 10", "CIS 12", "CIS 16", "CIS 18", "CIS 2", "CIS 3", "CIS 4", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Delivery", "Installation", "Reconnaissance"], "mitre_attack": ["T1059", "T1076", "T1082"], "nist": ["DE.AE", "DE.CM", "ID.AM", "ID.RA", "PR.AC", "PR.DS", "PR.IP", "PR.MA", "PR.PT"]} -investigative_searches = ["ESCU - Get Process Information For Port Activity", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Process Information For Port Activity", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint", "Network_Traffic", "Web"] providing_technologies = none @@ -800,7 +800,7 @@ version = 1 reference = ["https://meltdownattack.com/"] detection_searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule"] mappings = {"cis20": ["CIS 4"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint"] support_searches = [] data_models = ["Vulnerabilities"] providing_technologies = none @@ -816,7 +816,7 @@ version = 1 reference = ["http://www.splunk.com/view/SP-CAAAPQ6#announce", "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859"] detection_searches = ["ESCU - Open Redirect in Splunk Web - Rule"] mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] +investigative_searches = ["ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info"] support_searches = [] data_models = [] providing_technologies = none @@ -841,7 +841,7 @@ version = 1 reference = ["https://nvd.nist.gov/vuln/detail/CVE-2018-11409", "https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings", "https://www.exploit-db.com/exploits/44865/"] detection_searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule"] mappings = {"cis20": ["CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Get Notable Info", "ESCU - Investigate Network Traffic From src ip", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Investigate Web Activity From src ip", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info"] support_searches = [] data_models = [] providing_technologies = none @@ -860,7 +860,7 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule"] mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.AE", "DE.DP", "ID.AM"]} -investigative_searches = ["ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] +investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History", "ESCU - Get Notable Info"] support_searches = [] data_models = [] providing_technologies = none @@ -892,7 +892,7 @@ version = 2 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/"] detection_searches = ["ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule"] mappings = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM", "DE.DP", "PR.AC", "PR.DS"]} -investigative_searches = ["ESCU - Investigate AWS activities via region name", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] +investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get All AWS Activity From IP Address"] support_searches = [] data_models = [] providing_technologies = none @@ -910,7 +910,7 @@ version = 1 reference = ["https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/"] detection_searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule"] mappings = {"cis20": ["CIS 11"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.AC"]} -investigative_searches = ["ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Notable History", "ESCU - Get Process Responsible For The DNS Traffic"] +investigative_searches = ["ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID"] support_searches = [] data_models = [] providing_technologies = none @@ -929,7 +929,7 @@ version = 2 reference = ["https://attack.mitre.org/wiki/Technique/T1059", "https://www.microsoft.com/en-us/wdsi/threats/macro-malware", "https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf"] detection_searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule"] mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Exploitation"], "mitre_attack": ["T1036", "T1059", "T1064"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -945,7 +945,7 @@ version = 1 reference = ["http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/", "http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680", "https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454"] detection_searches = ["ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Excessive DNS Failures - Rule"] mappings = {"cis20": ["CIS 1", "CIS 12", "CIS 13", "CIS 3", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "DE.CM", "ID.AM", "PR.DS", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Responsible For The DNS Traffic"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] support_searches = [] data_models = ["Network_Resolution"] providing_technologies = none @@ -961,7 +961,7 @@ version = 1 reference = ["https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/"] detection_searches = ["ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule"] mappings = {"cis20": ["CIS 12", "CIS 3", "CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["DE.AE", "PR.IP"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Email Info", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Email Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] support_searches = [] data_models = ["Email", "UEBA"] providing_technologies = none @@ -981,7 +981,7 @@ version = 1 reference = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5", "https://attack.mitre.org/wiki/Technique/T1170"] detection_searches = ["ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect mshta exe running scripts in command-line arguments - Rule", "ESCU - Registry Keys Used For Persistence - Rule"] mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation"], "mitre_attack": ["T1059", "T1103", "T1131"], "nist": ["DE.AE", "DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -999,7 +999,7 @@ version = 1 reference = ["https://attack.mitre.org/wiki/Technique/T1078", "https://owasp.org/www-community/attacks/Credential_stuffing", "https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work"] detection_searches = ["ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule"] mappings = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} -investigative_searches = ["ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate User Activities In Okta"] +investigative_searches = ["ESCU - Investigate User Activities In Okta", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate Okta Activity by IP Address"] support_searches = [] data_models = [] providing_technologies = none @@ -1017,7 +1017,7 @@ version = 2 reference = ["https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf", "https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html"] detection_searches = ["ESCU - Process Execution via WMI - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Temporary Event Subscription - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1035,7 +1035,7 @@ version = 1 reference = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"] detection_searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Changes to File Associations - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015", "T1042", "T1103", "T1112", "T1131", "T1138"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1053,7 +1053,7 @@ version = 1 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] detection_searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule"] mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -investigative_searches = ["ESCU - Get EC2 Instance Details by instanceId", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History"] +investigative_searches = ["ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History"] support_searches = [] data_models = [] providing_technologies = none @@ -1070,7 +1070,7 @@ version = 2 reference = ["https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html", "https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf", "https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262"] detection_searches = ["ESCU - Detect Rare Executables - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule"] mappings = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Command and Control", "Installation"], "mitre_attack": ["T1015", "T1036", "T1085"], "nist": ["DE.CM", "ID.AM", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1088,7 +1088,7 @@ version = 1 reference = ["https://www.monkey.org/~dugsong/dsniff/"] detection_searches = ["ESCU - Protocols passing authentication in cleartext - Rule"] mappings = {"cis20": ["CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS"]} -investigative_searches = ["ESCU - Get Process Information For Port Activity", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info"] support_searches = [] data_models = ["Network_Traffic"] providing_technologies = none @@ -1125,7 +1125,7 @@ version = 1 reference = ["https://attack.mitre.org/wiki/Defense_Evasion"] detection_searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Suspicious Reg exe Process - Rule"] mappings = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1089", "T1112"], "nist": ["DE.CM", "PR.PT"]} -investigative_searches = ["ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1141,7 +1141,7 @@ version = 1 reference = ["https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/", "https://attack.mitre.org/wiki/Technique/T1042"] detection_searches = ["ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule"] mappings = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1161,7 +1161,7 @@ version = 2 reference = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/", "https://zeltser.com/security-incident-log-review-checklist/", "http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html"] detection_searches = ["ESCU - Deleting Shadow Copies - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule"] mappings = {"cis20": ["CIS 10", "CIS 3", "CIS 5", "CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.AE", "DE.CM", "DE.DP", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1178,7 +1178,7 @@ version = 2 reference = ["http://www.fuzzysecurity.com/tutorials/19.html", "https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html", "http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/", "https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html", "https://www.youtube.com/watch?v=dq2Hv7J9fvk"] detection_searches = ["ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1031", "T1050", "T1053", "T1089", "T1103", "T1131", "T1138"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1194,7 +1194,7 @@ version = 2 reference = ["https://attack.mitre.org/tactics/TA0004/"] detection_searches = ["ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule"] mappings = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Actions on Objectives", "Exploitation"], "mitre_attack": ["T1015", "T1068"], "nist": ["DE.CM", "ID.AM", "PR.DS", "PR.PT"]} -investigative_searches = ["ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none @@ -1210,7 +1210,7 @@ version = 3 reference = ["https://attack.mitre.org/wiki/Technique/T1050", "https://attack.mitre.org/wiki/Technique/T1031"] detection_searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule"] mappings = {"cis20": ["CIS 3", "CIS 5", "CIS 8", "CIS 9"], "kill_chain_phases": ["Actions on Objectives", "Installation"], "mitre_attack": ["T1031", "T1050", "T1089"], "nist": ["DE.AE", "DE.CM", "PR.AC", "PR.AT", "PR.IP", "PR.PT"]} -investigative_searches = ["ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +investigative_searches = ["ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] support_searches = [] data_models = ["Endpoint"] providing_technologies = none diff --git a/package/default/macros.conf b/package/default/macros.conf index 3471bfab77..c93e15c0a1 100644 --- a/package/default/macros.conf +++ b/package/default/macros.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-11T12:39:19 UTC +# On Date: 2020-05-11T15:39:38 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# diff --git a/package/default/savedsearches.conf b/package/default/savedsearches.conf index 805673ae90..c53098f2e9 100644 --- a/package/default/savedsearches.conf +++ b/package/default/savedsearches.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-11T12:39:19 UTC +# On Date: 2020-05-11T15:39:38 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# diff --git a/package/default/transforms.conf b/package/default/transforms.conf index ab1d2383dd..95e5cd8368 100644 --- a/package/default/transforms.conf +++ b/package/default/transforms.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-11T12:39:19 UTC +# On Date: 2020-05-11T15:39:38 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# diff --git a/package/default/use_case_library.conf b/package/default/use_case_library.conf index e5e940b564..f74724152d 100644 --- a/package/default/use_case_library.conf +++ b/package/default/use_case_library.conf @@ -1,6 +1,6 @@ ############# # Automatically generated by generator.py in splunk/security-content -# On Date: 2020-05-11T12:39:19 UTC +# On Date: 2020-05-11T15:39:38 UTC # Author: Splunk Security Research # Contact: research@splunk.com ############# @@ -14,7 +14,7 @@ version = 1 references = ["https://aws.amazon.com/blogs/security/aws-cloudtrail-now-tracks-cross-account-activity-to-its-origin/"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - Get Notable History", "ESCU - AWS Investigate User Activities By Source User", "ESCU - AWS Investigate User Activities By AccessKeyId"] +searches = ["ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule", "ESCU - AWS Investigate User Activities By AccessKeyId", "ESCU - AWS Investigate User Activities By Source User", "ESCU - Get Notable History"] description = Track when a user assumes an IAM role in another AWS account to obtain cross-account access to services and resources in that account. Accessing new roles could be an indication of malicious activity. narrative = Amazon Web Services (AWS) admins manage access to AWS resources and services across the enterprise using AWS's Identity and Access Management (IAM) functionality. IAM provides the ability to create and manage AWS users, groups, and roles-each with their own unique set of privileges and defined access to specific resources (such as EC2 instances, the AWS Management Console, API, or the command-line interface). Unlike conventional (human) users, IAM roles are assumable by anyone in the organization. They provide users with dynamically created temporary security credentials that expire within a set time period.\ Herein lies the rub. In between the time between when the temporary credentials are issued and when they expire is a period of opportunity, where a user could leverage the temporary credentials to wreak havoc-spin up or remove instances, create new users, elevate privileges, and other malicious activities-throughout the environment.\ @@ -27,7 +27,7 @@ version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] +searches = ["ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - EC2 Instance Started With Previously Unseen AMI - Rule", "ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Monitor your AWS EC2 instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or EC2 instances started by previously unseen users are just a few examples of potentially malicious behavior. narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS). It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ @@ -41,7 +41,7 @@ version = 2 references = ["https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html", "https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Notable History", "ESCU - Get Process Responsible For The DNS Traffic"] +searches = ["ESCU - AWS Network Access Control List Created with All Open Ports - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - AWS Network Access Control List Deleted - Rule", "ESCU - Detect Spike in Network ACL Activity - Rule", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID"] description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and operational/risk auditing of your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Management Console, AWS Command Line Interface, and AWS SDKs and APIs to ensure that your servers are not vulnerable to attacks. This analytic story contains detection searches that leverage CloudTrail logs from AWS to check for bad configurations and malicious activity in your AWS network access controls. @@ -52,7 +52,7 @@ version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From Region", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From City"] +searches = ["ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen Country - Rule", "ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule", "ESCU - Get All AWS Activity From City", "ESCU - Get All AWS Activity From Country", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get All AWS Activity From Region"] description = Monitor your AWS provisioning activities for behaviors originating from unfamiliar or unusual locations. These behaviors may indicate that malicious activities are occurring somewhere within your network. narrative = Because most enterprise AWS activities originate from familiar geographic locations, monitoring for activity from unknown or unusual regions is an important security measure. This indicator can be especially useful in environments where it is impossible to whitelist specific IPs (because they vary).\ This Analytic Story was designed to provide you with flexibility in the precision you employ in specifying legitimate geographic regions. It can be as specific as an IP address or a city, or as broad as a region (think state) or an entire country. By determining how precise you want your geographical locations to be and monitoring for new locations that haven't previously accessed your environment, you can detect adversaries as they begin to probe your environment. Since there are legitimate reasons for activities from unfamiliar locations, this is not a standalone indicator. Nevertheless, location can be a relevant piece of information that you may wish to investigate further. @@ -64,7 +64,7 @@ version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://redlock.io/blog/cryptojacking-tesla"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect new API calls from user roles - Rule", "ESCU - Detect API activity from users without MFA - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Investigate AWS User Activities by user field", "ESCU - Get Notable History", "ESCU - Get Notable Info"] +searches = ["ESCU - Detect Spike in AWS API Activity - Rule", "ESCU - Detect new API calls from user roles - Rule", "ESCU - Detect AWS API Activities From Unapproved Accounts - Rule", "ESCU - Detect Spike in Security Group Activity - Rule", "ESCU - Detect API activity from users without MFA - Rule", "ESCU - Investigate AWS User Activities by user field", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Detect and investigate dormant user accounts for your AWS environment that have become active again. Because inactive and ad-hoc accounts are common attack targets, it's critical to enable governance within your environment. narrative = It seems obvious that it is critical to monitor and control the users who have access to your cloud infrastructure. Nevertheless, it's all too common for enterprises to lose track of ad-hoc accounts, leaving their servers vulnerable to attack. In fact, this was the very oversight that led to Tesla's cryptojacking attack in February, 2018.\ In addition to compromising the security of your data, when bad actors leverage your compute resources, it can incur monumental costs, since you will be billed for any new EC2 instances and increased bandwidth usage. \ @@ -78,7 +78,7 @@ version = 1 references = ["https://www.sans.org/media/critical-security-controls/critical-controls-poster-2016.pdf"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Short Lived Windows Accounts - Rule", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Short Lived Windows Accounts - Rule", "ESCU - Identify New User Accounts - Rule", "ESCU - Detect Excessive Account Lockouts From Endpoint - Rule", "ESCU - Detect Excessive User Account Lockouts - Rule", "ESCU - Get Logon Rights Modifications For User", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Logon Rights Modifications For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint"] description = A common attack technique is to leverage user accounts to gain unauthorized access to the target's network. This Analytic Story minimizes opportunities for attack by helping you actively manage creation/use/dormancy/deletion--the lifecycle of system and application accounts. narrative = Monitoring user accounts within your enterprise is a critical analytic function that helps ensure that credential and access policies/procedures are properly implemented and are being enforced. Proactive ad-hoc hunting, as well as routine monitoring, can ensure user or system accounts are not being abused by unauthorized individuals or processes. In the event of a network event or breach, user-authentication logs are a key resource in determining if or how an account might have been compromised or co-opted, leading to suspicious or malicious activity. @@ -89,7 +89,7 @@ version = 1 references = ["https://github.com/SpiderLabs/owasp-modsecurity-crs/blob/v3.2/dev/rules/REQUEST-944-APPLICATION-ATTACK-JAVA.conf"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Suspicious Java Classes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web POSTs From src", "ESCU - Get Notable Info", "ESCU - Investigate Suspicious Strings in HTTP Header", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Web Servers Executing Suspicious Processes - Rule", "ESCU - Unusually Long Content-Type Length - Rule", "ESCU - Suspicious Java Classes - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Web POSTs From src", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Suspicious Strings in HTTP Header"] description = Detect and investigate activities--such as unusually long `Content-Type` length, suspicious java classes and web servers executing suspicious processes--consistent with attempts to exploit Apache Struts vulnerabilities. narrative = In March of 2017, a remote code-execution vulnerability in the Jakarta Multipart parser in Apache Struts, a widely used open-source framework for creating Java web applications, was disclosed and assigned to CVE-2017-5638. About two months later, hackers exploited the flaw to carry out the world's 5th largest data breach. The target, credit giant Equifax, told investigators that it had become aware of the vulnerability two months before the attack. \ The exploit involved manipulating the `Content-Type HTTP` header to execute commands embedded in the header.\ @@ -113,7 +113,7 @@ version = 1 references = ["https://www.cisecurity.org/controls/inventory-of-authorized-and-unauthorized-devices/"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address"] +searches = ["ESCU - Detect Unauthorized Assets by MAC address - Rule", "ESCU - Get Notable History", "ESCU - Get First Occurrence and Last Occurrence of a MAC Address", "ESCU - Get Notable Info"] description = Keep a careful inventory of every asset on your network to make it easier to detect rogue devices. Unauthorized/unmanaged devices could be an indication of malicious behavior that should be investigated further. narrative = This Analytic Story is designed to help you develop a better understanding of what authorized and unauthorized devices are part of your enterprise. This story can help you better categorize and classify assets, providing critical business context and awareness of their assets during an incident. Information derived from this Analytic Story can be used to better inform and support other analytic stories. For successful detection, you will need to leverage the Assets and Identity Framework from Enterprise Security to populate your known assets. @@ -124,7 +124,7 @@ version = 1 references = ["https://www.zerofox.com/blog/what-is-digital-risk-monitoring/", "https://securingtomorrow.mcafee.com/consumer/family-safety/what-is-typosquatting/", "https://blog.malwarebytes.com/cybercrime/2016/06/explained-typosquatting/"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For User", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Email Info", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Monitor DNS For Brand Abuse - Rule", "ESCU - Monitor Web Traffic For Brand Abuse - Rule", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Email Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] description = Detect and investigate activity that may indicate that an adversary is using faux domains to mislead users into interacting with malicious infrastructure. Monitor DNS, email, and web traffic for permutations of your brand name. narrative = While you can educate your users and customers about the risks and threats posed by typosquatting, phishing, and corporate espionage, human error is a persistent fact of life. Of course, your adversaries are all too aware of this reality and will happily leverage it for nefarious purposes whenever possible3phishing with lookalike addresses, embedding faux command-and-control domains in malware, and hosting malicious content on domains that closely mimic your corporate servers. This is where brand monitoring comes in.\ You can use our adaptation of `DNSTwist`, together with the support searches in this Analytic Story, to generate permutations of specified brands and external domains. Splunk can monitor email, DNS requests, and web traffic for these permutations and provide you with early warnings and situational awareness--powerful elements of an effective defense.\ @@ -137,7 +137,7 @@ version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Investigate AWS activities via region name", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Get Notable Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] +searches = ["ESCU - Cloud Compute Instance Created With Previously Unseen Image - Rule", "ESCU - Cloud Compute Instance Created By Previously Unseen User - Rule", "ESCU - Cloud Compute Instance Created With Previously Unseen Instance Type - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - Cloud Compute Instance Started In Previously Unused Region - Rule", "ESCU - Investigate User Activities In Single Cloud Region", "ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details", "ESCU - Investigate User Activities In All Cloud Regions", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Investigate Cloud Compute Instance Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Monitor your cloud compute instances for activities related to cryptojacking/cryptomining. New instances that originate from previously unseen regions, users who launch abnormally high numbers of instances, or compute instances started by previously unseen users are just a few examples of potentially malicious behavior. narrative = Cryptomining is an intentionally difficult, resource-intensive business. Its complexity was designed into the process to ensure that the number of blocks mined each day would remain steady. So, it's par for the course that ambitious, but unscrupulous, miners make amassing the computing power of large enterprises--a practice known as cryptojacking--a top priority. \ Cryptojacking has attracted an increasing amount of media attention since its explosion in popularity in the fall of 2017. The attacks have moved from in-browser exploits and mobile phones to enterprise cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It's difficult to determine exactly how widespread the practice has become, since bad actors continually evolve their ability to escape detection, including employing unlisted endpoints, moderating their CPU usage, and hiding the mining pool's IP address behind a free CDN. \ @@ -151,7 +151,7 @@ version = 1 references = ["https://www.intego.com/mac-security-blog/osxcoldroot-and-the-rat-invasion/", "https://objective-see.com/blog/blog_0x2A.html", "https://www.bleepingcomputer.com/news/security/coldroot-rat-still-undetectable-despite-being-uploaded-on-github-two-years-ago/"] maintainers = "Jose Hernandez, Splunk" spec_version = 3 -searches = ["ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip"] +searches = ["ESCU - Processes Tapping Keyboard Events - Rule", "ESCU - Osquery pack - ColdRoot detection - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From src ip", "ESCU - Get User Information from Identity Table", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Authentication Logs For Endpoint"] description = Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. narrative = Conventional wisdom holds that Apple's MacOS operating system is significantly less vulnerable to attack than Windows machines. While that point is debatable, it is true that attacks against MacOS systems are much less common. However, this fact does not mean that Macs are impervious to breaches. To the contrary, research has shown that that Mac malware is increasing at an alarming rate. According to AV-test, in 2018, there were 86,865 new MacOS malware variants, up from 27,338 the year before—a 31% increase. In contrast, the independent research firm found that new Windows malware had increased from 65.17M to 76.86M during that same period, less than half the rate of growth. The bottom line is that while the numbers look a lot smaller than Windows, it's definitely time to take Mac security more seriously.\ This Analytic Story addresses the ColdRoot remote access trojan (RAT), which was uploaded to Github in 2016, but was still escaping detection by the first quarter of 2018, when a new, more feature-rich variant was discovered masquerading as an Apple audio driver. Among other capabilities, the Pascal-based ColdRoot can heist passwords from users' keychains and remotely control infected machines without detection. In the initial report of his findings, Patrick Wardle, Chief Research Officer for Digita Security, explained that the new ColdRoot RAT could start and kill processes on the breached system, spawn new remote-desktop sessions, take screen captures and assemble them into a live stream of the victim's desktop, and more.\ @@ -164,7 +164,7 @@ version = 1 references = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Suspicious writes to System Volume Information - Rule", "ESCU - Email files written outside of the Outlook directory - Rule", "ESCU - Suspicious writes to windows Recycle Bin - Rule", "ESCU - Hosts receiving high volume of network traffic from email server - Rule", "ESCU - Email servers sending high volume traffic to hosts - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] description = Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. narrative = A common adversary goal is to identify and exfiltrate data of value from a target organization. This data may include email conversations and addresses, confidential company information, links to network design/infrastructure, important dates, and so on.\ Attacks are composed of three activities: identification, collection, and staging data for exfiltration. Identification typically involves scanning systems and observing user activity. Collection can involve the transfer of large amounts of data from various repositories. Staging/preparation includes moving data to a central location and compressing (and optionally encoding and/or encrypting) it. All of these activities provide opportunities for defenders to identify their presence. \ @@ -177,7 +177,7 @@ version = 1 references = ["https://attack.mitre.org/wiki/Command_and_Control", "https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Notable History", "ESCU - Get Process Responsible For The DNS Traffic"] +searches = ["ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - Detect Large Outbound ICMP Packets - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Get Parent Process Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID"] description = Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. narrative = Threat actors typically architect and implement an infrastructure to use in various ways during the course of their attack campaigns. In some cases, they leverage this infrastructure for scanning and performing reconnaissance activities. In others, they may use this infrastructure to launch actual attacks. One of the most important functions of this infrastructure is to establish servers that will communicate with implants on compromised endpoints. These servers establish a command and control channel that is used to proxy data between the compromised endpoint and the attacker. These channels relay commands from the attacker to the compromised endpoint and the output of those commands back to the attacker.\ Because this communication is so critical for an adversary, they often use techniques designed to hide the true nature of the communications. There are many different techniques used to establish and communicate over these channels. This Analytic Story provides searches that look for a variety of the techniques used for these channels, as well as indications that these channels are active, by examining logs associated with border control devices and network-access control lists. @@ -212,7 +212,7 @@ version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1003", "https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Creation of Shadow Copy - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Pass the Hash Attempts", "ESCU - Investigate Previous Unseen User"] +searches = ["ESCU - Access LSASS Memory for Dump Creation - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Creation of Shadow Copy with wmic and powershell - Rule", "ESCU - Create Remote Thread into LSASS - Rule", "ESCU - Dump LSASS via comsvcs DLL - Rule", "ESCU - Attempted Credential Dump From Registry via Reg exe - Rule", "ESCU - Detect Mimikatz Using Loaded Images - Rule", "ESCU - Credential Dumping via Symlink to Shadow Copy - Rule", "ESCU - Creation of Shadow Copy - Rule", "ESCU - Credential Dumping via Copy Command from Shadow Copy - Rule", "ESCU - Unsigned Image Loaded by LSASS - Rule", "ESCU - Detect Credential Dumping through LSASS access - Rule", "ESCU - Investigate Pass the Ticket Attempts", "ESCU - Investigate Previous Unseen User", "ESCU - Investigate Failed Logins for Multiple Destinations", "ESCU - Investigate Pass the Hash Attempts"] description = Uncover activity consistent with credential dumping, a technique wherein attackers compromise systems and attempt to obtain and exfiltrate passwords. The threat actors use these pilfered credentials to further escalate privileges and spread throughout a target environment. The included searches in this Analytic Story are designed to identify attempts to credential dumping. narrative = Credential dumping—gathering credentials from a target system, often hashed or encrypted—is a common attack technique. Even though the credentials may not be in plain text, an attacker can still exfiltrate the data and set to cracking it offline, on their own systems. The threat actors target a variety of sources to extract them, including the Security Accounts Manager (SAM), Local Security Authority (LSA), NTDS from Domain Controllers, or the Group Policy Preference (GPP) files.\ Once attackers obtain valid credentials, they use them to move throughout a target network with ease, discovering new systems and identifying assets of interest. Credentials obtained in this manner typically include those of privileged users, which may provide access to more sensitive information and system operations.\ @@ -225,7 +225,7 @@ version = 2 references = ["https://www.us-cert.gov/ncas/alerts/TA18-074A"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Create local admin accounts using net exe - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Process File Activity", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Registry Activity", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Create local admin accounts using net exe - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule", "ESCU - Detect New Local Admin account - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Single Letter Process On Endpoint - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Process Registry Activity", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process File Activity"] description = Monitor for suspicious activities associated with DHS Technical Alert US-CERT TA18-074A. Some of the activities that adversaries used in these compromises included spearfishing attacks, malware, watering-hole domains, many and more. narrative = The frequency of nation-state cyber attacks has increased significantly over the last decade. Employing numerous tactics and techniques, these attacks continue to escalate in complexity. \ There is a wide range of motivations for these state-sponsored hacks, including stealing valuable corporate, military, or diplomatic dataѿall of which could confer advantages in various arenas. They may also target critical infrastructure. \ @@ -239,7 +239,7 @@ version = 1 references = ["https://www.us-cert.gov/ncas/alerts/TA13-088A", "https://www.imperva.com/learn/application-security/dns-amplification/"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User"] +searches = ["ESCU - Large Volume of DNS ANY Queries - Rule", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User", "ESCU - Get Notable Info"] description = DNS poses a serious threat as a Denial of Service (DOS) amplifier, if it responds to `ANY` queries. This Analytic Story can help you detect attackers who may be abusing your company's DNS infrastructure to launch amplification attacks, causing Denial of Service to other victims. narrative = The Domain Name System (DNS) is the protocol used to map domain names to IP addresses. It has been proven to work very well for its intended function. However if DNS is misconfigured, servers can be abused by attackers to levy amplification or redirection attacks against victims. Because DNS responses to `ANY` queries are so much larger than the queries themselves--and can be made with a UDP packet, which does not require a handshake--attackers can spoof the source address of the packet and cause much more data to be sent to the victim than if they sent the traffic themselves. The `ANY` requests are will be larger than normal DNS server requests, due to the fact that the server provides significant details, such as MX records and associated IP addresses. A large volume of this traffic can result in a DOS on the victim's machine. This misconfiguration leads to two possible victims, the first being the DNS servers participating in an attack and the other being the hosts that are the targets of the DOS attack.\ The search in this story can help you to detect if attackers are abusing your company's DNS infrastructure to launch DNS amplification attacks causing Denial of Service to other victims. @@ -251,7 +251,7 @@ version = 1 references = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - DNS record changed - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Get DNS Server History for a host"] +searches = ["ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - DNS record changed - Rule", "ESCU - Get DNS Server History for a host"] description = Secure your environment against DNS hijacks with searches that help you detect and investigate unauthorized changes to DNS records. narrative = Dubbed the Achilles heel of the Internet (see https://www.f5.com/labs/articles/threat-intelligence/dns-is-still-the-achilles-heel-of-the-internet-25613), DNS plays a critical role in routing web traffic but is notoriously vulnerable to attack. One reason is its distributed nature. It relies on unstructured connections between millions of clients and servers over inherently insecure protocols.\ The gravity and extent of the importance of securing DNS from attacks is undeniable. The fallout of compromised DNS can be disastrous. Not only can hackers bring down an entire business, they can intercept confidential information, emails, and login credentials, as well. \ @@ -270,7 +270,7 @@ version = 1 references = ["https://www.cisecurity.org/controls/data-protection/", "https://www.sans.org/reading-room/whitepapers/dns/splunk-detect-dns-tunneling-37022", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect USB device insertion - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Responsible For The DNS Traffic"] +searches = ["ESCU - Detect USB device insertion - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] description = Fortify your data-protection arsenal--while continuing to ensure data confidentiality and integrity--with searches that monitor for and help you investigate possible signs of data exfiltration. narrative = Attackers can leverage a variety of resources to compromise or exfiltrate enterprise data. Common exfiltration techniques include remote-access channels via low-risk, high-payoff active-collections operations and close-access operations using insiders and removable media. While this Analytic Story is not a comprehensive listing of all the methods by which attackers can exfiltrate data, it provides a useful starting point. @@ -281,7 +281,7 @@ version = 2 references = ["https://attack.mitre.org/wiki/Technique/T1089", "https://blog.malwarebytes.com/cybercrime/2015/11/vonteera-adware-uses-certificates-to-disable-anti-malware/", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Tools-Report.pdf"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Attempt To Stop Security Service - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Attempt To Stop Security Service - Rule", "ESCU - Attempt To Add Certificate To Untrusted Store - Rule", "ESCU - Unload Sysmon Filter Driver - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Looks for activities and techniques associated with the disabling of security tools on a Windows system, such as suspicious `reg.exe` processes, processes launching netsh, and many others. narrative = Attackers employ a variety of tactics in order to avoid detection and operate without barriers. This often involves modifying the configuration of security tools to get around them or explicitly disabling them to prevent them from running. This Analytic Story includes searches that look for activity consistent with attackers attempting to disable various security mechanisms. Such activity may involve monitoring for suspicious registry activity, as this is where much of the configuration for Windows and various other programs reside, or explicitly attempting to shut down security-related services. Other times, attackers attempt various tricks to prevent specific programs from running, such as adding the certificates with which the security tools are signed to a blacklist (which would prevent them from running). @@ -292,7 +292,7 @@ version = 2 references = ["https://www.fireeye.com/blog/threat-research/2017/09/apt33-insights-into-iranian-cyber-espionage.html", "https://umbrella.cisco.com/blog/2013/04/15/on-the-trail-of-malicious-dynamic-dns-domains/", "http://www.noip.com/blog/2014/07/11/dynamic-dns-can-use-2/", "https://www.splunk.com/blog/2015/08/04/detecting-dynamic-dns-domains-in-splunk.html"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Process Responsible For The DNS Traffic"] +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Detect web traffic to dynamic domain providers - Rule", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Investigate Web Activity From src ip", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint"] description = Detect and investigate hosts in your environment that may be communicating with dynamic domain providers. Attackers may leverage these services to help them avoid firewall blocks and blacklists. narrative = Dynamic DNS services (DDNS) are legitimate low-cost or free services that allow users to rapidly update domain resolutions to IP infrastructure. While their usage can be benign, malicious actors can abuse DDNS to host harmful payloads or interactive-command-and-control infrastructure. These attackers will manually update or automate domain resolution changes by routing dynamic domains to IP addresses that circumvent firewall blocks and blacklists and frustrate a network defender's analytic and investigative processes. These searches will look for DNS queries made from within your infrastructure to suspicious dynamic domains and then investigate more deeply, when appropriate. While this list of top-level dynamic domains is not exhaustive, it can be dynamically updated as new suspicious dynamic domains are identified. @@ -303,7 +303,7 @@ version = 1 references = ["https://www.us-cert.gov/ncas/alerts/TA18-201A", "https://www.first.org/resources/papers/conf2017/Advanced-Incident-Detection-and-Threat-Hunting-using-Sysmon-and-Splunk.pdf", "https://www.vkremez.com/2017/05/emotet-banking-trojan-malware-analysis.html"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Detection of tools built by NirSoft - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Process Information For Port Activity", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Detect rarely used executables, specific registry paths that may confer malware survivability and persistence, instances where cmd.exe is used to launch script interpreters, and other indicators that the Emotet financial malware has compromised your environment. narrative = The trojan downloader known as Emotet first surfaced in 2014, when it was discovered targeting the banking industry to steal credentials. However, according to a joint technical alert (TA) issued by three government agencies (https://www.us-cert.gov/ncas/alerts/TA18-201A), Emotet has evolved far beyond those beginnings to become what a ThreatPost article called a threat-delivery service(see https://threatpost.com/emotet-malware-evolves-beyond-banking-to-threat-delivery-service/134342/). For example, in early 2018, Emotet was found to be using its loader function to spread the Quakbot and Ransomware variants. \ According to the TA, the the malware continues to be among the most costly and destructive malware affecting the private and public sectors. Researchers have linked it to the threat group Mealybug, which has also been on the security communitys radar since 2014.\ @@ -316,7 +316,7 @@ version = 2 references = ["https://www.us-cert.gov/HIDDEN-COBRA-North-Korean-Malicious-Cyber-Activity", "https://www.operationblockbuster.com/wp-content/uploads/2016/02/Operation-Blockbuster-Destructive-Malware-Report.pdf"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - First time seen command line argument - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - Create or delete windows shares using net exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Investigate Web Activity From Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Responsible For The DNS Traffic"] +searches = ["ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Suspicious File Write - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Detect Outbound SMB Traffic - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Create or delete windows shares using net exe - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Get Parent Process Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Monitor for and investigate activities, including the creation or deletion of hidden shares and file writes, that may be evidence of infiltration by North Korean government-sponsored cybercriminals. Details of this activity were reported in DHS Report TA-18-149A. narrative = North Korea's government-sponsored "cyber army" has been slowly building momentum and gaining sophistication over the last 15 years or so. As a result, the group's activity, which the US government refers to as "Hidden Cobra," has surreptitiously crept onto the collective radar as a preeminent global threat.\ These state-sponsored actors are thought to be responsible for everything from a hack on a South Korean nuclear plant to an attack on Sony in anticipation of its release of the movie "The Interview" at the end of 2014. They're also notorious for cyberespionage. In recent years, the group seems to be focused on financial crimes, such as cryptojacking.\ @@ -330,7 +330,7 @@ version = 1 references = ["https://blog.malwarebytes.com/cybercrime/2016/09/hosts-file-hijacks/"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Windows hosts file modification - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Windows hosts file modification - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint"] description = Detect evidence of tactics used to redirect traffic from a host to a destination other than the one intended--potentially one that is part of an adversary's attack infrastructure. An example is redirecting communications regarding patches and updates or misleading users into visiting a malicious website. narrative = Attackers will often attempt to manipulate client communications for nefarious purposes. In some cases, an attacker may endeavor to modify a local host file to redirect communications with resources (such as antivirus or system-update services) to prevent clients from receiving patches or updates. In other cases, an attacker might use this tactic to have the client connect to a site that looks like the intended site, but instead installs malware or collects information from the victim. Additionally, an attacker may redirect a victim in order to execute a MITM attack and observe communications. @@ -341,7 +341,7 @@ version = 1 references = ["http://www.deependresearch.org/2016/04/jboss-exploits-view-from-victim.html"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Get Notable Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Investigate Web Activity From Host"] description = In March of 2016, adversaries were seen using JexBoss--an open-source utility used for testing and exploiting JBoss application servers. These searches help detect evidence of these attacks, such as network connections to external resources or web services spawning atypical child processes, among others. narrative = This Analytic Story looks for probing and exploitation attempts targeting JBoss application servers. While the vulnerabilities associated with this story are rather dated, they were leveraged in a spring 2016 campaign in connection with the Samsam ransomware variant. Incidents involving this ransomware are unique, in that they begin with attacks against vulnerable services, rather than the phishing or drive-by attacks more common with ransomware. In this case, vulnerable JBoss applications appear to be the target of choice.\ It is helpful to understand how often a notable event generated by this story occurs, as well as the commonalities between some of these events, both of which may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. It may also help to understand whether the issue is restricted to a single user/system or whether it is broader in scope.\ @@ -366,7 +366,7 @@ version = 1 references = ["https://github.com/splunk/cloud-datamodel-security-research"] maintainers = "Rod Soto, Splunk" spec_version = 3 -searches = ["ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - GCP Kubernetes activity by src ip", "ESCU - Get Notable Info", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Amazon EKS Kubernetes Pod scan detection - Rule", "ESCU - Amazon EKS Kubernetes cluster scan detection - Rule", "ESCU - GCP Kubernetes cluster scan detection - Rule", "ESCU - Amazon EKS Kubernetes activity by src ip", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - GCP Kubernetes activity by src ip", "ESCU - Get Notable Info"] description = This story addresses detection against Kubernetes cluster fingerprint scan and attack by providing information on items such as source ip, user agent, cluster names. narrative = Kubernetes is the most used container orchestration platform, this orchestration platform contains sensitve information and management priviledges of production workloads, microservices and applications. These searches allow operator to detect suspicious unauthenticated requests from the internet to kubernetes cluster. @@ -377,7 +377,7 @@ version = 2 references = ["https://www.fireeye.com/blog/executive-perspective/2015/08/malware_lateral_move.html"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Detect Activity Related to Pass the Hash Attacks - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Schtasks scheduling job on remote system - Rule", "ESCU - Remote Desktop Process Running On System - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] description = Detect and investigate tactics, techniques, and procedures around how attackers move laterally within the enterprise. Because lateral movement can expose the adversary to detection, it should be an important focus for security analysts. narrative = Once attackers gain a foothold within an enterprise, they will seek to expand their accesses and leverage techniques that facilitate lateral movement. Attackers will often spend quite a bit of time and effort moving laterally. Because lateral movement renders an attacker the most vulnerable to detection, it's an excellent focus for detection and investigation.\ Indications of lateral movement can include the abuse of system utilities (such as `psexec.exe`), unauthorized use of remote desktop services, `file/admin$` shares, WMI, PowerShell, pass-the-hash, or the abuse of scheduled tasks. Organizations must be extra vigilant in detecting lateral movement techniques and look for suspicious activity in and around high-value strategic network assets, such as Active Directory, which are often considered the primary target or "crown jewels" to a persistent threat actor.\ @@ -392,7 +392,7 @@ version = 4 references = ["https://blogs.mcafee.com/mcafee-labs/malware-employs-powershell-to-infect-systems/", "https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Attempt To Set Default PowerShell Execution Policy To Unrestricted or Bypass - Rule", "ESCU - Malicious PowerShell Process With Obfuscation Techniques - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule", "ESCU - Malicious PowerShell Process - Encoded Command - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] description = Attackers are finding stealthy ways "live off the land," leveraging utilities and tools that come standard on the endpoint--such as PowerShell--to achieve their goals without downloading binary files. These searches can help you detect and investigate PowerShell command-line options that may be indicative of malicious intent. narrative = The searches in this Analytic Story monitor for parameters often used for malicious purposes. It is helpful to understand how often the notable events generated by this story occur, as well as the commonalities between some of these events. These factors may provide clues about whether this is a common occurrence of minimal concern or a rare event that may require more extensive investigation. Likewise, it is important to determine whether the issue is restricted to a single user/system or is broader in scope.\ The following factors may assist you in determining whether the event is malicious: \ @@ -412,7 +412,7 @@ version = 1 references = ["https://www.carbonblack.com/2016/03/04/tracking-locky-ransomware-using-carbon-black/"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - All backup logs for host", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User"] +searches = ["ESCU - Unsuccessful Netbackup backups - Rule", "ESCU - Extended Period Without Successful Netbackup Backups - Rule", "ESCU - All backup logs for host", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For User"] description = Address common concerns when monitoring your backup processes. These searches can help you reduce risks from ransomware, device theft, or denial of physical access to a host by backing up data on endpoints. narrative = Having backups is a standard best practice that helps ensure continuity of business operations. Having mature backup processes can also help you reduce the risks of many security-related incidents and streamline your response processes. The detection searches in this Analytic Story will help you identify systems that have backup failures, as well as systems that have not been backed up for an extended period of time. The story will also return the notable event history and all of the backup logs for an endpoint. @@ -423,7 +423,7 @@ version = 1 references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Identify and investigate prohibited/unauthorized software or processes that may be concealing malicious behavior within your environment. narrative = It is critical to identify unauthorized software and processes running on enterprise endpoints and determine whether they are likely to be malicious. This Analytic Story requires the user to populate the Interesting Processes table within Enterprise Security with prohibited processes. An included support search will augment this data, adding information on processes thought to be malicious. This search requires data from endpoint detection-and-response solutions, endpoint data sources (such as Sysmon), or Windows Event Logs--assuming that the Active Directory administrator has enabled process tracking within the System Event Audit Logs.\ It is important to investigate any software identified as suspicious, in order to understand how it was installed or executed. Analyzing authentication logs or any historic notable events might elicit additional investigative leads of interest. For best results, schedule the search to run every two weeks. @@ -435,7 +435,7 @@ version = 1 references = ["https://learn.cisecurity.org/20-controls-download"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] +searches = ["ESCU - No Windows Updates in a time frame - Rule", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Monitor your enterprise to ensure that your endpoints are being patched and updated. Adversaries notoriously exploit known vulnerabilities that could be mitigated by applying routine security patches. narrative = It is a common best practice to ensure that endpoints are being patched and updated in a timely manner, in order to reduce the risk of compromise via a publicly disclosed vulnerability. Timely application of updates/patches is important to eliminate known vulnerabilities that may be exploited by various threat actors.\ Searches in this analytic story are designed to help analysts monitor endpoints for system patches and/or updates. This helps analysts identify any systems that are not successfully updated in a timely matter.\ @@ -448,7 +448,7 @@ version = 1 references = ["https://technet.microsoft.com/library/bb490939.aspx", "https://htmlpreview.github.io/?https://github.com/MatthewDemaske/blogbackup/blob/master/netshell.html", "http://blog.jpcert.or.jp/2016/01/windows-commands-abused-by-attackers.html"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Processes launching netsh - Rule", "ESCU - Processes created by netsh - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Processes created by netsh - Rule", "ESCU - Processes launching netsh - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Detect activities and various techniques associated with the abuse of `netsh.exe`, which can disable local firewall settings or set up a remote connection to a host from an infected system. narrative = It is a common practice for attackers of all types to leverage native Windows tools and functionality to execute commands for malicious reasons. One such tool on Windows OS is `netsh.exe`,a command-line scripting utility that allows you to--either locally or remotely--display or modify the network configuration of a computer that is currently running. `Netsh.exe` can be used to discover and disable local firewall settings. It can also be used to set up a remote connection to a host from an infected system.\ To get started, run the detection search to identify parent processes of `netsh.exe`. @@ -460,7 +460,7 @@ version = 2 references = ["https://www.symantec.com/blogs/threat-intelligence/orangeworm-targets-healthcare-us-europe-asia", "https://www.infosecurity-magazine.com/news/healthcare-targeted-by-hacker/"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - First time seen command line argument - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Detect activities and various techniques associated with the Orangeworm Attack Group, a group that frequently targets the healthcare industry. narrative = In May of 2018, the attack group Orangeworm was implicated for installing a custom backdoor called Trojan.Kwampirs within large international healthcare corporations in the United States, Europe, and Asia. This malware provides the attackers with remote access to the target system, decrypting and extracting a copy of its main DLL payload from its resource section. Before writing the payload to disk, it inserts a randomly generated string into the middle of the decrypted payload in an attempt to evade hash-based detections.\ Awareness of the Orangeworm group first surfaced in January, 2015. It has conducted targeted attacks against related industries, as well, such as pharmaceuticals and healthcare IT solution providers.\ @@ -475,7 +475,7 @@ version = 1 references = ["https://www.fireeye.com/blog/threat-research/2019/04/spear-phishing-campaign-targets-ukraine-government.html"] maintainers = "Splunk Research Team, Splunk" spec_version = 3 -searches = ["ESCU - Suspicious LNK file launching a process - Rule", "ESCU - Detect Oulook exe writing a zip file - Rule", "ESCU - Get Parent Process Info"] +searches = ["ESCU - Detect Oulook exe writing a zip file - Rule", "ESCU - Suspicious LNK file launching a process - Rule", "ESCU - Get Parent Process Info"] description = Detect signs of malicious payloads that may indicate that your environment has been breached via a phishing attack. narrative = Despite its simplicity, phishing remains the most pervasive and dangerous cyberthreat. In fact, research shows that as many as [91% of all successful attacks](https://digitalguardian.com/blog/91-percent-cyber-attacks-start-phishing-email-heres-how-protect-against-phishing) are initiated via a phishing email. \ As most people know, these emails use fraudulent domains, [email scraping](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), familiar contact names inserted as senders, and other tactics to lure targets into clicking a malicious link, opening an attachment with a [nefarious payload](https://www.cyberscoop.com/emotet-trojan-phishing-scraping-templates-cofense-geodo/), or entering sensitive personal information that perpetrators may intercept. This attack technique requires a relatively low level of skill and allows adversaries to easily cast a wide net. Worse, because its success relies on the gullibility of humans, it's impossible to completely "automate" it out of your environment. However, you can use ES and ESCU to detect and investigate potentially malicious payloads injected into your environment subsequent to a phishing attack. \ @@ -493,7 +493,7 @@ version = 1 references = ["https://www.infosecurity-magazine.com/news/scope-of-mudcarp-attacks-highlight-1/", "http://blog.amossys.fr/badflick-is-not-so-bad.html"] maintainers = "iDefense Cyber Espionage Team, iDefense" spec_version = 3 -searches = ["ESCU - First time seen command line argument - Rule", "ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Malicious PowerShell Process - Connect To Internet With Hidden Window - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Monitor your environment for suspicious behaviors that resemble the techniques employed by the MUDCARP threat group. narrative = This story was created as a joint effort between iDefense and Splunk.\ iDefense analysts have recently discovered a Windows executable file that, upon execution, spoofs a decryption tool and then drops a file that appears to be the custom-built javascript backdoor, "Orz," which is associated with the threat actors known as MUDCARP (as well as "temp.Periscope" and "Leviathan"). The file is executed using Wscript.\ @@ -531,7 +531,7 @@ version = 1 references = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get Process Information For Port Activity", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Protocol or Port Mismatch - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] description = Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. narrative = A traditional security best practice is to control the ports, protocols, and services allowed within your environment. By limiting the services and protocols to those explicitly approved by policy, administrators can minimize the attack surface. The combined effect allows both network defenders and security controls to focus and not be mired in superfluous traffic or data types. Looking for deviations to policy can identify attacker activity that abuses services and protocols to run on alternate or non-standard ports in the attempt to avoid detection or frustrate forensic analysts. @@ -542,7 +542,7 @@ version = 1 references = ["https://www.carbonblack.com/2017/06/28/carbon-black-threat-research-technical-analysis-petya-notpetya-ransomware/", "https://www.splunk.com/blog/2017/06/27/closing-the-detection-to-mitigation-gap-or-to-petya-or-notpetya-whocares-.html"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Get Process Information For Port Activity", "ESCU - Get Registry Activities", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - SMB Traffic Spike - MLTK - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - TOR Traffic - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Scheduled tasks used in BadRabbit ransomware - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Prohibited Network Traffic Allowed - Rule", "ESCU - SMB Traffic Spike - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Process Information For Port Activity", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Leverage searches that allow you to detect and investigate unusual activities that might relate to ransomware--spikes in SMB traffic, suspicious wevtutil usage, the presence of common ransomware extensions, and system processes run from unexpected locations, and many others. narrative = Ransomware is an ever-present risk to the enterprise, wherein an infected host encrypts business-critical data, holding it hostage until the victim pays the attacker a ransom. There are many types and varieties of ransomware that can affect an enterprise. Attackers can deploy ransomware to enterprises through spearphishing campaigns and driveby downloads, as well as through traditional remote service-based exploitation. In the case of the WannaCry campaign, there was self-propagating wormable functionality that was used to maximize infection. Fortunately, organizations can apply several techniques--such as those in this Analytic Story--to detect and or mitigate the effects of ransomware. @@ -553,7 +553,7 @@ version = 1 references = ["https://www.fireeye.com/blog/executive-perspective/2015/09/the_new_route_toper.html", "https://www.cisco.com/c/en/us/about/security-center/event-response/synful-knock.html"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Detect New Login Attempts to Routers - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint"] description = Validate the security configuration of network infrastructure and verify that only authorized users and systems are accessing critical assets. Core routing and switching infrastructure are common strategic targets for attackers. narrative = Networking devices, such as routers and switches, are often overlooked as resources that attackers will leverage to subvert an enterprise. Advanced threats actors have shown a proclivity to target these critical assets as a means to siphon and redirect network traffic, flash backdoored operating systems, and implement cryptographic weakened algorithms to more easily decrypt network traffic.\ This Analytic Story helps you gain a better understanding of how your network devices are interacting with your hosts. By compromising your network devices, attackers can obtain direct access to the company's internal infrastructure— effectively increasing the attack surface and accessing private services/data. @@ -565,7 +565,7 @@ version = 1 references = ["https://capec.mitre.org/data/definitions/66.html", "https://www.incapsula.com/web-application-security/sql-injection.html"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] +searches = ["ESCU - SQL Injection with Long URLs - Rule", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Use the searches in this Analytic Story to help you detect structured query language (SQL) injection attempts characterized by long URLs that contain malicious parameters. narrative = It is very common for attackers to inject SQL parameters into vulnerable web applications, which then interpret the malicious SQL statements.\ This Analytic Story contains a search designed to identify attempts by attackers to leverage this technique to compromise a host and gain a foothold in the target environment. @@ -577,7 +577,7 @@ version = 1 references = ["https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/", "https://nakedsecurity.sophos.com/2018/07/31/samsam-the-almost-6-million-ransomware/", "https://thehackernews.com/2018/07/samsam-ransomware-attacks.html"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Get Process Information For Port Activity", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Remote Desktop Network Traffic - Rule", "ESCU - Batch File Write to System32 - Rule", "ESCU - Detect PsExec With accepteula Flag - Rule", "ESCU - Remote Desktop Network Bruteforce - Rule", "ESCU - Spike in File Writes - Rule", "ESCU - Common Ransomware Notes - Rule", "ESCU - Detect malicious requests to exploit JBoss servers - Rule", "ESCU - Prohibited Software On Endpoint - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - File with Samsam Extension - Rule", "ESCU - Common Ransomware Extensions - Rule", "ESCU - Samsam Test File Write - Rule", "ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Backup Logs For Endpoint", "ESCU - Get Process Information For Port Activity", "ESCU - Get Update Logs For Endpoint", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Successful Remote Desktop Authentications", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. narrative = The first version of the SamSam ransomware (a.k.a. Samas or SamsamCrypt) was launched in 2015 by a group of Iranian threat actors. The malicious software has affected and continues to affect thousands of victims and has raised almost $6M in ransom.\ Although categorized under the heading of ransomware, SamSam campaigns have some importance distinguishing characteristics. Most notable is the fact that conventional ransomware is a numbers game. Perpetrators use a "spray-and-pray" approach with phishing campaigns or other mechanisms, charging a small ransom (typically under $1,000). The goal is to find a large number of victims willing to pay these mini-ransoms, adding up to a lucrative payday. They use relatively simple methods for infecting systems.\ @@ -593,7 +593,7 @@ version = 1 references = ["https://meltdownattack.com/"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Spectre and Meltdown Vulnerable Systems - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint"] description = Assess and mitigate your systems' vulnerability to Spectre and Meltdown exploitation with the searches in this Analytic Story. narrative = Meltdown and Spectre exploit critical vulnerabilities in modern CPUs that allow unintended access to data in memory. This Analytic Story will help you identify the systems can be patched for these vulnerabilities, as well as those that still need to be patched. @@ -604,7 +604,7 @@ version = 1 references = ["http://www.splunk.com/view/SP-CAAAPQ6#announce", "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2016-4859"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable Info"] +searches = ["ESCU - Open Redirect in Splunk Web - Rule", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Keeping your Splunk deployment up to date is critical and may help you reduce the risk of CVE-2016-4859, an open-redirection vulnerability within some older versions of Splunk Enterprise. The detection search will help ensure that users are being properly authenticated and not being redirected to malicious domains. narrative = This Analytic Story is associated with CVE-2016-4859, an open-redirect vulnerability in the following versions of Splunk Enterprise:\ \ @@ -624,7 +624,7 @@ version = 1 references = ["https://nvd.nist.gov/vuln/detail/CVE-2018-11409", "https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings", "https://www.exploit-db.com/exploits/44865/"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Get Notable Info", "ESCU - Investigate Network Traffic From src ip", "ESCU - Investigate Web Activity From src ip", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Splunk Enterprise Information Disclosure - Rule", "ESCU - Investigate Web Activity From src ip", "ESCU - Investigate Network Traffic From src ip", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Reduce the risk of CVE-2018-11409, an information disclosure vulnerability within some older versions of Splunk Enterprise, with searches designed to help ensure that your Splunk system does not leak information to authenticated users. narrative = Although there have been no reports of it being exploited, Splunk Enterprise versions through 7.0.1 reportedly have a vulnerability that may expose information through a REST endpoint (read more here: https://www.splunk.com/view/SP-CAAAP5E#VulnerabilityDescriptionsandRatings). NIST has included it in its vulnerability database (read more here: https://nvd.nist.gov/vuln/detail/CVE-2018-11409). The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Customers should upgrade to the latest version to reduce the risk of this vulnerability.\ Splunk Enterprise exposes partial information about the host operating system, hardware, and Splunk license. Splunk Enterprise before 6.6.0 exposes this information without authentication. Splunk Enterprise 6.6.0 and later exposes this information only to authenticated Splunk users. Based on the information exposure, Splunk characterizes this issue as a low severity impact.\ @@ -638,7 +638,7 @@ version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get EC2 Launch Details", "ESCU - Investigate AWS activities via region name", "ESCU - Get Notable Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] +searches = ["ESCU - EC2 Instance Started With Previously Unseen User - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - MLTK - Rule", "ESCU - Abnormally High AWS Instances Terminated by User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - Rule", "ESCU - Abnormally High AWS Instances Launched by User - MLTK - Rule", "ESCU - EC2 Instance Started In Previously Unseen Region - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Investigate AWS activities via region name", "ESCU - Get EC2 Launch Details", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Use the searches in this Analytic Story to monitor your AWS EC2 instances for evidence of anomalous activity and suspicious behaviors, such as EC2 instances that originate from unusual locations or those launched by previously unseen users (among others). Included investigative searches will help you probe more deeply, when the information warrants it. narrative = AWS CloudTrail is an AWS service that helps you enable governance, compliance, and risk auditing within your AWS account. Actions taken by a user, role, or an AWS service are recorded as events in CloudTrail. It is crucial for a company to monitor events and actions taken in the AWS Console, AWS command-line interface, and AWS SDKs and APIs to ensure that your EC2 instances are not vulnerable to attacks. This Analytic Story identifies suspicious activities in your AWS EC2 instances and helps you respond and investigate those activities. @@ -649,7 +649,7 @@ version = 1 references = ["https://docs.aws.amazon.com/IAM/latest/UserGuide/cloudtrail-integration.html"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - Detect new user AWS Console Login - Rule", "ESCU - AWS Investigate User Activities By ARN"] +searches = ["ESCU - Detect new user AWS Console Login - Rule", "ESCU - Detect AWS Console Login by User from New Country - Rule", "ESCU - Detect AWS Console Login by User from New City - Rule", "ESCU - Detect AWS Console Login by User from New Region - Rule", "ESCU - AWS Investigate User Activities By ARN"] description = Monitor your AWS authentication events using your CloudTrail logs. Searches within this Analytic Story will help you stay aware of and investigate suspicious logins. narrative = It is important to monitor and control who has access to your AWS infrastructure. Detecting suspicious logins to your AWS infrastructure will provide good starting points for investigations. Abusive behaviors caused by compromised credentials can lead to direct monetary costs, as you will be billed for any EC2 instances created by the attacker. @@ -660,7 +660,7 @@ version = 2 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect S3 access from a new IP - Rule", "ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - Detect New Open S3 buckets - Rule", "ESCU - Investigate AWS activities via region name", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History"] +searches = ["ESCU - Detect Spike in S3 Bucket deletion - Rule", "ESCU - Detect New Open S3 buckets - Rule", "ESCU - Detect S3 access from a new IP - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Investigate AWS activities via region name", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - AWS S3 Bucket details via bucketName", "ESCU - Get All AWS Activity From IP Address"] description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. narrative = As cloud computing has exploded, so has the number of creative attacks on virtual environments. And as the number-two cloud-service provider, Amazon Web Services (AWS) has certainly had its share.\ Amazon's "shared responsibility" model dictates that the company has responsibility for the environment outside of the VM and the customer is responsible for the security inside of the S3 container. As such, it's important to stay vigilant for activities that may belie suspicious behavior inside of your environment.\ @@ -673,7 +673,7 @@ version = 1 references = ["https://rhinosecuritylabs.com/aws/hiding-cloudcobalt-strike-beacon-c2-using-amazon-apis/"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get User Information from Identity Table", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get All AWS Activity From IP Address", "ESCU - Get Notable Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - AWS Network ACL Details from ID", "ESCU - Get Notable History", "ESCU - Get Process Responsible For The DNS Traffic"] +searches = ["ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Info", "ESCU - Get Process Information For Port Activity", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - AWS Investigate User Activities By ARN", "ESCU - AWS Network Interface details via resourceId", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get All AWS Activity From IP Address", "ESCU - AWS Network ACL Details from ID"] description = Leverage these searches to monitor your AWS network traffic for evidence of anomalous activity and suspicious behaviors, such as a spike in blocked outbound traffic in your virtual private cloud (VPC). narrative = A virtual private cloud (VPC) is an on-demand managed cloud-computing service that isolates computing resources for each client. Inside the VPC container, the environment resembles a physical network. \ Amazon's VPC service enables you to launch EC2 instances and leverage other Amazon resources. The traffic that flows in and out of this VPC can be controlled via network access-control rules and security groups. Amazon also has a feature called VPC Flow Logs that enables you to log IP traffic going to and from the network interfaces in your VPC. This data is stored using Amazon CloudWatch Logs.\ @@ -687,7 +687,7 @@ version = 2 references = ["https://attack.mitre.org/wiki/Technique/T1059", "https://www.microsoft.com/en-us/wdsi/threats/macro-malware", "https://www.fireeye.com/content/dam/fireeye-www/services/pdfs/mandiant-apt1-report.pdf"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - First time seen command line argument - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Detect Use of cmd exe to Launch Script Interpreters - Rule", "ESCU - First time seen command line argument - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Leveraging the Windows command-line interface (CLI) is one of the most common attack techniques--one that is also detailed in the MITRE ATT&CK framework. Use this Analytic Story to help you identify unusual or suspicious use of the CLI on Windows systems. narrative = The ability to execute arbitrary commands via the Windows CLI is a primary goal for the adversary. With access to the shell, an attacker can easily run scripts and interact with the target system. Often, attackers may only have limited access to the shell or may obtain access in unusual ways. In addition, malware may execute and interact with the CLI in ways that would be considered unusual and inconsistent with typical user activity. This provides defenders with opportunities to identify suspicious use and investigate, as appropriate. This Analytic Story contains various searches to help identify this suspicious activity, as well as others to aid you in deeper investigation. @@ -698,7 +698,7 @@ version = 1 references = ["http://blogs.splunk.com/2015/10/01/random-words-on-entropy-and-dns/", "http://www.darkreading.com/analytics/security-monitoring/got-malware-three-signs-revealed-in-dns-traffic/d/d-id/1139680", "https://live.paloaltonetworks.com/t5/Threat-Vulnerability-Articles/What-are-suspicious-DNS-queries/ta-p/71454"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get DNS Server History for a host", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get DNS traffic ratio", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Process Responsible For The DNS Traffic"] +searches = ["ESCU - Detect Long DNS TXT Record Response - Rule", "ESCU - Detect hosts connecting to dynamic domain providers - Rule", "ESCU - Clients Connecting to Multiple DNS Servers - Rule", "ESCU - Detection of DNS Tunnels - Rule", "ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule", "ESCU - Excessive DNS Failures - Rule", "ESCU - DNS Query Length With High Standard Deviation - Rule", "ESCU - DNS Query Length Outliers - MLTK - Rule", "ESCU - Get Parent Process Info", "ESCU - Get DNS traffic ratio", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Responsible For The DNS Traffic", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get DNS Server History for a host", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] description = Attackers often attempt to hide within or otherwise abuse the domain name system (DNS). You can thwart attempts to manipulate this omnipresent protocol by monitoring for these types of abuses. narrative = Although DNS is one of the fundamental underlying protocols that make the Internet work, it is often ignored (perhaps because of its complexity and effectiveness). However, attackers have discovered ways to abuse the protocol to meet their objectives. One potential abuse involves manipulating DNS to hijack traffic and redirect it to an IP address under the attacker's control. This could inadvertently send users intending to visit google.com, for example, to an unrelated malicious website. Another technique involves using the DNS protocol for command-and-control activities with the attacker's malicious code or to covertly exfiltrate data. The searches within this Analytic Story look for these types of abuses. @@ -709,7 +709,7 @@ version = 1 references = ["https://www.splunk.com/blog/2015/06/26/phishing-hits-a-new-level-of-quality/"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Email Info", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Suspicious Email Attachment Extensions - Rule", "ESCU - Suspicious Email - UBA Anomaly - Rule", "ESCU - Monitor Email For Brand Abuse - Rule", "ESCU - Email Attachments With Lots Of Spaces - Rule", "ESCU - Get Emails From Specific Sender", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Email Info", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host"] description = Email remains one of the primary means for attackers to gain an initial foothold within the modern enterprise. Detect and investigate suspicious emails in your environment with the help of the searches in this Analytic Story. narrative = It is a common practice for attackers of all types to leverage targeted spearphishing campaigns and mass mailers to deliver weaponized email messages and attachments. Fortunately, there are a number of ways to monitor email data in Splunk to detect suspicious content.\ Once a phishing message has been detected, the next steps are to answer the following questions: \ @@ -724,7 +724,7 @@ version = 1 references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5", "https://attack.mitre.org/wiki/Technique/T1170"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect mshta exe running scripts in command-line arguments - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Detect Prohibited Applications Spawning cmd exe - Rule", "ESCU - Detect mshta exe running scripts in command-line arguments - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Monitor and detect techniques used by attackers who leverage the mshta.exe process to execute malicious code. narrative = One common adversary tactic is to bypass application white-listing solutions via the mshta.exe process, which executes Microsoft HTML applications with the .hta suffix. In these cases, attackers use the trusted Windows utility to eproxy execution of malicious files, whether an .hta application, javascript, or VBScript.\ One example of a notable mshta.exe attack was the Kovter malware (https://medium.com/@mbromileyDFIR/malware-monday-aebb456356c5) that was implicated in ransomware and click-fraud attacks. Kovter utilized .hta to execute a series of javascript commands, each progressively more dangerous. According to the Mitre Parternship Network (https://attack.mitre.org/wiki/Technique/T1170), FIN7 has leveraged mshta.exe, as has the MuddyWater group, who used it to execute its POWERSTATS payload (which then used the utility to execute additional payloads).\ @@ -737,7 +737,7 @@ version = 1 references = ["https://attack.mitre.org/wiki/Technique/T1078", "https://owasp.org/www-community/attacks/Credential_stuffing", "https://searchsecurity.techtarget.com/answer/What-is-a-password-spraying-attack-and-how-does-it-work"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Investigate Okta Activity by IP Address", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate User Activities In Okta"] +searches = ["ESCU - Multiple Okta Users With Invalid Credentails From The Same IP - Rule", "ESCU - Okta User Logins From Multiple Cities - Rule", "ESCU - Okta Account Lockout Events - Rule", "ESCU - Okta Failed SSO Attempts - Rule", "ESCU - Investigate User Activities In Okta", "ESCU - Investigate Okta Activity by app", "ESCU - Investigate Okta Activity by IP Address"] description = Monitor your Okta environment for suspicious activities. Due to the Covid outbreak, many users are migrating over to leverage cloud services more and more. Okta is a popular tool to manage multiple users and the web-based applications they need to stay productive. The searches in this story will help monitor your Okta environment for suspicious activities and associated user behaviors. narrative = Okta is the leading single sign on (SSO) provider, allowing users to authenticate once to Okta, and from there access a variety of web-based applications. These applications are assigned to users and allow administrators to centrally manage which users are allowed to access which applications. It also provides centralized logging to help understand how the applications are used and by whom. \ While SSO is a major convenience for users, it also provides attackers with an opportunity. If the attacker can gain access to Okta, they can access a variety of applications. As such monitoring the environment is important. \ @@ -750,7 +750,7 @@ version = 2 references = ["https://www.blackhat.com/docs/us-15/materials/us-15-Graeber-Abusing-Windows-Management-Instrumentation-WMI-To-Build-A-Persistent%20Asynchronous-And-Fileless-Backdoor-wp.pdf", "https://www.fireeye.com/blog/threat-research/2017/03/wmimplant_a_wmi_ba.html"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Process Execution via WMI - Rule", "ESCU - Script Execution via WMI - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Script Execution via WMI - Rule", "ESCU - Process Execution via WMI - Rule", "ESCU - WMI Temporary Event Subscription - Rule", "ESCU - Remote WMI Command Attempt - Rule", "ESCU - WMI Permanent Event Subscription - Sysmon - Rule", "ESCU - WMI Permanent Event Subscription - Rule", "ESCU - Remote Process Instantiation via WMI - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Sysmon WMI Activity for Host", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] description = Attackers are increasingly abusing Windows Management Instrumentation (WMI), a framework and associated utilities available on all modern Windows operating systems. Because WMI can be leveraged to manage both local and remote systems, it is important to identify the processes executed and the user context within which the activity occurred. narrative = WMI is a Microsoft infrastructure for management data and operations on Windows operating systems. It includes of a set of utilities that can be leveraged to manage both local and remote Windows systems. Attackers are increasingly turning to WMI abuse in their efforts to conduct nefarious tasks, such as reconnaissance, detection of antivirus and virtual machines, code execution, lateral movement, persistence, and data exfiltration. \ The detection searches included in this Analytic Story are used to look for suspicious use of WMI commands that attackers may leverage to interact with remote systems. The searches specifically look for the use of WMI to run processes on remote systems.\ @@ -763,7 +763,7 @@ version = 1 references = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. narrative = Attackers are developing increasingly sophisticated techniques for hijacking target servers, while evading detection. One such technique that has become progressively more common is registry modification.\ The registry is a key component of the Windows operating system. It has a hierarchical database called "registry" that contains settings, options, and values for executables. Once the threat actor gains access to a machine, they can use reg.exe to modify their account to obtain administrator-level privileges, maintain persistence, and move laterally within the environment.\ @@ -776,7 +776,7 @@ version = 1 references = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get Notable History"] +searches = ["ESCU - EC2 Instance Modified With Previously Unseen User - Rule", "ESCU - AWS Investigate User Activities By ARN", "ESCU - Get EC2 Instance Details by instanceId", "ESCU - Get Notable History"] description = Identify unusual changes to your AWS EC2 instances that may indicate malicious activity. Modifications to your EC2 instances by previously unseen users is an example of an activity that may warrant further investigation. narrative = A common attack technique is to infiltrate a cloud instance and make modifications. The adversary can then secure access to your infrastructure or hide their activities. So it's important to stay alert to changes that may indicate that your environment has been compromised. \ Searches within this Analytic Story can help you detect the presence of a threat by monitoring for EC2 instances that have been created or changed--either by users that have never previously performed these activities or by known users who modify or create instances in a way that have not been done before. This story also provides investigative searches that help you go deeper once you detect suspicious behavior. @@ -788,7 +788,7 @@ version = 2 references = ["https://www.fireeye.com/blog/threat-research/2017/08/monitoring-windows-console-activity-part-two.html", "https://www.splunk.com/pdfs/technical-briefs/advanced-threat-detection-and-response-tech-brief.pdf", "https://www.sans.org/reading-room/whitepapers/logging/detecting-security-incidents-windows-workstation-event-logs-34262"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - System Processes Run From Unexpected Locations - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Detect processes used for System Network Configuration Discovery - Rule", "ESCU - RunDLL Loading DLL By Ordinal - Rule", "ESCU - Detect Rare Executables - Rule", "ESCU - Unusually Long Command Line - Rule", "ESCU - Unusually Long Command Line - MLTK - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Quickly identify systems running new or unusual processes in your environment that could be indicators of suspicious activity. Processes run from unusual locations, those with conspicuously long command lines, and rare executables are all examples of activities that may warrant deeper investigation. narrative = Being able to profile a host's processes within your environment can help you more quickly identify processes that seem out of place when compared to the rest of the population of hosts or asset types.\ This Analytic Story lets you identify processes that are either a) not typically seen running or b) have some sort of suspicious command-line arguments associated with them. This Analytic Story will also help you identify the user running these processes and the associated process activity on the host.\ @@ -801,7 +801,7 @@ version = 1 references = ["https://www.monkey.org/~dugsong/dsniff/"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Process Information For Port Activity", "ESCU - Get Notable Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Protocols passing authentication in cleartext - Rule", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Process Information For Port Activity", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Leverage searches that detect cleartext network protocols that may leak credentials or should otherwise be encrypted. narrative = Various legacy protocols operate by default in the clear, without the protections of encryption. This potentially leaks sensitive information that can be exploited by passively sniffing network traffic. Depending on the protocol, this information could be highly sensitive, or could allow for session hijacking. In addition, these protocols send authentication information, which would allow for the harvesting of usernames and passwords that could potentially be used to authenticate and compromise secondary systems. @@ -812,7 +812,7 @@ version = 1 references = ["https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud", "https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718"] maintainers = "Jim Apger, Splunk" spec_version = 3 -searches = ["ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Get Web Session Information via session id", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info"] +searches = ["ESCU - Web Fraud - Anomalous User Clickspeed - Rule", "ESCU - Web Fraud - Account Harvesting - Rule", "ESCU - Web Fraud - Password Sharing Across Accounts - Rule", "ESCU - Get Web Session Information via session id", "ESCU - Get Emails From Specific Sender", "ESCU - Get Notable History", "ESCU - Get Notable Info"] description = Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. narrative = The Federal Bureau of Investigations (FBI) defines Internet fraud as the use of Internet services or software with Internet access to defraud victims or to otherwise take advantage of them. According to the Bureau, Internet crime schemes are used to steal millions of dollars each year from victims and continue to plague the Internet through various methods. The agency includes phishing scams, data breaches, Denial of Service (DOS) attacks, email account compromise, malware, spoofing, and ransomware in this category.\ These crimes are not the fraud itself, but rather the attack techniques commonly employed by fraudsters in their pursuit of data that enables them to commit malicious actssuch as obtaining and using stolen credit cards. They represent a serious problem that is steadily increasing and not likely to go away anytime soon.\ @@ -828,7 +828,7 @@ version = 1 references = ["https://attack.mitre.org/wiki/Defense_Evasion"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Disabling Remote User Account Control - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Disabling Remote User Account Control - Rule", "ESCU - Suspicious Reg exe Process - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] description = Detect tactics used by malware to evade defenses on Windows endpoints. A few of these include suspicious `reg.exe` processes, files hidden with `attrib.exe` and disabling user-account control, among many others narrative = Defense evasion is a tactic--identified in the MITRE ATT&CK framework--that adversaries employ in a variety of ways to bypass or defeat defensive security measures. There are many techniques enumerated by the MITRE ATT&CK framework that are applicable in this context. This Analytic Story includes searches designed to identify the use of such techniques on Windows platforms. @@ -839,7 +839,7 @@ version = 1 references = ["https://blog.malwarebytes.com/cybercrime/2013/12/file-extensions-2/", "https://attack.mitre.org/wiki/Technique/T1042"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - Execution of File with Multiple Extensions - Rule", "ESCU - Suspicious Changes to File Associations - Rule", "ESCU - Execution of File With Spaces Before Extension - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] description = Detect and investigate suspected abuse of file extensions and Windows file associations. Some of the malicious behaviors involved may include inserting spaces before file extensions or prepending the file extension with a different one, among other techniques. narrative = Attackers use a variety of techniques to entice users to run malicious code or to persist on an endpoint. One way to accomplish these goals is to leverage file extensions and the mechanism Windows uses to associate files with specific applications. \ Since its earliest days, Windows has used extensions to identify file types. Users have become familiar with these extensions and their application associations. For example, if users see that a file ends in `.doc` or `.docx`, they will assume that it is a Microsoft Word document and expect that double-clicking will open it using `winword.exe`. The user will typically also presume that the `.docx` file is safe. \ @@ -854,7 +854,7 @@ version = 2 references = ["https://www.crowdstrike.com/blog/bears-midst-intrusion-democratic-national-committee/", "https://zeltser.com/security-incident-log-review-checklist/", "http://journeyintoir.blogspot.com/2013/01/re-introducing-usnjrnl.html"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Suspicious wevtutil Usage - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Deleting Shadow Copies - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Deleting Shadow Copies - Rule", "ESCU - USN Journal Deletion - Rule", "ESCU - Windows Event Log Cleared - Rule", "ESCU - Suspicious wevtutil Usage - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Vulnerability Logs For Endpoint", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Adversaries often try to cover their tracks by manipulating Windows logs. Use these searches to help you monitor for suspicious activity surrounding log files--an essential component of an effective defense. narrative = Because attackers often modify system logs to cover their tracks and/or to thwart the investigative process, log monitoring is an industry-recognized best practice. While there are legitimate reasons to manipulate system logs, it is still worthwhile to keep track of who manipulated the logs, when they manipulated them, and in what way they manipulated them (determining which accesses, tools, or utilities were employed). Even if no malicious activity is detected, the knowledge of an attempt to manipulate system logs may be indicative of a broader security risk that should be thoroughly investigated.\ The Analytic Story gives users two different ways to detect manipulation of Windows Event Logs and one way to detect deletion of the Update Sequence Number (USN) Change Journal. The story helps determine the history of the host and the users who have accessed it. Finally, the story aides in investigation by retrieving all the information on the process that caused these events (if the process has been identified). @@ -866,7 +866,7 @@ version = 2 references = ["http://www.fuzzysecurity.com/tutorials/19.html", "https://www.fireeye.com/blog/threat-research/2010/07/malware-persistence-windows-registry.html", "http://resources.infosecinstitute.com/common-malware-persistence-mechanisms/", "https://www.fireeye.com/blog/threat-research/2017/05/fin7-shim-databases-persistence.html", "https://www.youtube.com/watch?v=dq2Hv7J9fvk"] maintainers = "Bhavin Patel, Splunk" spec_version = 3 -searches = ["ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Reg exe used to hide files directories via registry keys - Rule", "ESCU - Schtasks used for forcing a reboot - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Hiding Files And Directories With Attrib exe - Rule", "ESCU - Detect Path Interception By Creation Of program exe - Rule", "ESCU - Monitor Registry Keys for Print Monitors - Rule", "ESCU - Registry Keys Used For Persistence - Rule", "ESCU - Registry Keys for Creating SHIM Databases - Rule", "ESCU - Remote Registry Key modifications - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Shim Database Installation With Suspicious Parameters - Rule", "ESCU - Shim Database File Creation - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Monitor for activities and techniques associated with maintaining persistence on a Windows system--a sign that an adversary may have compromised your environment. narrative = Maintaining persistence is one of the first steps taken by attackers after the initial compromise. Attackers leverage various custom and built-in tools to ensure survivability and persistent access within a compromised enterprise. This Analytic Story provides searches to help you identify various behaviors used by attackers to maintain persistent access to a Windows environment. @@ -877,7 +877,7 @@ version = 2 references = ["https://attack.mitre.org/tactics/TA0004/"] maintainers = "David Dorsey, Splunk" spec_version = 3 -searches = ["ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Get Registry Activities", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Investigate Web Activity From Host"] +searches = ["ESCU - Registry Keys Used For Privilege Escalation - Rule", "ESCU - Uncommon Processes On Endpoint - Rule", "ESCU - Overwriting Accessibility Binaries - Rule", "ESCU - Child Processes of Spoolsv exe - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Registry Activities", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Investigate Web Activity From Host", "ESCU - Get Process Info"] description = Monitor for and investigate activities that may be associated with a Windows privilege-escalation attack, including unusual processes running on endpoints, modified registry keys, and more. narrative = Privilege escalation is a "land-and-expand" technique, wherein an adversary gains an initial foothold on a host and then exploits its weaknesses to increase his privileges. The motivation is simple: certain actions on a Windows machine--such as installing software--may require higher-level privileges than those the attacker initially acquired. By increasing his privilege level, the attacker can gain the control required to carry out his malicious ends. This Analytic Story provides searches to detect and investigate behaviors that attackers may use to elevate their privileges in your environment. @@ -888,7 +888,7 @@ version = 3 references = ["https://attack.mitre.org/wiki/Technique/T1050", "https://attack.mitre.org/wiki/Technique/T1031"] maintainers = "Rico Valdez, Splunk" spec_version = 3 -searches = ["ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Get User Information from Identity Table", "ESCU - Get Notable Info", "ESCU - Get Parent Process Info", "ESCU - Get Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Notable History", "ESCU - Get Risk Modifiers For Endpoint"] +searches = ["ESCU - First Time Seen Running Windows Service - Rule", "ESCU - Reg exe Manipulating Windows Services Registry Keys - Rule", "ESCU - Sc exe Manipulating Windows Services - Rule", "ESCU - Get Parent Process Info", "ESCU - Get Risk Modifiers For User", "ESCU - Get User Information from Identity Table", "ESCU - Get Risk Modifiers For Endpoint", "ESCU - Get Notable History", "ESCU - Get Notable Info", "ESCU - Get Authentication Logs For Endpoint", "ESCU - Get Process Info"] description = Windows services are often used by attackers for persistence and the ability to load drivers or otherwise interact with the Windows kernel. This Analytic Story helps you monitor your environment for indications that Windows services are being modified or created in a suspicious manner. narrative = The Windows operating system uses a services architecture to allow for running code in the background, similar to a UNIX daemon. Attackers will often leverage Windows services for persistence, hiding in plain sight, seeking the ability to run privileged code that can interact with the kernel. In many cases, attackers will create a new service to host their malicious code. Attackers have also been observed modifying unnecessary or unused services to point to their own code, as opposed to what was intended. In these cases, attackers often use tools to create or modify services in ways that are not typical for most environments, providing opportunities for detection. @@ -902,7 +902,7 @@ asset_type = confidence = medium explanation = This search looks for AWS provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -annotations = {"analytics_story": ["AWS Suspicious Provisioning Activities"], "asset_type": "AWS Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new city is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your city, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = none @@ -913,7 +913,7 @@ asset_type = confidence = medium explanation = This search looks for AWS provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -annotations = {"analytics_story": ["AWS Suspicious Provisioning Activities"], "asset_type": "AWS Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching over plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new country is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = none @@ -924,7 +924,7 @@ asset_type = confidence = medium explanation = This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -annotations = {"analytics_story": ["AWS Suspicious Provisioning Activities"], "asset_type": "AWS Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = none @@ -935,7 +935,7 @@ asset_type = confidence = medium explanation = This search looks for AWS provisioning activities from previously unseen regions. Region in this context is similar to a state in the United States. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -annotations = {"analytics_story": ["AWS Suspicious Provisioning Activities"], "asset_type": "AWS Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ This search will fire any time a new region is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your region, there should be few false positives. If you are located in regions where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. providing_technologies = none @@ -946,7 +946,7 @@ asset_type = confidence = medium explanation = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search. -annotations = {"analytics_story": ["AWS Cross Account Activity"], "asset_type": "AWS Instance", "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.AC", "PR.DS", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. providing_technologies = none @@ -956,7 +956,7 @@ asset_type = confidence = medium explanation = The search looks for CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS, version 4.4.0 or later, and configure your CloudTrail inputs. -annotations = {"analytics_story": ["AWS Network ACL Activity"], "asset_type": "AWS Instance", "cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that an admin has created this ACL with all ports open for some legitimate purpose however, this should be scoped and not allowed in production environment. providing_technologies = none @@ -966,7 +966,7 @@ asset_type = confidence = medium explanation = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -annotations = {"analytics_story": ["AWS Network ACL Activity"], "asset_type": "AWS Instance", "cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has legitimately deleted a network ACL. providing_technologies = none @@ -976,7 +976,7 @@ asset_type = confidence = medium explanation = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"analytics_story": ["Suspicious AWS EC2 Activities", "AWS Cryptomining"], "asset_type": "AWS Instance", "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. providing_technologies = none @@ -986,7 +986,7 @@ asset_type = confidence = medium explanation = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"analytics_story": ["Suspicious AWS EC2 Activities", "Cloud Cryptomining"], "asset_type": "AWS Instance", "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. providing_technologies = none @@ -996,7 +996,7 @@ asset_type = confidence = medium explanation = This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -annotations = {"analytics_story": ["Suspicious AWS EC2 Activities"], "asset_type": "AWS Instance", "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. providing_technologies = none @@ -1006,7 +1006,7 @@ asset_type = confidence = medium explanation = This search looks for CloudTrail events where a user successfully terminates an abnormally high number of instances. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"analytics_story": ["Suspicious AWS EC2 Activities"], "asset_type": "AWS Instance", "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. providing_technologies = none @@ -1016,7 +1016,7 @@ asset_type = confidence = medium explanation = Detect memory dumping of the LSASS process. how_to_implement = This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 10 for lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Windows", "cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual. providing_technologies = none @@ -1026,7 +1026,7 @@ asset_type = confidence = medium explanation = This search provides detection information on unauthenticated requests against Kubernetes' Pods API how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on forAWS (version 4.4.0 or later), then configure your AWS CloudWatch EKS Logs.Please also customize the `kubernetes_pods_aws_scan_fingerprint_detection` macro to filter out the false positives. -annotations = {"analytics_story": ["Kubernetes Scanning Activity"], "asset_type": "Amazon EKS Kubernetes cluster Pod", "kill_chain_phases": ["Reconnaissance"], "security_domain": "threat"} +annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context. providing_technologies = none @@ -1036,7 +1036,7 @@ asset_type = confidence = medium explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudWatch EKS Logs inputs. -annotations = {"analytics_story": ["Kubernetes Scanning Activity"], "asset_type": "Amazon EKS Kubernetes cluster", "kill_chain_phases": ["Reconnaissance"], "security_domain": "threat"} +annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context. providing_technologies = none @@ -1046,7 +1046,7 @@ asset_type = confidence = medium explanation = Attempt to add a certificate to the untrusted certificate store how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Disabling Security Tools"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack_id": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. providing_technologies = none @@ -1056,7 +1056,7 @@ asset_type = confidence = medium explanation = Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Registry node. You must also be ingesting logs with the fields registry_path, registry_key_name, and registry_value_name from your endpoints. -annotations = {"analytics_story": ["Credential Dumping", "Malicious PowerShell"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack_id": ["T1086", "T1064"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["DE.CM"]} known_false_positives = Administrators may attempt to change the default execution policy on a system for a variety of reasons. However, setting the policy to "unrestricted" or "bypass" as this search is designed to identify, would be unusual. Hits should be reviewed and investigated as appropriate. providing_technologies = none @@ -1066,7 +1066,7 @@ asset_type = confidence = medium explanation = This search looks for attempts to stop security-related services on the endpoint. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., -annotations = {"analytics_story": ["Disabling Security Tools"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack_id": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. providing_technologies = none @@ -1076,7 +1076,7 @@ asset_type = confidence = medium explanation = Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = None identified. providing_technologies = none @@ -1086,7 +1086,7 @@ asset_type = confidence = medium explanation = The search looks for a batch file (.bat) written to the Windows system directory tree. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"analytics_story": ["SamSam Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. providing_technologies = none @@ -1096,7 +1096,7 @@ asset_type = confidence = medium explanation = This search looks for child processes of spoolsv.exe. This activity is associated with a POC privilege-escalation exploit associated with CVE-2018-8440. Spoolsv.exe is the process associated with the Print Spooler service in Windows and typically runs as SYSTEM. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. Update the `children_of_spoolsv_filter` macro to filter out legitimate child processes spawned by spoolsv.exe. -annotations = {"analytics_story": ["Windows Privilege Escalation"], "asset_type": "Endpoint", "cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack_id": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 5", "CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1068"], "nist": ["PR.AC", "PR.PT", "DE.CM"]} known_false_positives = Some legitimate printer-related processes may show up as children of spoolsv.exe. You should confirm that any activity as legitimate and may be added as exclusions in the search. providing_technologies = none @@ -1108,7 +1108,7 @@ explanation = This search allows you to identify the endpoints that have connect how_to_implement = This search requires that DNS data is being ingested and populating the `Network_Resolution` data model. This data can come from DNS logs or from solutions that parse network traffic for this data, such as Splunk Stream or Bro.\ This search produces fields (`dest_count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Distinct DNS Connections, **Field:** dest_count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"analytics_story": ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"], "asset_type": "Endpoint", "cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack_id": ["T1048"], "nist": ["PR.PT", "DE.AE", "PR.DS"], "security_domain": "network"} +annotations = {"cis20": ["CIS 9", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048"], "nist": ["PR.PT", "DE.AE", "PR.DS"]} known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. providing_technologies = none @@ -1118,7 +1118,7 @@ asset_type = confidence = medium explanation = This search looks for cloud compute instances created by users who have not created them before. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users. -annotations = {"analytics_story": ["Cloud Cryptomining"], "asset_type": "Cloud Compute Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a user will start to create compute instances for the first time, for any number of reasons. Verify with the user launching instances that this is the intended behavior. providing_technologies = none @@ -1128,7 +1128,7 @@ asset_type = confidence = medium explanation = This search looks for cloud compute instances being created with previously unseen image IDs. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the "Previously Seen Cloud Compute Images" support search to create a baseline of previously seen images. -annotations = {"analytics_story": ["Cloud Cryptomining"], "asset_type": "Cloud Compute Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = After a new image is created, the first systems created with that image will cause this alert to fire. Verify that the image being used was created by a legitimate user. providing_technologies = none @@ -1138,7 +1138,7 @@ asset_type = confidence = medium explanation = Find EC2 instances being created with previously unseen instance types. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the " Previously Seen Cloud Compute Instance Types" support search to create a baseline of previously seen regions. -annotations = {"analytics_story": ["Cloud Cryptomining"], "asset_type": "Cloud Compute Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type. providing_technologies = none @@ -1148,7 +1148,7 @@ asset_type = confidence = medium explanation = This search looks at cloud-infrastructure events where an instance is created in any region within the last hour and then compares it to a lookup file of previously seen regions where instances have been created. how_to_implement = You must be ingesting the appropriate cloud-infrastructure logs and have the Security Research cloud data model (https://github.com/splunk/cloud-datamodel-security-research/) installed. Run the \"Previously Seen Cloud Compute Instance Types\" support search to create a baseline of previously seen regions. -annotations = {"analytics_story": ["Cloud Cryptomining"], "asset_type": "Cloud Compute Instance", "cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. providing_technologies = none @@ -1162,7 +1162,7 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su 1. \ 1. **Label:** File Extension, **Field:** file_extension\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"analytics_story": ["SamSam Ransomware", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. providing_technologies = none @@ -1172,7 +1172,7 @@ asset_type = confidence = medium explanation = The search looks for files created with names matching those typically used in ransomware notes that tell the victim how to get their data back. how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint Filesystem data-model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"analytics_story": ["SamSam Ransomware", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It's possible that a legitimate file could be created with the same name used by ransomware note files. providing_technologies = none @@ -1182,7 +1182,7 @@ asset_type = confidence = medium explanation = Detect remote thread creation into LSASS consistent with credential dumping. how_to_implement = This search needs Sysmon Logs with a Sysmon configuration, which includes EventCode 8 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Windows", "cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Other tools can access LSASS for legitimate reasons and generate an event. In these cases, tweaking the search may help eliminate noise. providing_technologies = none @@ -1192,7 +1192,7 @@ asset_type = confidence = medium explanation = This search looks for the creation of local administrator accounts using net.exe. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["DHS Report TA18-074A"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1059"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create admin accounts. providing_technologies = none @@ -1202,7 +1202,7 @@ asset_type = confidence = medium explanation = This search looks for the creation or deletion of hidden shares using net.exe. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Hidden Cobra Malware"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. providing_technologies = none @@ -1212,7 +1212,7 @@ asset_type = confidence = medium explanation = Monitor for signs that Ntdsutil, Vssadmin, or Wmic has been used to create a shadow copy. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Legtimate administrator usage of Ntdsutil, Vssadmin, or Wmic will create false positives. providing_technologies = none @@ -1222,7 +1222,7 @@ asset_type = confidence = medium explanation = This search detects the use of wmic and Powershell to create a shadow copy. how_to_implement = You must enable Powershell scriptblock logging in order to detect this attack.This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Legtimate administrator usage of wmic to create a shadow copy. providing_technologies = none @@ -1232,7 +1232,7 @@ asset_type = confidence = medium explanation = This search detects credential dumping using copy command from a shadow copy. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = unknown providing_technologies = none @@ -1242,7 +1242,7 @@ asset_type = confidence = medium explanation = This search detects the creation of a symlink to a shadow copy. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = unknown providing_technologies = none @@ -1258,7 +1258,7 @@ This search produces fields (`query`,`query_length`,`count`) that are not yet su 1. \ 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"analytics_story": ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command and Control"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack_id": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = If you are seeing more results than desired, you may consider reducing the value for threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. providing_technologies = none @@ -1268,7 +1268,7 @@ asset_type = confidence = medium explanation = This search allows you to identify DNS requests and compute the standard deviation on the length of the names being resolved, then filter on two times the standard deviation to show you those queries that are unusually large for your environment. how_to_implement = To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. -annotations = {"analytics_story": ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command and Control"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack_id": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It's possible there can be long domain names that are legitimate. providing_technologies = none @@ -1278,7 +1278,7 @@ asset_type = confidence = medium explanation = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. -annotations = {"analytics_story": ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic", "Host Redirection"], "asset_type": "Endpoint", "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. providing_technologies = none @@ -1292,7 +1292,7 @@ how_to_implement = To successfully implement this search you will need to ensure If Splunk>Phantom is also configured in your environment, a Playbook called "DNS Hijack Enrichment" can be configured to run when any results are found by this detection search. The playbook takes in the DNS record changed and uses Geoip, whois, Censys and PassiveTotal to detect if DNS issuers changed. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.2/playbook/dns-hijack-enrichment/`).\ -annotations = {"analytics_story": ["DNS Hijacking"], "asset_type": "Endpoint", "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = Legitimate DNS changes can be detected in this search. Investigate, verify and update the list of provided current answers for the domains in question as appropriate. providing_technologies = none @@ -1302,7 +1302,7 @@ asset_type = confidence = medium explanation = The vssadmin.exe utility is used to interact with the Volume Shadow Copy Service. Wmic is an interface to the Windows Management Instrumentation. This search looks for either of these tools being used to delete shadow copies. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["SamSam Ransomware", "Windows Log Manipulation", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. providing_technologies = none @@ -1318,7 +1318,7 @@ This search produces fields (`eventName`,`userIdentity.type`,`userIdentity.arn`) 1. \ 1. **Label:** AWS User Type, **Field:** userIdentity.type\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"analytics_story": ["AWS User Monitoring"], "asset_type": "AWS Instance", "cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"], "security_domain": "network"} +annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. providing_technologies = none @@ -1334,7 +1334,7 @@ This search produces fields (`eventName`,`firstTime`,`lastTime`) that are not ye 1. \ 1. **Label:** Last Time, **Field:** lastTime\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"analytics_story": ["AWS User Monitoring"], "asset_type": "AWS Instance", "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"], "security_domain": "access"} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC", "ID.AM"]} known_false_positives = It's likely that you'll find activity detected by users/service accounts that are not listed in the `identity_lookup_expanded` or ` aws_service_accounts.csv` file. If the user is a legitimate service account, update the `aws_service_accounts.csv` table with that entry. providing_technologies = none @@ -1344,7 +1344,7 @@ asset_type = confidence = medium explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"analytics_story": ["Suspicious AWS Login Activities"], "asset_type": "AWS Instance", "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. providing_technologies = none @@ -1354,7 +1354,7 @@ asset_type = confidence = medium explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"analytics_story": ["Suspicious AWS Login Activities"], "asset_type": "AWS Instance", "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. providing_technologies = none @@ -1364,7 +1364,7 @@ asset_type = confidence = medium explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"analytics_story": ["Suspicious AWS Login Activities"], "asset_type": "AWS Instance", "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. providing_technologies = none @@ -1374,7 +1374,7 @@ asset_type = confidence = medium explanation = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows. -annotations = {"analytics_story": ["Lateral Movement"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1075"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "security_domain": "access"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1075"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. providing_technologies = none @@ -1384,7 +1384,7 @@ asset_type = confidence = medium explanation = This search looks for reading lsass memory consistent with credential dumping. how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 10 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Windows", "cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. Other tools can access lsass for legitimate reasons, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. providing_technologies = none @@ -1398,7 +1398,7 @@ how_to_implement = You need to ingest data from your DNS logs in the Network_Res If Splunk>Phantom is also configured in your environment, a Playbook called `Lets Encrypt Domain Investigate` can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook link:`https://my.phantom.us/4.2/playbook/lets-encrypt-domain-investigate/`).\ -annotations = {"analytics_story": ["Common Phishing Frameworks"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack_id": ["T1192"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 8", "CIS 7"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1192"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} known_false_positives = If a known good domain is not listed in the legit_domains.csv file, then the search could give you false postives. Please update that lookup file to filter out DNS requests to legitimate domains. providing_technologies = none @@ -1412,7 +1412,7 @@ how_to_implement = You must ingest your Windows security event logs in the `Chan If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \ (Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\ -annotations = {"analytics_story": ["Account Monitoring and Controls"], "asset_type": "Windows", "cis20": ["CIS 16"], "mitre_attack_id": ["T1078"], "nist": ["PR.IP"], "security_domain": "access"} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["PR.IP"]} known_false_positives = It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts. providing_technologies = none @@ -1422,7 +1422,7 @@ asset_type = confidence = medium explanation = This search detects user accounts that have been locked out a relatively high number of times in a short period. how_to_implement = ou must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. -annotations = {"analytics_story": ["Account Monitoring and Controls"], "asset_type": "Windows", "cis20": ["CIS 16"], "mitre_attack_id": ["T1078"], "nist": ["PR.IP"], "security_domain": "access"} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["PR.IP"]} known_false_positives = It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts. providing_technologies = none @@ -1432,7 +1432,7 @@ asset_type = confidence = medium explanation = This search looks for outbound ICMP packets with a packet size larger than 1,000 bytes. Various threat actors have been known to use ICMP as a command and control channel for their attack infrastructure. Large ICMP packets from an endpoint to a remote host may be indicative of this activity. how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have a good understanding of how your network segments are designed and that you are able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in the `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model -annotations = {"analytics_story": ["Command and Control"], "asset_type": "Endpoint", "cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack_id": ["T1095"], "nist": ["DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1095"], "nist": ["DE.AE"]} known_false_positives = ICMP packets are used in a variety of ways to help troubleshoot networking issues and ensure the proper flow of traffic. As such, it is possible that a large ICMP packet could be perfectly legitimate. If large ICMP packets are associated with command and control traffic, there will typically be a large number of these packets observed over time. If the search is providing a large number of false positives, you can modify the search to adjust the byte threshold or whitelist specific IP addresses, as necessary. providing_technologies = none @@ -1442,7 +1442,7 @@ asset_type = confidence = medium explanation = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. -annotations = {"analytics_story": ["Suspicious DNS Traffic", "Command and Control"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack_id": ["T1043"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. providing_technologies = none @@ -1452,7 +1452,7 @@ asset_type = confidence = medium explanation = This search looks for reading loaded Images unique to credential dumping with Mimikatz. how_to_implement = This search needs Sysmon Logs and a sysmon configuration, which includes EventCode 7 with powershell.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Windows", "cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["DE.AE", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 6", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.AE", "DE.CM"]} known_false_positives = Other tools can import the same DLLs. These tools should be part of a whtelist. providing_technologies = none @@ -1462,7 +1462,7 @@ asset_type = confidence = medium explanation = This search looks for PowerShell requesting privileges consistent with credential dumping. how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. -annotations = {"asset_type": "Windows", "cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"], "security_domain": "access"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. providing_technologies = none @@ -1478,7 +1478,7 @@ This search produces fields (`Security_ID`,`Group_Name`,`Message`) that are not 1. \ 1. **Label:** Message, **Field:** Message\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"analytics_story": ["DHS Report TA18-074A"], "asset_type": "Windows", "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack_id": ["T1078"], "nist": ["PR.AC", "DE.CM"], "security_domain": "access"} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1078"], "nist": ["PR.AC", "DE.CM"]} known_false_positives = The activity may be legitimate. For this reason, it's best to verify the account with an administrator and ask whether there was a valid service request for the account creation. If your local administrator group name is not "Administrators", this search may generate an excessive number of false positives providing_technologies = none @@ -1488,7 +1488,7 @@ asset_type = confidence = medium explanation = The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days. how_to_implement = To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure. -annotations = {"analytics_story": ["Router and Infrastructure Security"], "asset_type": "Endpoint", "cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"], "security_domain": "network"} +annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "PR.AC", "PR.IP"]} known_false_positives = Legitimate router connections may appear as new connections providing_technologies = none @@ -1498,7 +1498,7 @@ asset_type = confidence = medium explanation = This search looks for CloudTrail events where a user has created an open/public S3 bucket. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"analytics_story": ["Suspicious AWS S3 Activities"], "asset_type": "S3 Bucket", "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. providing_technologies = none @@ -1508,7 +1508,7 @@ asset_type = confidence = medium explanation = This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk. how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. -annotations = {"analytics_story": ["Phishing Payloads"], "asset_type": "Endpoint", "cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack_id": ["T1193"], "nist": ["ID.AM", "PR.DS"], "security_domain": "network"} +annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = It is not uncommon for outlook to write legitimate zip files to the disk. providing_technologies = none @@ -1518,7 +1518,7 @@ asset_type = confidence = medium explanation = This search looks for outbound SMB connections made by hosts within your network to the Internet. SMB traffic is used for Windows file-sharing activity. One of the techniques often used by attackers involves retrieving the credential hash using an SMB request made to a compromised server controlled by the threat actor. how_to_implement = In order to run this search effectively, we highly recommend that you leverage the Assets and Identity framework. It is important that you have good understanding of how your network segments are designed, and be able to distinguish internal from external address space. Add a category named `internal` to the CIDRs that host the company's assets in `assets_by_cidr.csv` lookup file, which is located in `$SPLUNK_HOME/etc/apps/SA-IdentityManagement/lookups/`. More information on updating this lookup can be found here: https://docs.splunk.com/Documentation/ES/5.0.0/Admin/Addassetandidentitydata. This search also requires you to be ingesting your network traffic and populating the Network_Traffic data model -annotations = {"analytics_story": ["DHS Report TA18-074A", "Hidden Cobra Malware"], "asset_type": "Endpoint", "cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack_id": ["T1043"], "nist": ["DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. providing_technologies = none @@ -1528,7 +1528,7 @@ asset_type = confidence = medium explanation = The search is looking for the creation of program.exe in the C: drive. The creation of this file in that location may be driven by a motive to perform path interception. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. -annotations = {"analytics_story": ["Windows Persistence Techniques"], "asset_type": "", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is unlikely that a normal user may create and place this file in the C: drive. Confirm with the user. providing_technologies = none @@ -1538,7 +1538,7 @@ asset_type = confidence = medium explanation = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. -annotations = {"analytics_story": ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack_id": ["T1059"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. providing_technologies = none @@ -1548,7 +1548,7 @@ asset_type = confidence = medium explanation = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within the command line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["DHS Report TA18-074A", "SamSam Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1059"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine providing_technologies = none @@ -1558,7 +1558,7 @@ asset_type = confidence = medium explanation = This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts and populating the endpoint data model with the resultant dataset. The macro `filter_rare_process_whitelist` searches two lookup files to whitelist your processes. These consist of `rare_process_whitelist_default.csv` and `rare_process_whitelist_local.csv`. To add your own processes to the whitelist, add them to `rare_process_whitelist_local.csv`. If you wish to remove an entry from the default lookup file, you will have to modify the macro itself to set the whitelist value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment. -annotations = {"analytics_story": ["Emotet Malware DHS Report TA18-201A ", "Unusual Processes"], "asset_type": "Endpoint", "cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 2", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.PT", "PR.DS", "DE.CM"]} known_false_positives = Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_whitelist_local.csv` to filter them out of your search results. providing_technologies = none @@ -1568,7 +1568,7 @@ asset_type = confidence = medium explanation = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. -annotations = {"analytics_story": ["Suspicious AWS S3 Activities"], "asset_type": "S3 Bucket", "cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour providing_technologies = none @@ -1584,7 +1584,7 @@ This search produces fields (`eventName`,`numberOfApiCalls`,`uniqueApisCalled`) 1. \ 1. **Label:** Unique API Calls, **Field:** uniqueApisCalled\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"analytics_story": ["AWS User Monitoring"], "asset_type": "AWS Instance", "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"], "security_domain": "network"} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = providing_technologies = none @@ -1594,7 +1594,7 @@ asset_type = confidence = medium explanation = This search will detect users creating spikes in API activity related to network access-control lists (ACLs)in your AWS environment. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Network ACL Activity by ARN" support search once to create a lookup file of previously seen Network ACL Activity. To add or remove API event names related to network ACLs, edit the macro `network_acl_events`. -annotations = {"analytics_story": ["AWS Network ACL Activity"], "asset_type": "AWS Instance", "cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"], "security_domain": "network"} +annotations = {"cis20": ["CIS 12", "CIS 11"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Please modify this according the your environment. providing_technologies = none @@ -1604,7 +1604,7 @@ asset_type = confidence = medium explanation = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. -annotations = {"analytics_story": ["Suspicious AWS S3 Activities"], "asset_type": "S3 Bucket", "cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"], "security_domain": "network"} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. providing_technologies = none @@ -1614,7 +1614,7 @@ asset_type = confidence = medium explanation = This search will detect users creating spikes in API activity related to security groups in your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike.This search works best when you run the "Baseline of Security Group Activity by ARN" support search once to create a history of previously seen Security Group Activity. To add or remove API event names for security groups, edit the macro `security_group_api_calls`. -annotations = {"analytics_story": ["AWS User Monitoring"], "asset_type": "AWS Instance", "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"], "security_domain": "network"} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. providing_technologies = none @@ -1624,7 +1624,7 @@ asset_type = confidence = medium explanation = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. -annotations = {"analytics_story": ["Suspicious AWS Traffic", "AWS Network ACL Activity", "Command and Control"], "asset_type": "AWS Instance", "cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"], "security_domain": "network"} +annotations = {"cis20": ["CIS 11"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. providing_technologies = none @@ -1634,7 +1634,7 @@ asset_type = confidence = medium explanation = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. -annotations = {"analytics_story": ["Data Protection"], "asset_type": "Endpoint", "cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "nist": ["PR.PT", "PR.DS"]} known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. providing_technologies = none @@ -1644,7 +1644,7 @@ asset_type = confidence = medium explanation = By populating the organization's assets within the assets_by_str.csv, we will be able to detect unauthorized devices that are trying to connect with the organization's network by inspecting DHCP request packets, which are issued by devices when they attempt to obtain an IP address from the DHCP server. The MAC address associated with the source of the DHCP request is checked against the list of known devices, and reports on those that are not found. how_to_implement = This search uses the Network_Sessions data model shipped with Enterprise Security. It leverages the Assets and Identity framework to populate the assets_by_str.csv file located in SA-IdentityManagement, which will contain a list of known authorized organizational assets including their MAC addresses. Ensure that all inventoried systems have their MAC address populated. -annotations = {"analytics_story": ["Asset Tracking"], "asset_type": "Infrastructure", "cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"], "security_domain": "network"} +annotations = {"cis20": ["CIS 1"], "kill_chain_phases": ["Reconnaissance", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This search might be prone to high false positives. Please consider this when conducting analysis or investigations. Authorized devices may be detected as unauthorized. If this is the case, verify the MAC address of the system responsible for the false positive and add it to the Assets and Identity framework with the proper information. providing_technologies = none @@ -1654,7 +1654,7 @@ asset_type = confidence = medium explanation = This search looks for the execution of the cscript.exe or wscript.exe processes, with a parent of cmd.exe. The search will return the count, the first and last time this execution was seen on a machine, the user, and the destination of the machine how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"analytics_story": ["Suspicious Command-Line Executions", "Emotet Malware DHS Report TA18-201A "], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack_id": ["T1059"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications may exhibit this behavior. providing_technologies = none @@ -1664,7 +1664,7 @@ asset_type = confidence = medium explanation = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. -annotations = {"analytics_story": ["JBoss Vulnerability", "SamSam Ransomware"], "asset_type": "Web Server", "kill_chain_phases": ["Reconnaissance"], "mitre_attack_id": ["T1082"], "security_domain": "network"} +annotations = {"kill_chain_phases": ["Reconnaissance"], "mitre_attack": ["T1082"]} known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. providing_technologies = none @@ -1680,7 +1680,7 @@ This search produces fields (query, answer, isDynDNS) that are not yet supported 1. \ 1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"analytics_story": ["Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control", "Data Protection", "Dynamic DNS", "Suspicious DNS Traffic", "DNS Hijacking"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 8", "CIS 12", "CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} known_false_positives = Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. providing_technologies = none @@ -1690,7 +1690,7 @@ asset_type = confidence = medium explanation = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model -annotations = {"analytics_story": ["JBoss Vulnerability", "SamSam Ransomware"], "asset_type": "Web Server", "cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 12", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} known_false_positives = No known false positives for this detection. providing_technologies = none @@ -1700,7 +1700,7 @@ asset_type = confidence = medium explanation = This search looks for the execution of "mshta.exe" with command-line arguments that launch a script. The search will return the first time and last time these command-line arguments were used for these executions, as well as the target system, the user, process "mshta.exe" and its parent process. how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"analytics_story": ["Suspicious MSHTA Activity"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack_id": ["T1059"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Exploitation"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive. providing_technologies = none @@ -1710,7 +1710,7 @@ asset_type = confidence = medium explanation = This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. -annotations = {"analytics_story": ["AWS User Monitoring"], "asset_type": "AWS Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. providing_technologies = none @@ -1720,7 +1720,7 @@ asset_type = confidence = medium explanation = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days. Run "Update previously seen users in CloudTrail" hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. -annotations = {"analytics_story": ["Suspicious AWS Login Activities"], "asset_type": "AWS Instance", "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. providing_technologies = none @@ -1730,7 +1730,7 @@ asset_type = confidence = medium explanation = This search looks for fast execution of processes used for system network configuration discovery on the endpoint. how_to_implement = You must be ingesting data that records registry activity from your hosts to populate the Endpoint data model in the processes node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report reads and writes to the registry or that are populated via Windows event logs, after enabling process tracking in your Windows audit settings. -annotations = {"analytics_story": ["Unusual Processes"], "asset_type": "Endpoint", "cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = It is uncommon for normal users to execute a series of commands used for network discovery. System administrators often use scripts to execute these commands. These can generate false positives. providing_technologies = none @@ -1742,7 +1742,7 @@ explanation = This search looks for web connections to dynamic DNS providers. how_to_implement = This search requires you to be ingesting web-traffic logs. You can obtain these logs from indexing data from a web proxy or by using a network-traffic-analysis tool, such as Bro or Splunk Stream. The web data model must contain the URL being requested, the IP address of the host initiating the request, and the destination IP. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of dynamic DNS providers. Consider periodically updating this local lookup file with new domains.\ This search produces fields (`isDynDNS`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** IsDynamicDNS, **Field:** isDynDNS\ Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"analytics_story": ["Dynamic DNS"], "asset_type": "Endpoint", "cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack_id": ["T1102", "T1041"], "nist": ["PR.IP", "DE.DP"], "security_domain": "network"} +annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1102", "T1041"], "nist": ["PR.IP", "DE.DP"]} known_false_positives = It is possible that list of dynamic DNS providers is outdated and/or that the URL being requested is legitimate. providing_technologies = none @@ -1752,7 +1752,7 @@ asset_type = confidence = medium explanation = This search is used to detect DNS tunneling, by calculating the sum of the length of DNS queries and DNS answers. The search also filters out potential false positives by filtering out queries made to internal systems and the queries originating from internal DNS, Web, and Email servers. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting an unusually large volume of DNS traffic. how_to_implement = To successfully implement this search, we must ensure that DNS data is being ingested and mapped to the appropriate fields in the Network_Resolution data model. Fields like src_category are automatically provided by the Assets and Identity Framework shipped with Splunk Enterprise Security. You will need to ensure you are using the Assets and Identity Framework and populating the src_category field. You will also need to enable the `cim_corporate_web_domain_search()` macro which will essentially filter out the DNS queries made to the corporate web domains to reduce alert fatigue. -annotations = {"analytics_story": ["Data Protection", "Suspicious DNS Traffic", "Command and Control"], "asset_type": "Endpoint", "cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack_id": ["T1043"], "nist": ["PR.PT", "PR.DS"], "security_domain": "network"} +annotations = {"cis20": ["CIS 13"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "PR.DS"]} known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. providing_technologies = none @@ -1762,7 +1762,7 @@ asset_type = confidence = medium explanation = This search looks for specific command-line arguments that may indicate the execution of tools made by Nirsoft, which are legitimate, but may be abused by attackers. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Emotet Malware DHS Report TA18-201A "], "asset_type": "Endpoint", "cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack_id": ["T1072", "T1087"], "nist": ["PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1072", "T1087"], "nist": ["PR.IP"]} known_false_positives = While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose. providing_technologies = none @@ -1772,7 +1772,7 @@ asset_type = confidence = medium explanation = The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC). how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. -annotations = {"analytics_story": ["Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1112"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. providing_technologies = none @@ -1782,7 +1782,7 @@ asset_type = confidence = medium explanation = Detect the usage of comsvcs.dll for dumping the lsass process. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints, to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = None identified. providing_technologies = none @@ -1792,7 +1792,7 @@ asset_type = confidence = medium explanation = This search looks for EC2 instances being modified by users who have not previously modified them. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`. -annotations = {"analytics_story": ["Unusual AWS EC2 Modifications"], "asset_type": "AWS Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. providing_technologies = none @@ -1802,7 +1802,7 @@ asset_type = confidence = medium explanation = This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen AWS Regions" support search only once to create of baseline of previously seen regions. -annotations = {"analytics_story": ["Suspicious AWS EC2 Activities", "AWS Cryptomining"], "asset_type": "AWS Instance", "cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE"]} known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. providing_technologies = none @@ -1812,7 +1812,7 @@ asset_type = confidence = medium explanation = This search looks for EC2 instances being created with previously unseen AMIs. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 AMIs" support search once to create a history of previously seen AMIs. -annotations = {"analytics_story": ["AWS Cryptomining"], "asset_type": "AWS Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = After a new AMI is created, the first systems created with that AMI will cause this alert to fire. Verify that the AMI being used was created by a legitimate user. providing_technologies = none @@ -1822,7 +1822,7 @@ asset_type = confidence = medium explanation = This search looks for EC2 instances being created with previously unseen instance types. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. -annotations = {"analytics_story": ["AWS Cryptomining"], "asset_type": "AWS Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. providing_technologies = none @@ -1832,7 +1832,7 @@ asset_type = confidence = medium explanation = This search looks for EC2 instances being created by users who have not created them before. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Launches By User" support search once to create a history of previously seen ARNs. -annotations = {"analytics_story": ["Suspicious AWS EC2 Activities", "AWS Cryptomining"], "asset_type": "AWS Instance", "cis20": ["CIS 1"], "nist": ["ID.AM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. providing_technologies = none @@ -1844,7 +1844,7 @@ explanation = Attackers often use spaces as a means to obfuscate an attachment's how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. \ **Splunk Phantom Playbook Integration**\ If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/` and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. -annotations = {"analytics_story": ["Suspicious Emails", "Emotet Malware DHS Report TA18-201A "], "asset_type": "Endpoint", "cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"], "security_domain": "network"} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time providing_technologies = none @@ -1854,7 +1854,7 @@ asset_type = confidence = medium explanation = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"analytics_story": ["Collection and Staging"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1114"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114"]} known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. providing_technologies = none @@ -1864,7 +1864,7 @@ asset_type = confidence = medium explanation = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. -annotations = {"analytics_story": ["Collection and Staging"], "asset_type": "Endpoint", "cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1114", "T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1114", "T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. providing_technologies = none @@ -1874,7 +1874,7 @@ asset_type = confidence = medium explanation = This search identifies DNS query failures by counting the number of DNS responses that do not indicate success, and trigger on more than 50 occurrences. how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. -annotations = {"analytics_story": ["Suspicious DNS Traffic", "Command and Control"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack_id": ["T1048", "T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 8", "CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1048", "T1043"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} known_false_positives = It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. providing_technologies = none @@ -1884,7 +1884,7 @@ asset_type = confidence = medium explanation = This search looks for processes launched from files with at least five spaces in the name before the extension. This is typically done to obfuscate the file extension by pushing it outside of the default view. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"analytics_story": ["Windows File Extension and Association Abuse"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. providing_technologies = none @@ -1894,7 +1894,7 @@ asset_type = confidence = medium explanation = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. -annotations = {"analytics_story": ["Windows File Extension and Association Abuse"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = None identified. providing_technologies = none @@ -1904,7 +1904,7 @@ asset_type = confidence = medium explanation = This search returns a list of hosts that have not successfully completed a backup in over a week. how_to_implement = To successfully implement this search you need to first obtain data from your backup solution, either from the backup logs on your hosts, or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your backup solution. Depending on how often you backup your systems, you may want to modify how far in the past to look for a successful backup, other than the default of seven days. -annotations = {"analytics_story": ["Monitor Backup Solution"], "asset_type": "Endpoint", "cis20": ["CIS 10"], "nist": ["PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} known_false_positives = None identified providing_technologies = none @@ -1914,7 +1914,7 @@ asset_type = confidence = medium explanation = The search looks for file writes with extensions consistent with a SamSam ransomware attack. how_to_implement = You must be ingesting data that records file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"analytics_story": ["SamSam Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Because these extensions are not typically used in normal operations, you should investigate all results. providing_technologies = none @@ -1924,7 +1924,7 @@ asset_type = confidence = medium explanation = This search looks for the first time a Windows service is seen running in your environment. how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs in order for this search to execute successfully. The support search, `Previously Seen Running Windows Services`, should be run before this search to create the baseline of known Windows services. Please ensure that the Splunk Add-on for Microsoft Windows is version 5.0.0 or above. -annotations = {"analytics_story": ["Windows Service Abuse", "Orangeworm Attack Group"], "asset_type": "Endpoint", "cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack_id": ["T1050"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 2", "CIS 9"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1050"], "nist": ["ID.AM", "PR.DS", "PR.AC", "DE.AE"]} known_false_positives = A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. providing_technologies = none @@ -1934,7 +1934,7 @@ asset_type = confidence = medium explanation = This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must be ingesting logs with both the process name and command line from your endpoints. The complete process name with command-line arguments are mapped to the "process" field in the Endpoint data model. Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. For the search to do accurate calculation, ensure the search scheduling is the same value as the `relative_time` evaluation function. -annotations = {"analytics_story": ["Suspicious Command-Line Executions", "Hidden Cobra Malware", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "DHS Report TA18-074A"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack_id": ["T1064", "T1059"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1064", "T1059"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. We recommend customizing the `first_time_seen_cmd_line_filter` macro to exclude legitimate parent_process_name providing_technologies = none @@ -1944,7 +1944,7 @@ asset_type = confidence = medium explanation = This search show information on uploaded containers including source user, account, action, bucket name event name, http user agent, message and destination path. how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a subpub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_gcp_detection_filter` macro to filter out the false positives. -annotations = {"analytics_story": ["Container Implantation Monitoring and Investigation"], "asset_type": "GCP GCR Container", "security_domain": "threat"} +annotations = {} known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. GCP GCR registers container upload as a Storage event, this search must be considered under the context of CONTAINER upload creation which automatically generates a bucket entry for destination path. providing_technologies = none @@ -1954,7 +1954,7 @@ asset_type = confidence = medium explanation = This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster how_to_implement = You must install the GCP App for Splunk (version 2.0.0 or later), then configure stackdriver and set a Pub/Sub subscription to be imported to Splunk. You must also install Cloud Infrastructure data model.Customize the macro kubernetes_gcp_scan_fingerprint_attack_detection to filter out FPs. -annotations = {"analytics_story": ["Kubernetes Scanning Activity"], "asset_type": "GCP Kubernetes cluster", "kill_chain_phases": ["Reconnaissance"], "security_domain": "threat"} +annotations = {"kill_chain_phases": ["Reconnaissance"]} known_false_positives = Not all unauthenticated requests are malicious, but frequency, User Agent and source IPs will provide context. providing_technologies = none @@ -1964,7 +1964,7 @@ asset_type = confidence = medium explanation = Attackers leverage an existing Windows binary, attrib.exe, to mark specific as hidden by using specific flags so that the victim does not see the file. The search looks for specific command-line arguments to detect the use of attrib.exe to hide files. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Windows Defense Evasion Tactics", "Windows Persistence Techniques"], "asset_type": "", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. providing_technologies = none @@ -1974,7 +1974,7 @@ asset_type = confidence = medium explanation = This search looks for an increase of data transfers from your email server to your clients. This could be indicative of a malicious actor collecting data using your email server. how_to_implement = This search requires you to be ingesting your network traffic and populating the Network_Traffic data model. Your email servers must be categorized as "email_server" for the search to work, as well. You may need to adjust the deviation_threshold and minimum_data_samples values based on the network traffic in your environment. The "deviation_threshold" field is a multiplying factor to control how much variation you're willing to tolerate. The "minimum_data_samples" field is the minimum number of connections of data samples required for the statistic to be valid. -annotations = {"analytics_story": ["Collection and Staging"], "asset_type": "Endpoint", "cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = The false-positive rate will vary based on how you set the deviation_threshold and data_samples values. Our recommendation is to adjust these values based on your network traffic to and from your email servers. providing_technologies = none @@ -1984,7 +1984,7 @@ asset_type = confidence = medium explanation = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. -annotations = {"analytics_story": ["Account Monitoring and Controls"], "asset_type": "Domain Server", "cis20": ["CIS 16"], "mitre_attack_id": ["T1136"], "nist": ["PR.IP"], "security_domain": "access"} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1136"], "nist": ["PR.IP"]} known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. providing_technologies = none @@ -1994,7 +1994,7 @@ asset_type = confidence = medium explanation = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. -annotations = {"analytics_story": ["DNS Amplification Attacks"], "asset_type": "DNS Servers", "cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.IP"], "security_domain": "network"} +annotations = {"cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.IP"]} known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. providing_technologies = none @@ -2004,7 +2004,7 @@ asset_type = confidence = medium explanation = This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine. how_to_implement = In order to properly run this search, Splunk needs to ingest process data from your osquery deployed agents with the [splunk.conf](https://github.com/splunk/TA-osquery/blob/master/config/splunk.conf) pack enabled. Also the [TA-OSquery](https://github.com/splunk/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the data populate the Endpoint data model. -annotations = {"asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM"], "security_domain": "threat"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM"]} known_false_positives = At this stage, there are no known false positives. During testing, no process events refering the com.apple.loginwindow.plist files were observed during normal operation of re-opening applications on reboot. Therefore, it can be asumed that any occurences of this in the process events would be worth investigating. In the event that the legitimate modification by the system of these files is in fact logged to the process log, then the process_name of that process can be whitelisted. providing_technologies = none @@ -2014,7 +2014,7 @@ asset_type = confidence = medium explanation = This search looks for PowerShell processes started with parameters to modify the execution policy of the run, run in a hidden window, and connect to the Internet. This combination of command-line options is suspicious because it's overriding the default PowerShell execution policy, attempts to hide its activity from the user, and connects to the Internet. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Malicious PowerShell", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack_id": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. providing_technologies = none @@ -2024,7 +2024,7 @@ asset_type = confidence = medium explanation = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Malicious PowerShell"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack_id": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = System administrators may use this option, but it's not common. providing_technologies = none @@ -2034,7 +2034,7 @@ asset_type = confidence = medium explanation = This search looks for PowerShell processes started with parameters used to bypass the local execution policy for scripts. These parameters are often observed in attacks leveraging PowerShell scripts as they override the default PowerShell execution policy. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["DHS Report TA18-074A"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack_id": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = There may be legitimate reasons to bypass the PowerShell execution policy. The PowerShell script being run with this parameter should be validated to ensure that it is legitimate. providing_technologies = none @@ -2044,7 +2044,7 @@ asset_type = confidence = medium explanation = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Malicious PowerShell"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack_id": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. providing_technologies = none @@ -2054,7 +2054,7 @@ asset_type = confidence = medium explanation = This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Malicious PowerShell"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack_id": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 8"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "mitre_attack": ["T1086", "T1064"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} known_false_positives = These characters might be legitimately on the command-line, but it is not common. providing_technologies = none @@ -2064,7 +2064,7 @@ asset_type = confidence = medium explanation = This search looks for DNS requests for faux domains similar to the domains that you want to have monitored for abuse. how_to_implement = You need to ingest data from your DNS logs. Specifically you must ingest the domain that is being queried and the IP of the host originating the request. Ideally, you should also be ingesting the answer to the query and the query type. This approach allows you to also create your own localized passive DNS capability which can aid you in future investigations. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. -annotations = {"analytics_story": ["Brand Monitoring"], "asset_type": "Endpoint", "kill_chain_phases": ["Delivery", "Actions on Objectives"], "security_domain": "network"} +annotations = {"kill_chain_phases": ["Delivery", "Actions on Objectives"]} known_false_positives = None at this time providing_technologies = none @@ -2074,7 +2074,7 @@ asset_type = confidence = medium explanation = This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse. how_to_implement = You need to ingest email header data. Specifically the sender's address (src_user) must be populated. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. -annotations = {"analytics_story": ["Suspicious Emails", "Brand Monitoring"], "asset_type": "Endpoint", "cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"], "security_domain": "network"} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time providing_technologies = none @@ -2084,7 +2084,7 @@ asset_type = confidence = medium explanation = This search looks for registry activity associated with modifications to the registry key `HKLM\SYSTEM\CurrentControlSet\Control\Print\Monitors`. In this scenario, an attacker can load an arbitrary .dll into the print-monitor registry by giving the full path name to the after.dll. The system will execute the .dll with elevated (SYSTEM) permissions and will persist after reboot. how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report registry modifications. -annotations = {"analytics_story": ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"], "asset_type": "Endpoint", "cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.AC"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM", "PR.AC"]} known_false_positives = You will encounter noise from legitimate print-monitor registry entries. providing_technologies = none @@ -2094,7 +2094,7 @@ asset_type = confidence = medium explanation = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. -annotations = {"analytics_story": ["Brand Monitoring"], "asset_type": "Endpoint", "cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"], "security_domain": "network"} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = None at this time providing_technologies = none @@ -2104,7 +2104,7 @@ asset_type = confidence = medium explanation = This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address. how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"analytics_story": ["Suspicious Okta Activity"], "asset_type": "Infrastructure", "cis20": ["CIS 16"], "mitre_attack_id": ["T1078"], "nist": ["DE.CM"], "security_domain": "access"} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro XXXXXXXXXXXXX to raise the threshold or except specific IP adresses from triggering this search. providing_technologies = none @@ -2114,7 +2114,7 @@ asset_type = confidence = medium explanation = This searches show information on uploaded containers including source user, image id, source IP user type, http user agent, region, first time, last time of operation (PutImage). These searches are based on Cloud Infrastructure Data Model. how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You must also install Cloud Infrastructure data model. Please also customize the `container_implant_aws_detection_filter` macro to filter out the false positives. -annotations = {"analytics_story": ["Container Implantation Monitoring and Investigation"], "asset_type": "AWS ECR container", "security_domain": "threat"} +annotations = {} known_false_positives = Uploading container is a normal behavior from developers or users with access to container registry. providing_technologies = none @@ -2124,7 +2124,7 @@ asset_type = confidence = medium explanation = This search looks for Windows endpoints that have not generated an event indicating a successful Windows update in the last 60 days. Windows updates are typically released monthly and applied shortly thereafter. An endpoint that has not successfully applied an update in this time frame indicates the endpoint is not regularly being patched for some reason. how_to_implement = To successfully implement this search, it requires that the 'Update' data model is being populated. This can be accomplished by ingesting Windows events or the Windows Update log via a universal forwarder on the Windows endpoints you wish to monitor. The Windows add-on should be also be installed and configured to properly parse Windows events in Splunk. There may be other data sources which can populate this data model, including vulnerability management systems. -annotations = {"analytics_story": ["Monitor for Updates"], "asset_type": "Endpoint", "cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 18"], "nist": ["PR.PT", "PR.MA"]} known_false_positives = None identified providing_technologies = none @@ -2134,7 +2134,7 @@ asset_type = confidence = medium explanation = Detect Okta user lockout events how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"analytics_story": ["Suspicious Okta Activity"], "asset_type": "Infrastructure", "cis20": ["CIS 16"], "mitre_attack_id": ["T1078"], "nist": ["DE.CM"], "security_domain": "access"} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = None. Account lockouts should be followed up on to determine if the actual user was the one who caused the lockout, or if it was an unauthorized actor. providing_technologies = none @@ -2144,7 +2144,7 @@ asset_type = confidence = medium explanation = Detect failed Okta SSO events how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"analytics_story": ["Suspicious Okta Activity"], "asset_type": "Infrastructure", "cis20": ["CIS 16"], "mitre_attack_id": ["T1078"], "nist": ["DE.CM"], "security_domain": "access"} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = There may be a faulty config preventing legitmate users from accessing apps they should have access to. providing_technologies = none @@ -2154,7 +2154,7 @@ asset_type = confidence = medium explanation = This search detects logins from the same user from different states in a 24 hour period. how_to_implement = This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment. -annotations = {"analytics_story": ["Suspicious Okta Activity"], "asset_type": "Infrastructure", "cis20": ["CIS 16"], "mitre_attack_id": ["T1078"], "nist": ["DE.CM"], "security_domain": "access"} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1078"], "nist": ["DE.CM"]} known_false_positives = Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should *not* be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint. providing_technologies = none @@ -2164,7 +2164,7 @@ asset_type = confidence = medium explanation = This search allows you to look for evidence of exploitation for CVE-2016-4859, the Splunk Open Redirect Vulnerability. how_to_implement = No extra steps needed to implement this search. -annotations = {"analytics_story": ["Splunk Enterprise Vulnerability"], "asset_type": "Splunk Server", "cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} known_false_positives = None identified providing_technologies = none @@ -2174,7 +2174,7 @@ asset_type = confidence = medium explanation = This search looks for ColdRoot events from the osx-attacks osquery pack. how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model -annotations = {"analytics_story": ["ColdRoot MacOS RAT"], "asset_type": "Endpoint", "cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"], "security_domain": "threat"} +annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Installation", "Command and Control"], "nist": ["DE.DP", "DE.CM", "PR.PT"]} known_false_positives = There are no known false positives. providing_technologies = none @@ -2184,7 +2184,7 @@ asset_type = confidence = medium explanation = Microsoft Windows contains accessibility features that can be launched with a key combination before a user has logged in. An adversary can modify or replace these programs so they can get a command prompt or backdoor without logging in to the system. This search looks for modifications to these binaries. how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"analytics_story": ["Windows Privilege Escalation"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1015"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle. providing_technologies = none @@ -2194,7 +2194,7 @@ asset_type = confidence = medium explanation = This search looks for processes launched via WMI. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Suspicious WMI Use"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use wmi to execute commands for legitimate purposes. providing_technologies = none @@ -2204,7 +2204,7 @@ asset_type = confidence = medium explanation = This search looks for processes in an MacOS system that is tapping keyboard events in MacOS, and essentially monitoring all keystrokes made by a user. This is a common technique used by RATs to log keystrokes from a victim, although it can also be used by legitimate processes like Siri to react on human input how_to_implement = In order to properly run this search, Splunk needs to ingest data from your osquery deployed agents with the [osx-attacks.conf](https://github.com/facebook/osquery/blob/experimental/packs/osx-attacks.conf#L599) pack enabled. Also the [TA-OSquery](https://github.com/d1vious/TA-osquery) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the Alerts data model. -annotations = {"analytics_story": ["ColdRoot MacOS RAT"], "asset_type": "Endpoint", "cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP"], "security_domain": "threat"} +annotations = {"cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control"], "nist": ["DE.DP"]} known_false_positives = There might be some false positives as keyboard event taps are used by processes like Siri and Zoom video chat, for some good examples of processes to exclude please see [this](https://github.com/facebook/osquery/pull/5345#issuecomment-454639161) comment. providing_technologies = none @@ -2214,7 +2214,7 @@ asset_type = confidence = medium explanation = This search looks for processes launching netsh.exe to execute various commands via the netsh command-line utility. Netsh.exe is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper .dll when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe that are executing commands via the command line. how_to_implement = To successfully implement this search, you must be ingesting logs with the process name, command-line arguments, and parent processes from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"analytics_story": ["Netsh Abuse"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1059"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It is unusual for netsh.exe to have any child processes in most environments. It makes sense to investigate the child process and verify whether the process spawned is legitimate. providing_technologies = none @@ -2224,7 +2224,7 @@ asset_type = confidence = medium explanation = This search looks for processes launching netsh.exe. Netsh is a command-line scripting utility that allows you to, either locally or remotely, display or modify the network configuration of a computer that is currently running. Netsh can be used as a persistence proxy technique to execute a helper DLL when netsh.exe is executed. In this search, we are looking for processes spawned by netsh.exe and executing commands via the command line. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model -annotations = {"analytics_story": ["DHS Report TA18-074A", "Netsh Abuse", "Disabling Security Tools"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1059", "T1089"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1059", "T1089"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. providing_technologies = none @@ -2234,7 +2234,7 @@ asset_type = confidence = medium explanation = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. -annotations = {"analytics_story": ["Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack_id": ["T1043", "T1048"], "nist": ["DE.AE", "PR.AC"], "security_domain": "network"} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Delivery", "Command and Control"], "mitre_attack": ["T1043", "T1048"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified providing_technologies = none @@ -2244,7 +2244,7 @@ asset_type = confidence = medium explanation = This search looks for applications on the endpoint that you have marked as prohibited. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. This is typically populated via endpoint detection-and-response products, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is usually generated via logs that report process tracking in your Windows audit settings. In addition, you must also have only the `process_name` (not the entire process path) marked as "prohibited" in the Enterprise Security `interesting processes` table. To include the process names marked as "prohibited", which is included with ES Content Updates, run the included search Add Prohibited Processes to Enterprise Security. -annotations = {"analytics_story": ["SamSam Ransomware", "Monitor for Unauthorized Software", "Emotet Malware DHS Report TA18-201A "], "asset_type": "Endpoint", "cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Installation", "Command and Control", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified providing_technologies = none @@ -2254,7 +2254,7 @@ asset_type = confidence = medium explanation = This search looks for network traffic on common ports where a higher layer protocol does not match the port that is being used. For example, this search should identify cases where protocols other than HTTP are running on TCP port 80. This can be used by attackers to circumvent firewall restrictions, or as an attempt to hide malicious communications over ports and protocols that are typically allowed and not well inspected. how_to_implement = Running this search properly requires a technology that can inspect network traffic and identify common protocols. Technologies such as Bro and Palo Alto Networks firewalls are two examples that will identify protocols via inspection, and not just assume a specific protocol based on the transport protocol and ports. -annotations = {"analytics_story": ["Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control"], "asset_type": "Endpoint", "cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack_id": ["T1043"], "nist": ["DE.AE", "PR.AC"], "security_domain": "network"} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE", "PR.AC"]} known_false_positives = None identified providing_technologies = none @@ -2264,7 +2264,7 @@ asset_type = confidence = medium explanation = This search looks for cleartext protocols at risk of leaking credentials. Currently, this consists of legacy protocols such as telnet, POP3, IMAP, and non-anonymous FTP sessions. While some of these protocols can be used over SSL, they typically run on different assigned ports in those cases. how_to_implement = This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model. -annotations = {"analytics_story": ["Use of Cleartext Protocols"], "asset_type": "Endpoint", "cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"], "security_domain": "network"} +annotations = {"cis20": ["CIS 9", "CIS 14"], "kill_chain_phases": ["Reconnaissance", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "PR.AC", "PR.DS"]} known_false_positives = Some networks may use kerberized FTP or telnet servers, however, this is rare. providing_technologies = none @@ -2274,7 +2274,7 @@ asset_type = confidence = medium explanation = The search looks for reg.exe modifying registry keys that define Windows services and their configurations. how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. -annotations = {"analytics_story": ["Windows Service Abuse", "Windows Persistence Techniques"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack_id": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} known_false_positives = It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. providing_technologies = none @@ -2284,7 +2284,7 @@ asset_type = confidence = medium explanation = The search looks for command-line arguments used to hide a file or directory using the reg add command. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics", "Windows Persistence Techniques"], "asset_type": "", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = None at the moment providing_technologies = none @@ -2294,7 +2294,7 @@ asset_type = confidence = medium explanation = The search looks for modifications to registry keys that can be used to launch an application or service at system startup. how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"analytics_story": ["Suspicious Windows Registry Activities", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "DHS Report TA18-074A", "Suspicious MSHTA Activity", "Windows Persistence Techniques", "Emotet Malware DHS Report TA18-201A ", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1103", "T1131"], "nist": ["PR.PT", "DE.CM", "DE.AE"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1103", "T1131"], "nist": ["PR.PT", "DE.CM", "DE.AE"]} known_false_positives = There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. providing_technologies = none @@ -2304,7 +2304,7 @@ asset_type = confidence = medium explanation = This search looks for modifications to registry keys that can be used to elevate privileges. The registry keys under "Image File Execution Options" are used to intercept calls to an executable and can be used to attach malicious binaries to benign system binaries. how_to_implement = To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"analytics_story": ["Suspicious Windows Registry Activities", "Windows Privilege Escalation"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1015"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task. providing_technologies = none @@ -2314,7 +2314,7 @@ asset_type = confidence = medium explanation = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"analytics_story": ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1138"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications providing_technologies = none @@ -2324,7 +2324,7 @@ asset_type = confidence = medium explanation = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. -annotations = {"analytics_story": ["SamSam Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"], "security_domain": "network"} +annotations = {"cis20": ["CIS 12", "CIS 9", "CIS 16"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. providing_technologies = none @@ -2334,7 +2334,7 @@ asset_type = confidence = medium explanation = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search allows for whitelisting both source and destination hosts to remove them from the output of the search so you can focus on the uncommon uses of remote desktop on your network. how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. -annotations = {"analytics_story": ["Hidden Cobra Malware", "SamSam Ransomware", "Lateral Movement"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"], "security_domain": "network"} +annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. providing_technologies = none @@ -2344,7 +2344,7 @@ asset_type = confidence = medium explanation = This search looks for the remote desktop process mstsc.exe running on systems upon which it doesn't typically run. This is accomplished by filtering out all systems that are noted in the `common_rdp_source category` in the Assets and Identity framework. how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. The search requires you to identify systems that do not commonly use remote desktop. You can use the included support search "Identify Systems Using Remote Desktop" to identify these systems. After identifying them, you will need to add the "common_rdp_source" category to that system using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in `SA-IdentityManagement/lookups`. -annotations = {"analytics_story": ["Hidden Cobra Malware", "Lateral Movement"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 9", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1076"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} known_false_positives = Remote Desktop may be used legitimately by users on the network. providing_technologies = none @@ -2354,7 +2354,7 @@ asset_type = confidence = medium explanation = This search looks for wmic.exe being launched with parameters to spawn a process on a remote system. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Suspicious WMI Use", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. providing_technologies = none @@ -2364,7 +2364,7 @@ asset_type = confidence = medium explanation = This search monitors for remote modifications to registry keys. how_to_implement = To successfully implement this search, you must populate the `Endpoint` data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"analytics_story": ["Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics", "Windows Persistence Techniques"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. providing_technologies = none @@ -2374,7 +2374,7 @@ asset_type = confidence = medium explanation = This search looks for wmic.exe being launched with parameters to operate on remote systems. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Suspicious WMI Use"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Administrators may use this legitimately to gather info from remote systems. providing_technologies = none @@ -2384,7 +2384,7 @@ asset_type = confidence = medium explanation = This search looks for DLLs under %AppData% being loaded by rundll32.exe that are calling the exported function at ordinal 2. Calling exported functions by ordinal is not as common as calling by exported name. There was a bug fixed in IDAPro on 2016-08-08 that would not display functions without names. Calling functions by ordinal would overcome the lack of name and make it harder for analyst to reverse engineer. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Unusual Processes"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack_id": ["T1085"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1085"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process providing_technologies = none @@ -2394,7 +2394,7 @@ asset_type = confidence = medium explanation = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. -annotations = {"analytics_story": ["DHS Report TA18-074A", "Hidden Cobra Malware", "Ransomware", "Emotet Malware DHS Report TA18-201A "], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1043"], "nist": ["DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. providing_technologies = none @@ -2407,7 +2407,7 @@ how_to_implement = To successfully implement this search, you will need to ensur This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \ 1. **Label:** Number of events, **Field:** count\ Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details` -annotations = {"analytics_story": ["DHS Report TA18-074A", "Hidden Cobra Malware", "Ransomware", "Emotet Malware DHS Report TA18-201A "], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1043"], "nist": ["DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1043"], "nist": ["DE.CM"]} known_false_positives = If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the `smb_traffic_spike_mltk_filter` macro to filter out false positive results providing_technologies = none @@ -2417,7 +2417,7 @@ asset_type = confidence = medium explanation = This search looks for long URLs that have several SQL commands visible within them. how_to_implement = To successfully implement this search, you need to be monitoring network communications to your web servers or ingesting your HTTP logs and populating the Web data model. You must also identify your web servers in the Enterprise Security assets table. -annotations = {"analytics_story": ["SQL Injection"], "asset_type": "Database Server", "cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack_id": ["T1043"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 4", "CIS 13", "CIS 18"], "kill_chain_phases": ["Delivery"], "mitre_attack": ["T1043"], "nist": ["PR.DS", "ID.RA", "PR.PT", "PR.IP", "DE.CM"]} known_false_positives = It's possible that legitimate traffic will have long URLs or long user agent strings and that common SQL commands may be found within the URL. Please investigate as appropriate. providing_technologies = none @@ -2427,7 +2427,7 @@ asset_type = confidence = medium explanation = The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation. how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"analytics_story": ["SamSam Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Delivery"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = No false positives have been identified. providing_technologies = none @@ -2437,7 +2437,7 @@ asset_type = confidence = medium explanation = This search looks for arguments to sc.exe indicating the creation or modification of a Windows service. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Windows Service Abuse", "Orangeworm Attack Group", "DHS Report TA18-074A", "Windows Persistence Techniques", "Disabling Security Tools"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack_id": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 8"], "kill_chain_phases": ["Installation"], "mitre_attack": ["T1050", "T1031", "T1089"], "nist": ["PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.CM"]} known_false_positives = Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. providing_technologies = none @@ -2447,7 +2447,7 @@ asset_type = confidence = medium explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate a task name associated with the Dragonfly threat actor was created or deleted. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["DHS Report TA18-074A"], "asset_type": "Endpoint", "cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1053"], "nist": ["PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = No known false positives providing_technologies = none @@ -2457,7 +2457,7 @@ asset_type = confidence = medium explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate that task names related to the execution of Bad Rabbit ransomware were created or deleted. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1053"], "nist": ["PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = No known false positives providing_technologies = none @@ -2467,7 +2467,7 @@ asset_type = confidence = medium explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Lateral Movement"], "asset_type": "Endpoint", "cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1053"], "nist": ["PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. providing_technologies = none @@ -2477,7 +2477,7 @@ asset_type = confidence = medium explanation = This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled. how_to_implement = To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"analytics_story": ["Windows Persistence Techniques", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1053"], "nist": ["PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1053"], "nist": ["PR.IP"]} known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. providing_technologies = none @@ -2487,7 +2487,7 @@ asset_type = confidence = medium explanation = This search looks for scripts launched via WMI. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Suspicious WMI Use"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. providing_technologies = none @@ -2497,7 +2497,7 @@ asset_type = confidence = medium explanation = This search looks for shim database files being written to default directories. The sdbinst.exe application is used to install shim database files (.sdb). According to Microsoft, a shim is a small library that transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"analytics_story": ["Windows Persistence Techniques"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1138"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["DE.CM"]} known_false_positives = Because legitimate shim files are created and used all the time, this event, in itself, is not suspicious. However, if there are other correlating events, it may warrant further investigation. providing_technologies = none @@ -2507,7 +2507,7 @@ asset_type = confidence = medium explanation = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Windows Persistence Techniques"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1138"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1138"], "nist": ["DE.CM"]} known_false_positives = None identified providing_technologies = none @@ -2517,7 +2517,7 @@ asset_type = confidence = medium explanation = This search detects accounts that were created and deleted in a short time period. how_to_implement = This search requires you to have enabled your Group Management Audit Logs in your Local Windows Security Policy and be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/ -annotations = {"analytics_story": ["Account Monitoring and Controls"], "asset_type": "Windows", "cis20": ["CIS 16"], "mitre_attack_id": ["T1136"], "nist": ["PR.IP"], "security_domain": "access"} +annotations = {"cis20": ["CIS 16"], "mitre_attack": ["T1136"], "nist": ["PR.IP"]} known_false_positives = It is possible that an administrator created and deleted an account in a short time period. Verifying activity with an administrator is advised. providing_technologies = none @@ -2527,7 +2527,7 @@ asset_type = confidence = medium explanation = This search looks for process names that consist only of a single letter. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["DHS Report TA18-074A"], "asset_type": "Endpoint", "cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["ID.AM", "PR.DS"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process. providing_technologies = none @@ -2537,7 +2537,7 @@ asset_type = confidence = medium explanation = The search is used to detect systems that are still vulnerable to the Spectre and Meltdown vulnerabilities. how_to_implement = The search requires that you are ingesting your vulnerability-scanner data and that it reports the CVE of the vulnerability identified. -annotations = {"analytics_story": ["Spectre And Meltdown Vulnerabilities"], "asset_type": "Endpoint", "cis20": ["CIS 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 4"], "nist": ["ID.RA", "RS.MI", "PR.IP", "DE.CM"]} known_false_positives = It is possible that your vulnerability scanner is not detecting that the patches have been applied. providing_technologies = none @@ -2547,7 +2547,7 @@ asset_type = confidence = medium explanation = The search looks for a sharp increase in the number of files written to a particular host how_to_implement = In order to implement this search, you must populate the Endpoint file-system data model node. This is typically populated via endpoint detection and response products, such as Carbon Black or endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the file system. -annotations = {"analytics_story": ["SamSam Ransomware", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM"]} known_false_positives = It is important to understand that if you happen to install any new applications on your hosts or are copying a large number of files, you can expect to see a large increase of file modifications. providing_technologies = none @@ -2557,7 +2557,7 @@ asset_type = confidence = medium explanation = This search allows you to look for evidence of exploitation for CVE-2018-11409, a Splunk Enterprise Information Disclosure Bug. how_to_implement = The REST endpoint that exposes system information is also necessary for the proper operation of Splunk clustering and instrumentation. Whitelisting your Splunk systems will reduce false positives. -annotations = {"analytics_story": ["Splunk Enterprise Vulnerability CVE-2018-11409"], "asset_type": "Splunk Server", "cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.AC", "PR.IP", "DE.CM"]} known_false_positives = Retrieving server information may be a legitimate API request. Verify that the attempt is a valid request for information. providing_technologies = none @@ -2567,7 +2567,7 @@ asset_type = confidence = medium explanation = This search looks for changes to registry values that control Windows file associations, executed by a process that is not typical for legitimate, routine changes to this area. how_to_implement = To successfully implement this search you need to be ingesting information on registry changes that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Registry` nodes. -annotations = {"analytics_story": ["Windows File Extension and Association Abuse", "Suspicious Windows Registry Activities"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1042"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} known_false_positives = There may be other processes in your environment that users may legitimately use to modify file associations. If this is the case and you are finding false positives, you can modify the search to add those processes as exceptions. providing_technologies = none @@ -2577,7 +2577,7 @@ asset_type = confidence = medium explanation = This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA). how_to_implement = You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance. -annotations = {"analytics_story": ["Suspicious Emails"], "asset_type": "Endpoint", "cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"], "security_domain": "threat"} +annotations = {"cis20": ["CIS 7"], "kill_chain_phases": ["Delivery"], "nist": ["PR.IP"]} known_false_positives = This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and whitelist the URL if you determine that it is a legitimate sender. providing_technologies = none @@ -2589,7 +2589,7 @@ explanation = This search looks for emails that have attachments with suspicious how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. \ **Splunk Phantom Playbook Integration**\ If Splunk Phantom is also configured in your environment, a Playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox. -annotations = {"analytics_story": ["Suspicious Emails", "Emotet Malware DHS Report TA18-201A "], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["DE.AE", "PR.IP"], "security_domain": "network"} +annotations = {"cis20": ["CIS 3", "CIS 7", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["DE.AE", "PR.IP"]} known_false_positives = None identified providing_technologies = none @@ -2599,7 +2599,7 @@ asset_type = confidence = medium explanation = The search looks for files created with names that have been linked to malicious activity. how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or via other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file system reads and writes. In addition, this search leverages an included lookup file that contains the names of the files to watch for, as well as a note to communicate why that file name is being monitored. This lookup file can be edited to add or remove file the file names you want to monitor. -annotations = {"analytics_story": ["Hidden Cobra Malware"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = It's possible for a legitimate file to be created with the same name as one noted in the lookup file. Filenames listed in the lookup file should be unique enough that collisions are rare. Looking at the location of the file and the process responsible for the activity can help determine whether or not the activity is legitimate. providing_technologies = none @@ -2609,7 +2609,7 @@ asset_type = confidence = medium explanation = This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts. how_to_implement = In order to properly run this search, Splunk needs to ingest data from your web-traffic appliances that serve or sit in the path of your Struts application servers. This can be accomplished by indexing data from a web proxy, or by using network traffic-analysis tools, such as Splunk Stream or Bro. -annotations = {"analytics_story": ["Apache Struts Vulnerability"], "asset_type": "Endpoint", "cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "nist": ["DE.AE"], "security_domain": "threat"} +annotations = {"cis20": ["CIS 7", "CIS 12"], "kill_chain_phases": ["Exploitation"], "nist": ["DE.AE"]} known_false_positives = There are no known false positives. providing_technologies = none @@ -2619,7 +2619,7 @@ asset_type = confidence = medium explanation = This search looks for a ``*.lnk` file under `C:\User*` or `*\Local\Temp\*` executing a process. This is common behavior used by various spear phishing tools. how_to_implement = You must be ingesting data that records filesystem and process activity from your hosts to populate the Endpoint data model. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or endpoint data sources, such as Sysmon. -annotations = {"analytics_story": ["Phishing Payloads"], "asset_type": "Endpoint", "cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack_id": ["T1193"], "nist": ["ID.AM", "PR.DS"], "security_domain": "network"} +annotations = {"cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "mitre_attack": ["T1193"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = This detection should yield little or no false positive results. It is uncommon for LNK files to execute process from temporary or user directories. providing_technologies = none @@ -2629,7 +2629,7 @@ asset_type = confidence = medium explanation = This search looks for reg.exe being launched from a command prompt not started by the user. When a user launches cmd.exe, the parent process is usually explorer.exe. This search filters out those instances. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["DHS Report TA18-074A", "Windows Defense Evasion Tactics", "Disabling Security Tools"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1112", "T1089"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1112", "T1089"], "nist": ["DE.CM"]} known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. providing_technologies = none @@ -2639,7 +2639,7 @@ asset_type = confidence = medium explanation = The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, or system event logs. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Windows Log Manipulation", "Ransomware"], "asset_type": "", "cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.PT", "PR.AC", "PR.AT", "DE.AE"]} known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. providing_technologies = none @@ -2649,7 +2649,7 @@ asset_type = confidence = medium explanation = This search detects writes to the 'System Volume Information' folder by something other than the System process. how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"analytics_story": ["Collection and Staging"], "asset_type": "Windows", "cis20": ["CIS 8"], "mitre_attack_id": ["T1074"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]} known_false_positives = It is possible that other utilities or system processes may legitimately write to this folder. Investigate and modify the search to include exceptions as appropriate. providing_technologies = none @@ -2659,7 +2659,7 @@ asset_type = confidence = medium explanation = This search detects writes to the recycle bin by a process other than explorer.exe. how_to_implement = To successfully implement this search you need to be ingesting information on filesystem and process logs responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `Filesystem` nodes. -annotations = {"analytics_story": ["Collection and Staging"], "asset_type": "Windows", "cis20": ["CIS 8"], "mitre_attack_id": ["T1074"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "mitre_attack": ["T1074"], "nist": ["DE.CM"]} known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. providing_technologies = none @@ -2669,7 +2669,7 @@ asset_type = confidence = medium explanation = This search looks for system processes that normally run out of C:\Windows\System32\ or C:\Windows\SysWOW64 that are not run from that location. This can indicate a malicious process that is trying to hide as a legitimate process. how_to_implement = To successfully implement this search you need to ingest details about process execution from your hosts. Specifically, this search requires the process name and the full path to the process executable. -annotations = {"analytics_story": ["Suspicious Command-Line Executions", "Ransomware", "Unusual Processes"], "asset_type": "Endpoint", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1036"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1036"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = None identified providing_technologies = none @@ -2679,7 +2679,7 @@ asset_type = confidence = medium explanation = This search looks for network traffic identified as The Onion Router (TOR), a benign anonymity network which can be abused for a variety of nefarious purposes. how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. -annotations = {"analytics_story": ["Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack_id": ["T1043"], "nist": ["DE.AE"], "security_domain": "network"} +annotations = {"cis20": ["CIS 9", "CIS 12"], "kill_chain_phases": ["Command and Control"], "mitre_attack": ["T1043"], "nist": ["DE.AE"]} known_false_positives = None at this time providing_technologies = none @@ -2689,7 +2689,7 @@ asset_type = confidence = medium explanation = The fsutil.exe application is a legitimate Windows utility used to perform tasks related to the file allocation table (FAT) and NTFS file systems. The update sequence number (USN) change journal provides a log of all changes made to the files on the disk. This search looks for fsutil.exe deleting the USN journal. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Windows Log Manipulation", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 6", "CIS 8", "CIS 10"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.CM", "PR.PT", "DE.AE", "DE.DP", "PR.IP"]} known_false_positives = None identified providing_technologies = none @@ -2699,7 +2699,7 @@ asset_type = confidence = medium explanation = This search looks for applications on the endpoint that you have marked as uncommon. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search uses a lookup file `uncommon_processes_default.csv` to track various features of process names that are usually uncommon in most environments. Please consider updating `uncommon_processes_local.csv` to hunt for processes that are uncommon in your environment. -annotations = {"analytics_story": ["Windows Privilege Escalation", "Unusual Processes"], "asset_type": "Endpoint", "cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1015"], "nist": ["ID.AM", "PR.DS"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 2"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1015"], "nist": ["ID.AM", "PR.DS"]} known_false_positives = None identified providing_technologies = none @@ -2709,7 +2709,7 @@ asset_type = confidence = medium explanation = Attackers often disable security tools to avoid detection. This search looks for the usage of process `fltMC.exe` to unload a Sysmon Driver that will stop sysmon from collecting the data. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. This search is also shipped with `unload_sysmon_filter_driver_filter` macro, update this macro to filter out false positives. -annotations = {"analytics_story": ["Disabling Security Tools"], "asset_type": "", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1089"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1089"], "nist": ["DE.CM"]} known_false_positives = providing_technologies = none @@ -2719,7 +2719,7 @@ asset_type = confidence = medium explanation = This search detects loading of unsigned images by LSASS. how_to_implement = This search needs Sysmon Logs with a sysmon configuration, which includes EventCode 7 with lsass.exe. This search uses an input macro named `sysmon`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Sysmon logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives. -annotations = {"analytics_story": ["Credential Dumping"], "asset_type": "Windows", "cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1003"], "nist": ["DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1003"], "nist": ["DE.CM"]} known_false_positives = Other tools could load images into LSASS for legitimate reason. But enterprise tools should always use signed DLLs. providing_technologies = none @@ -2729,7 +2729,7 @@ asset_type = confidence = medium explanation = This search gives you the hosts where a backup was attempted and then failed. how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. -annotations = {"analytics_story": ["Monitor Backup Solution"], "asset_type": "Endpoint", "cis20": ["CIS 10"], "nist": ["PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} known_false_positives = None identified providing_technologies = none @@ -2739,7 +2739,7 @@ asset_type = confidence = medium explanation = Command lines that are extremely long may be indicative of malicious activity on your hosts. how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships, from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"analytics_story": ["Suspicious Command-Line Executions", "Ransomware", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Unusual Processes"], "asset_type": "", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications start with long command lines. providing_technologies = none @@ -2749,7 +2749,7 @@ asset_type = confidence = medium explanation = Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Machine Learning Toolkit (MLTK) to help identify command lines with lengths that are unusual for a given user. how_to_implement = You must be ingesting endpoint data that monitors command lines and populates the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, MLTK version >= 4.2 must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of Command Line Length - MLTK" must be executed before this detection search, as it builds an ML model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment. -annotations = {"analytics_story": ["Suspicious Command-Line Executions", "Ransomware", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Unusual Processes"], "asset_type": "", "cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.CM"]} known_false_positives = Some legitimate applications use long command lines for installs or updates. You should review identified command lines for legitimacy. You may modify the first part of the search to omit legitimate command lines from consideration. If you are seeing more results than desired, you may consider changing the value of threshold in the search to a smaller value. You should also periodically re-run the support search to re-build the ML model on the latest data. You may get unexpected results if the user identified in the results is not present in the data used to build the associated model. providing_technologies = none @@ -2759,7 +2759,7 @@ asset_type = confidence = medium explanation = This search looks for unusually long strings in the Content-Type http header that the client sends the server. how_to_implement = This particular search leverages data extracted from Stream:HTTP. You must configure the http stream using the Splunk Stream App on your Splunk Stream deployment server to extract the cs_content_type field. -annotations = {"analytics_story": ["Apache Struts Vulnerability"], "asset_type": "Web Server", "cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"], "security_domain": "network"} +annotations = {"cis20": ["CIS 3", "CIS 4", "CIS 18", "CIS 12"], "kill_chain_phases": ["Delivery"], "nist": ["ID.RA", "RS.MI", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} known_false_positives = Very few legitimate Content-Type fields will have a length greater than 100 characters. providing_technologies = none @@ -2769,7 +2769,7 @@ asset_type = confidence = medium explanation = This search looks for the creation of WMI permanent event subscriptions. how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -annotations = {"analytics_story": ["Suspicious WMI Use"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. providing_technologies = none @@ -2779,7 +2779,7 @@ asset_type = confidence = medium explanation = This search looks for the creation of WMI permanent event subscriptions. how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate alerts for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. -annotations = {"analytics_story": ["Suspicious WMI Use"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. providing_technologies = none @@ -2789,7 +2789,7 @@ asset_type = confidence = medium explanation = This search looks for the creation of WMI temporary event subscriptions. how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -annotations = {"analytics_story": ["Suspicious WMI Use"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1047", "T1084"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} known_false_positives = Some software may create WMI temporary event subscriptions for various purposes. The included search contains an exception for two of these that occur by default on Windows 10 systems. You may need to modify the search to create exceptions for other legitimate events. providing_technologies = none @@ -2799,7 +2799,7 @@ asset_type = confidence = medium explanation = This search is used to identify the creation of multiple user accounts using the same email domain name. how_to_implement = We start with a dataset that provides visibility into the email address used for the account creation. In this example, we are narrowing our search down to the single web page that hosts the Magento2 e-commerce platform (via URI) used for account creation, the single http content-type to grab only the user's clicks, and the http field that provides the username (form_data), for performance reasons. After we have the username and email domain, we look for numerous account creations per email domain. Common data sources used for this detection are customized Apache logs or Splunk Stream. -annotations = {"analytics_story": ["Web Fraud Detection"], "asset_type": "Account", "cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1136"], "nist": ["DE.CM", "DE.DP"], "security_domain": "threat"} +annotations = {"cis20": ["CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1136"], "nist": ["DE.CM", "DE.DP"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamolous behavior. This search will need to be customized to fit your environment—improving its fidelity by counting based on something much more specific, such as a device ID that may be present in your dataset. Consideration for whether the large number of registrations are occuring from a first-time seen domain may also be important. Extending the search window to look further back in time, or even calculating the average per hour/day for each email domain to look for an anomalous spikes, will improve this search. You can also use Shannon entropy or Levenshtein Distance (both courtesy of URL Toolbox) to consider the randomness or similarity of the email name or email domain, as the names are often machine-generated. providing_technologies = none @@ -2809,7 +2809,7 @@ asset_type = confidence = medium explanation = This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session. how_to_implement = Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. -annotations = {"analytics_story": ["Web Fraud Detection"], "asset_type": "account", "cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1078"], "nist": ["DE.AE", "DE.CM"], "security_domain": "threat"} +annotations = {"cis20": ["CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1078"], "nist": ["DE.AE", "DE.CM"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosly written detections that simply detect anamoluous behavior. providing_technologies = none @@ -2819,7 +2819,7 @@ asset_type = confidence = medium explanation = This search is used to identify user accounts that share a common password. how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. -annotations = {"analytics_story": ["Web Fraud Detection"], "asset_type": "account", "cis20": ["CIS 16"], "nist": ["DE.DP"], "security_domain": "threat"} +annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. providing_technologies = none @@ -2829,7 +2829,7 @@ asset_type = confidence = medium explanation = This search looks for suspicious processes on all systems labeled as web servers. how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. In addition, web servers will need to be identified in the Assets and Identity Framework of Enterprise Security. -annotations = {"analytics_story": ["Apache Struts Vulnerability"], "asset_type": "Web Server", "cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1082"], "nist": ["PR.IP"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1082"], "nist": ["PR.IP"]} known_false_positives = Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks. providing_technologies = none @@ -2839,7 +2839,7 @@ asset_type = confidence = medium explanation = This search looks for Windows events that indicate one of the Windows event logs has been purged. how_to_implement = To successfully implement this search, you need to be ingesting Windows event logs from your hosts. -annotations = {"analytics_story": ["Windows Log Manipulation", "Ransomware"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack_id": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 5", "CIS 6"], "kill_chain_phases": ["Actions on Objectives"], "mitre_attack": ["T1070"], "nist": ["DE.DP", "PR.IP", "PR.AC", "PR.AT", "DE.AE"]} known_false_positives = It is possible that these logs may be legitimately cleared by Administrators. providing_technologies = none @@ -2849,7 +2849,7 @@ asset_type = confidence = medium explanation = The search looks for modifications to the hosts file on all Windows endpoints across your environment. how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"analytics_story": ["Host Redirection"], "asset_type": "Endpoint", "cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"], "security_domain": "endpoint"} +annotations = {"cis20": ["CIS 3", "CIS 8", "CIS 12"], "kill_chain_phases": ["Command and Control"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} known_false_positives = There may be legitimate reasons for system administrators to add entries to this file. providing_technologies = none