# Splunk Security Content Detections
![security_content](static/logo.png)
=====
All the detections shipped to different Splunk products. Below is a breakdown by kind.

## Cloud
<details>
  <summary>details</summary>



- [AWS Create Policy Version to allow all resources](#aws-create-policy-version-to-allow-all-resources)



- [AWS CreateAccessKey](#aws-createaccesskey)



- [AWS CreateLoginProfile](#aws-createloginprofile)



- [AWS Cross Account Activity From Previously Unseen Account](#aws-cross-account-activity-from-previously-unseen-account)



- [AWS Detect Users creating keys with encrypt policy without MFA](#aws-detect-users-creating-keys-with-encrypt-policy-without-mfa)



- [AWS Detect Users with KMS keys performing encryption S3](#aws-detect-users-with-kms-keys-performing-encryption-s3)



- [AWS ECR Container Scanning Findings High](#aws-ecr-container-scanning-findings-high)



- [AWS ECR Container Scanning Findings Low Informational Unknown](#aws-ecr-container-scanning-findings-low-informational-unknown)



- [AWS ECR Container Scanning Findings Medium](#aws-ecr-container-scanning-findings-medium)



- [AWS Excessive Security Scanning](#aws-excessive-security-scanning)



- [AWS IAM AccessDenied Discovery Events](#aws-iam-accessdenied-discovery-events)



- [AWS IAM Assume Role Policy Brute Force](#aws-iam-assume-role-policy-brute-force)



- [AWS IAM Delete Policy](#aws-iam-delete-policy)



- [AWS IAM Failure Group Deletion](#aws-iam-failure-group-deletion)



- [AWS IAM Successful Group Deletion](#aws-iam-successful-group-deletion)



- [AWS Investigate Security Hub alerts by dest](#aws-investigate-security-hub-alerts-by-dest)



- [AWS Investigate User Activities By ARN](#aws-investigate-user-activities-by-arn)



- [AWS Investigate User Activities By AccessKeyId](#aws-investigate-user-activities-by-accesskeyid)



- [AWS Network ACL Details from ID](#aws-network-acl-details-from-id)



- [AWS Network Access Control List Created with All Open Ports](#aws-network-access-control-list-created-with-all-open-ports)



- [AWS Network Access Control List Deleted](#aws-network-access-control-list-deleted)



- [AWS Network Interface details via resourceId](#aws-network-interface-details-via-resourceid)



- [AWS S3 Bucket details via bucketName](#aws-s3-bucket-details-via-bucketname)



- [AWS SAML Access by Provider User and Principal](#aws-saml-access-by-provider-user-and-principal)



- [AWS SAML Update identity provider](#aws-saml-update-identity-provider)



- [AWS SetDefaultPolicyVersion](#aws-setdefaultpolicyversion)



- [AWS UpdateLoginProfile](#aws-updateloginprofile)



- [Abnormally High Number Of Cloud Infrastructure API Calls](#abnormally-high-number-of-cloud-infrastructure-api-calls)



- [Abnormally High Number Of Cloud Instances Destroyed](#abnormally-high-number-of-cloud-instances-destroyed)



- [Abnormally High Number Of Cloud Instances Launched](#abnormally-high-number-of-cloud-instances-launched)



- [Abnormally High Number Of Cloud Security Group API Calls](#abnormally-high-number-of-cloud-security-group-api-calls)



















- [Amazon EKS Kubernetes Pod scan detection](#amazon-eks-kubernetes-pod-scan-detection)



- [Amazon EKS Kubernetes activity by src ip](#amazon-eks-kubernetes-activity-by-src-ip)



- [Amazon EKS Kubernetes cluster scan detection](#amazon-eks-kubernetes-cluster-scan-detection)



































- [Baseline Of Cloud Infrastructure API Calls Per User](#baseline-of-cloud-infrastructure-api-calls-per-user)



- [Baseline Of Cloud Instances Destroyed](#baseline-of-cloud-instances-destroyed)



- [Baseline Of Cloud Instances Launched](#baseline-of-cloud-instances-launched)



- [Baseline Of Cloud Security Group API Calls Per User](#baseline-of-cloud-security-group-api-calls-per-user)



- [Baseline of API Calls per User ARN](#baseline-of-api-calls-per-user-arn)







- [Baseline of Excessive AWS Instances Launched by User - MLTK](#baseline-of-excessive-aws-instances-launched-by-user---mltk)



- [Baseline of Excessive AWS Instances Terminated by User - MLTK](#baseline-of-excessive-aws-instances-terminated-by-user---mltk)



- [Baseline of Network ACL Activity by ARN](#baseline-of-network-acl-activity-by-arn)



- [Baseline of S3 Bucket deletion activity by ARN](#baseline-of-s3-bucket-deletion-activity-by-arn)





- [Baseline of Security Group Activity by ARN](#baseline-of-security-group-activity-by-arn)



- [Baseline of blocked outbound traffic from AWS](#baseline-of-blocked-outbound-traffic-from-aws)



























- [Cloud API Calls From Previously Unseen User Roles](#cloud-api-calls-from-previously-unseen-user-roles)



- [Cloud Compute Instance Created By Previously Unseen User](#cloud-compute-instance-created-by-previously-unseen-user)



- [Cloud Compute Instance Created In Previously Unused Region](#cloud-compute-instance-created-in-previously-unused-region)



- [Cloud Compute Instance Created With Previously Unseen Image](#cloud-compute-instance-created-with-previously-unseen-image)



- [Cloud Compute Instance Created With Previously Unseen Instance Type](#cloud-compute-instance-created-with-previously-unseen-instance-type)



- [Cloud Instance Modified By Previously Unseen User](#cloud-instance-modified-by-previously-unseen-user)



- [Cloud Provisioning Activity From Previously Unseen City](#cloud-provisioning-activity-from-previously-unseen-city)



- [Cloud Provisioning Activity From Previously Unseen Country](#cloud-provisioning-activity-from-previously-unseen-country)



- [Cloud Provisioning Activity From Previously Unseen IP Address](#cloud-provisioning-activity-from-previously-unseen-ip-address)



- [Cloud Provisioning Activity From Previously Unseen Region](#cloud-provisioning-activity-from-previously-unseen-region)





















- [Create a list of approved AWS service accounts](#create-a-list-of-approved-aws-service-accounts)





























































- [Detect AWS Console Login by New User](#detect-aws-console-login-by-new-user)



- [Detect AWS Console Login by User from New City](#detect-aws-console-login-by-user-from-new-city)



- [Detect AWS Console Login by User from New Country](#detect-aws-console-login-by-user-from-new-country)



- [Detect AWS Console Login by User from New Region](#detect-aws-console-login-by-user-from-new-region)

































- [Detect GCP Storage access from a new IP](#detect-gcp-storage-access-from-a-new-ip)



























- [Detect New Open GCP Storage Buckets](#detect-new-open-gcp-storage-buckets)



- [Detect New Open S3 Buckets over AWS CLI](#detect-new-open-s3-buckets-over-aws-cli)



- [Detect New Open S3 buckets](#detect-new-open-s3-buckets)























































- [Detect S3 access from a new IP](#detect-s3-access-from-a-new-ip)













- [Detect Spike in AWS Security Hub Alerts for EC2 Instance](#detect-spike-in-aws-security-hub-alerts-for-ec2-instance)



- [Detect Spike in AWS Security Hub Alerts for User](#detect-spike-in-aws-security-hub-alerts-for-user)



- [Detect Spike in S3 Bucket deletion](#detect-spike-in-s3-bucket-deletion)



- [Detect Spike in blocked Outbound Traffic from your AWS](#detect-spike-in-blocked-outbound-traffic-from-your-aws)





























- [Detect shared ec2 snapshot](#detect-shared-ec2-snapshot)













































































































- [GCP Detect gcploit framework](#gcp-detect-gcploit-framework)



- [GCP Kubernetes activity by src ip](#gcp-kubernetes-activity-by-src-ip)



- [GCP Kubernetes cluster pod scan detection](#gcp-kubernetes-cluster-pod-scan-detection)





- [Get All AWS Activity From City](#get-all-aws-activity-from-city)



- [Get All AWS Activity From Country](#get-all-aws-activity-from-country)



- [Get All AWS Activity From IP Address](#get-all-aws-activity-from-ip-address)



- [Get All AWS Activity From Region](#get-all-aws-activity-from-region)











- [Get EC2 Instance Details by instanceId](#get-ec2-instance-details-by-instanceid)



- [Get EC2 Launch Details](#get-ec2-launch-details)









































- [High Number of Login Failures from a single source](#high-number-of-login-failures-from-a-single-source)







































- [Investigate AWS User Activities by user field](#investigate-aws-user-activities-by-user-field)



- [Investigate AWS activities via region name](#investigate-aws-activities-via-region-name)







- [Investigate Okta Activity by IP Address](#investigate-okta-activity-by-ip-address)



- [Investigate Okta Activity by app](#investigate-okta-activity-by-app)













- [Investigate User Activities In Okta](#investigate-user-activities-in-okta)









- [Kubernetes AWS detect suspicious kubectl calls](#kubernetes-aws-detect-suspicious-kubectl-calls)

































































- [New container uploaded to AWS ECR](#new-container-uploaded-to-aws-ecr)









- [O365 Add App Role Assignment Grant User](#o365-add-app-role-assignment-grant-user)



- [O365 Added Service Principal](#o365-added-service-principal)



- [O365 Bypass MFA via Trusted IP](#o365-bypass-mfa-via-trusted-ip)



- [O365 Disable MFA](#o365-disable-mfa)



- [O365 Excessive Authentication Failures Alert](#o365-excessive-authentication-failures-alert)



- [O365 Excessive SSO logon errors](#o365-excessive-sso-logon-errors)



- [O365 New Federated Domain Added](#o365-new-federated-domain-added)



- [O365 PST export alert](#o365-pst-export-alert)



- [O365 Suspicious Admin Email Forwarding](#o365-suspicious-admin-email-forwarding)



- [O365 Suspicious Rights Delegation](#o365-suspicious-rights-delegation)



- [O365 Suspicious User Email Forwarding](#o365-suspicious-user-email-forwarding)































































- [Previously Seen AWS Cross Account Activity](#previously-seen-aws-cross-account-activity)



- [Previously Seen AWS Cross Account Activity - Initial](#previously-seen-aws-cross-account-activity---initial)



- [Previously Seen AWS Cross Account Activity - Update](#previously-seen-aws-cross-account-activity---update)



- [Previously Seen AWS Provisioning Activity Sources](#previously-seen-aws-provisioning-activity-sources)



- [Previously Seen AWS Regions](#previously-seen-aws-regions)



- [Previously Seen Cloud API Calls Per User Role - Initial](#previously-seen-cloud-api-calls-per-user-role---initial)



- [Previously Seen Cloud API Calls Per User Role - Update](#previously-seen-cloud-api-calls-per-user-role---update)



- [Previously Seen Cloud Compute Creations By User - Initial](#previously-seen-cloud-compute-creations-by-user---initial)



- [Previously Seen Cloud Compute Creations By User - Update](#previously-seen-cloud-compute-creations-by-user---update)



- [Previously Seen Cloud Compute Images - Initial](#previously-seen-cloud-compute-images---initial)



- [Previously Seen Cloud Compute Images - Update](#previously-seen-cloud-compute-images---update)



- [Previously Seen Cloud Compute Instance Types - Initial](#previously-seen-cloud-compute-instance-types---initial)



- [Previously Seen Cloud Compute Instance Types - Update](#previously-seen-cloud-compute-instance-types---update)



- [Previously Seen Cloud Instance Modifications By User - Initial](#previously-seen-cloud-instance-modifications-by-user---initial)



- [Previously Seen Cloud Instance Modifications By User - Update](#previously-seen-cloud-instance-modifications-by-user---update)



- [Previously Seen Cloud Provisioning Activity Sources - Initial](#previously-seen-cloud-provisioning-activity-sources---initial)



- [Previously Seen Cloud Provisioning Activity Sources - Update](#previously-seen-cloud-provisioning-activity-sources---update)



- [Previously Seen Cloud Regions - Initial](#previously-seen-cloud-regions---initial)



- [Previously Seen Cloud Regions - Update](#previously-seen-cloud-regions---update)



- [Previously Seen EC2 AMIs](#previously-seen-ec2-amis)



- [Previously Seen EC2 Instance Types](#previously-seen-ec2-instance-types)



- [Previously Seen EC2 Launches By User](#previously-seen-ec2-launches-by-user)



- [Previously Seen EC2 Modifications By User](#previously-seen-ec2-modifications-by-user)







- [Previously Seen Users In CloudTrail - Update](#previously-seen-users-in-cloudtrail---update)



- [Previously Seen Users in CloudTrail - Initial](#previously-seen-users-in-cloudtrail---initial)







- [Previously seen API call per user roles in CloudTrail](#previously-seen-api-call-per-user-roles-in-cloudtrail)



- [Previously seen S3 bucket access by remote IP](#previously-seen-s3-bucket-access-by-remote-ip)





- [Previously seen users in CloudTrail](#previously-seen-users-in-cloudtrail)





















































































































































































































































































- [Update previously seen users in CloudTrail](#update-previously-seen-users-in-cloudtrail)



























































- [aws detect attach to role policy](#aws-detect-attach-to-role-policy)



- [aws detect permanent key creation](#aws-detect-permanent-key-creation)



- [aws detect role creation](#aws-detect-role-creation)



- [aws detect sts assume role abuse](#aws-detect-sts-assume-role-abuse)



- [aws detect sts get session token abuse](#aws-detect-sts-get-session-token-abuse)


</details>

## Endpoint
<details>
  <summary>details</summary>

































































- [Access LSASS Memory for Dump Creation](#access-lsass-memory-for-dump-creation)



- [Account Discovery With Net App](#account-discovery-with-net-app)



- [All backup logs for host](#all-backup-logs-for-host)



- [Allow File And Printing Sharing In Firewall](#allow-file-and-printing-sharing-in-firewall)



- [Allow Inbound Traffic By Firewall Rule Registry](#allow-inbound-traffic-by-firewall-rule-registry)



- [Allow Inbound Traffic In Firewall Rule](#allow-inbound-traffic-in-firewall-rule)



- [Allow Network Discovery In Firewall](#allow-network-discovery-in-firewall)



- [Allow Operation with Consent Admin](#allow-operation-with-consent-admin)









- [Anomalous usage of 7zip](#anomalous-usage-of-7zip)



- [Any Powershell DownloadFile](#any-powershell-downloadfile)



- [Any Powershell DownloadString](#any-powershell-downloadstring)



- [Applying Stolen Credentials via Mimikatz modules](#applying-stolen-credentials-via-mimikatz-modules)



- [Applying Stolen Credentials via PowerSploit modules](#applying-stolen-credentials-via-powersploit-modules)



- [Assessment of Credential Strength via DSInternals modules](#assessment-of-credential-strength-via-dsinternals-modules)



- [Attacker Tools On Endpoint](#attacker-tools-on-endpoint)



- [Attempt To Add Certificate To Untrusted Store](#attempt-to-add-certificate-to-untrusted-store)



- [Attempt To Disable Services](#attempt-to-disable-services)



- [Attempt To Stop Security Service](#attempt-to-stop-security-service)



- [Attempt To delete Services](#attempt-to-delete-services)



- [Attempted Credential Dump From Registry via Reg exe](#attempted-credential-dump-from-registry-via-reg-exe)



- [Attempted Credential Dump From Registry via Reg exe](#attempted-credential-dump-from-registry-via-reg-exe)



- [BCDEdit Failure Recovery Modification](#bcdedit-failure-recovery-modification)



- [BITS Job Persistence](#bits-job-persistence)



- [BITSAdmin Download File](#bitsadmin-download-file)













- [Baseline of Command Line Length - MLTK](#baseline-of-command-line-length---mltk)



















- [Batch File Write to System32](#batch-file-write-to-system32)



- [CHCP Command Execution](#chcp-command-execution)



- [CMD Echo Pipe - Escalation](#cmd-echo-pipe---escalation)



- [CMLUA Or CMSTPLUA UAC Bypass](#cmlua-or-cmstplua-uac-bypass)



- [CertUtil Download With URLCache and Split Arguments](#certutil-download-with-urlcache-and-split-arguments)



- [CertUtil Download With VerifyCtl and Split Arguments](#certutil-download-with-verifyctl-and-split-arguments)



- [CertUtil With Decode Argument](#certutil-with-decode-argument)



- [Certutil exe certificate extraction](#certutil-exe-certificate-extraction)



- [Child Processes of Spoolsv exe](#child-processes-of-spoolsv-exe)



- [Clear Unallocated Sector Using Cipher App](#clear-unallocated-sector-using-cipher-app)



- [Clop Common Exec Parameter](#clop-common-exec-parameter)



- [Clop Ransomware Known Service Name](#clop-ransomware-known-service-name)























- [Cobalt Strike Named Pipes](#cobalt-strike-named-pipes)



- [Common Ransomware Extensions](#common-ransomware-extensions)



- [Common Ransomware Notes](#common-ransomware-notes)



- [Conti Common Exec parameter](#conti-common-exec-parameter)





- [Count of assets by category](#count-of-assets-by-category)



- [Create Remote Thread In Shell Application](#create-remote-thread-in-shell-application)



- [Create Remote Thread into LSASS](#create-remote-thread-into-lsass)



- [Create Service In Suspicious File Path](#create-service-in-suspicious-file-path)





- [Create local admin accounts using net exe](#create-local-admin-accounts-using-net-exe)



- [Create or delete windows shares using net exe](#create-or-delete-windows-shares-using-net-exe)



- [Creation of Shadow Copy](#creation-of-shadow-copy)



- [Creation of Shadow Copy with wmic and powershell](#creation-of-shadow-copy-with-wmic-and-powershell)



- [Creation of lsass Dump with Taskmgr](#creation-of-lsass-dump-with-taskmgr)



- [Credential Dumping via Copy Command from Shadow Copy](#credential-dumping-via-copy-command-from-shadow-copy)



- [Credential Dumping via Symlink to Shadow Copy](#credential-dumping-via-symlink-to-shadow-copy)



- [Credential Extraction indicative of FGDump and CacheDump with s option](#credential-extraction-indicative-of-fgdump-and-cachedump-with-s-option)



- [Credential Extraction indicative of FGDump and CacheDump with v option](#credential-extraction-indicative-of-fgdump-and-cachedump-with-v-option)



- [Credential Extraction indicative of Lazagne command line options](#credential-extraction-indicative-of-lazagne-command-line-options)



- [Credential Extraction indicative of use of DSInternals credential conversion modules](#credential-extraction-indicative-of-use-of-dsinternals-credential-conversion-modules)



- [Credential Extraction indicative of use of DSInternals modules](#credential-extraction-indicative-of-use-of-dsinternals-modules)



- [Credential Extraction indicative of use of Mimikatz modules](#credential-extraction-indicative-of-use-of-mimikatz-modules)



- [Credential Extraction indicative of use of PowerSploit modules](#credential-extraction-indicative-of-use-of-powersploit-modules)



- [Credential Extraction native Microsoft debuggers peek into the kernel](#credential-extraction-native-microsoft-debuggers-peek-into-the-kernel)



- [Credential Extraction native Microsoft debuggers via z command line option](#credential-extraction-native-microsoft-debuggers-via-z-command-line-option)



- [Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals](#credential-extraction-via-get-addbaccount-module-present-in-powersploit-and-dsinternals)



- [DLLHost with no Command Line Arguments with Network](#dllhost-with-no-command-line-arguments-with-network)



- [DNS Exfiltration Using Nslookup App](#dns-exfiltration-using-nslookup-app)









- [DSQuery Domain Discovery](#dsquery-domain-discovery)



- [Delete A Net User](#delete-a-net-user)



- [Delete ShadowCopy With PowerShell](#delete-shadowcopy-with-powershell)



- [Deleting Of Net Users](#deleting-of-net-users)



- [Deleting Shadow Copies](#deleting-shadow-copies)



- [Deny Permission using Cacls Utility](#deny-permission-using-cacls-utility)













- [Detect Activity Related to Pass the Hash Attacks](#detect-activity-related-to-pass-the-hash-attacks)



- [Detect AzureHound Command-Line Arguments](#detect-azurehound-command-line-arguments)



- [Detect AzureHound File Modifications](#detect-azurehound-file-modifications)



- [Detect Baron Samedit CVE-2021-3156](#detect-baron-samedit-cve-2021-3156)



- [Detect Baron Samedit CVE-2021-3156 Segfault](#detect-baron-samedit-cve-2021-3156-segfault)



- [Detect Baron Samedit CVE-2021-3156 via OSQuery](#detect-baron-samedit-cve-2021-3156-via-osquery)



- [Detect Computer Changed with Anonymous Account](#detect-computer-changed-with-anonymous-account)



- [Detect Copy of ShadowCopy with Script Block Logging](#detect-copy-of-shadowcopy-with-script-block-logging)



- [Detect Credential Dumping through LSASS access](#detect-credential-dumping-through-lsass-access)



- [Detect Dump LSASS Memory using comsvcs](#detect-dump-lsass-memory-using-comsvcs)



- [Detect Empire with PowerShell Script Block Logging](#detect-empire-with-powershell-script-block-logging)



- [Detect Excessive Account Lockouts From Endpoint](#detect-excessive-account-lockouts-from-endpoint)



- [Detect Excessive User Account Lockouts](#detect-excessive-user-account-lockouts)



- [Detect Exchange Web Shell](#detect-exchange-web-shell)







- [Detect HTML Help Renamed](#detect-html-help-renamed)



- [Detect HTML Help Spawn Child Process](#detect-html-help-spawn-child-process)



- [Detect HTML Help URL in Command Line](#detect-html-help-url-in-command-line)



- [Detect HTML Help Using InfoTech Storage Handlers](#detect-html-help-using-infotech-storage-handlers)





- [Detect Kerberoasting](#detect-kerberoasting)





- [Detect MSHTA Url in Command Line](#detect-mshta-url-in-command-line)



- [Detect Mimikatz Using Loaded Images](#detect-mimikatz-using-loaded-images)



- [Detect Mimikatz With PowerShell Script Block Logging](#detect-mimikatz-with-powershell-script-block-logging)



- [Detect New Local Admin account](#detect-new-local-admin-account)













- [Detect Outlook exe writing a zip file](#detect-outlook-exe-writing-a-zip-file)



- [Detect Pass the Hash](#detect-pass-the-hash)



- [Detect Path Interception By Creation Of program exe](#detect-path-interception-by-creation-of-program-exe)





- [Detect Prohibited Applications Spawning cmd exe](#detect-prohibited-applications-spawning-cmd-exe)



- [Detect Prohibited Applications Spawning cmd exe](#detect-prohibited-applications-spawning-cmd-exe)



- [Detect PsExec With accepteula Flag](#detect-psexec-with-accepteula-flag)



- [Detect RClone Command-Line Usage](#detect-rclone-command-line-usage)



- [Detect Rare Executables](#detect-rare-executables)



- [Detect Regasm Spawning a Process](#detect-regasm-spawning-a-process)



- [Detect Regasm with Network Connection](#detect-regasm-with-network-connection)



- [Detect Regasm with no Command Line Arguments](#detect-regasm-with-no-command-line-arguments)



- [Detect Regsvcs Spawning a Process](#detect-regsvcs-spawning-a-process)



- [Detect Regsvcs with Network Connection](#detect-regsvcs-with-network-connection)



- [Detect Regsvcs with No Command Line Arguments](#detect-regsvcs-with-no-command-line-arguments)



- [Detect Regsvr32 Application Control Bypass](#detect-regsvr32-application-control-bypass)



- [Detect Renamed 7-Zip](#detect-renamed-7-zip)



- [Detect Renamed PSExec](#detect-renamed-psexec)



- [Detect Renamed RClone](#detect-renamed-rclone)



- [Detect Renamed WinRAR](#detect-renamed-winrar)





- [Detect Rundll32 Application Control Bypass - advpack](#detect-rundll32-application-control-bypass---advpack)



- [Detect Rundll32 Application Control Bypass - setupapi](#detect-rundll32-application-control-bypass---setupapi)



- [Detect Rundll32 Application Control Bypass - syssetup](#detect-rundll32-application-control-bypass---syssetup)



- [Detect Rundll32 Inline HTA Execution](#detect-rundll32-inline-hta-execution)







- [Detect SharpHound Command-Line Arguments](#detect-sharphound-command-line-arguments)



- [Detect SharpHound File Modifications](#detect-sharphound-file-modifications)



- [Detect SharpHound Usage](#detect-sharphound-usage)

















- [Detect Use of cmd exe to Launch Script Interpreters](#detect-use-of-cmd-exe-to-launch-script-interpreters)



- [Detect WMI Event Subscription Persistence](#detect-wmi-event-subscription-persistence)















- [Detect mshta inline hta execution](#detect-mshta-inline-hta-execution)



- [Detect mshta renamed](#detect-mshta-renamed)



- [Detect processes used for System Network Configuration Discovery](#detect-processes-used-for-system-network-configuration-discovery)





- [Detection of tools built by NirSoft](#detection-of-tools-built-by-nirsoft)



- [Disable AMSI Through Registry](#disable-amsi-through-registry)



- [Disable ETW Through Registry](#disable-etw-through-registry)



- [Disable Logs Using WevtUtil](#disable-logs-using-wevtutil)



- [Disable Net User Account](#disable-net-user-account)



- [Disable Registry Tool](#disable-registry-tool)



- [Disable Show Hidden Files](#disable-show-hidden-files)



- [Disable Windows App Hotkeys](#disable-windows-app-hotkeys)



- [Disable Windows Behavior Monitoring](#disable-windows-behavior-monitoring)



- [Disable Windows SmartScreen Protection](#disable-windows-smartscreen-protection)



- [Disabling CMD Application](#disabling-cmd-application)



- [Disabling ControlPanel](#disabling-controlpanel)



- [Disabling Firewall with Netsh](#disabling-firewall-with-netsh)



- [Disabling FolderOptions Windows Feature](#disabling-folderoptions-windows-feature)



- [Disabling Net User Account](#disabling-net-user-account)



- [Disabling NoRun Windows App](#disabling-norun-windows-app)



- [Disabling Remote User Account Control](#disabling-remote-user-account-control)



- [Disabling SystemRestore In Registry](#disabling-systemrestore-in-registry)



- [Disabling Task Manager](#disabling-task-manager)





- [Download Files Using Telegram](#download-files-using-telegram)



- [Drop IcedID License dat](#drop-icedid-license-dat)



- [Dump LSASS via comsvcs DLL](#dump-lsass-via-comsvcs-dll)



- [Dump LSASS via procdump](#dump-lsass-via-procdump)



- [Dump LSASS via procdump Rename](#dump-lsass-via-procdump-rename)









- [Enable RDP In Other Port Number](#enable-rdp-in-other-port-number)



- [Enumerate Users Local Group Using Telegram](#enumerate-users-local-group-using-telegram)



- [Eventvwr UAC Bypass](#eventvwr-uac-bypass)



- [Excel Spawning PowerShell](#excel-spawning-powershell)



- [Excel Spawning Windows Script Host](#excel-spawning-windows-script-host)



- [Excessive Attempt To Disable Services](#excessive-attempt-to-disable-services)





- [Excessive Service Stop Attempt](#excessive-service-stop-attempt)



- [Excessive Usage Of Cacls App](#excessive-usage-of-cacls-app)



- [Excessive Usage Of Net App](#excessive-usage-of-net-app)



- [Excessive Usage Of SC Service Utility](#excessive-usage-of-sc-service-utility)



- [Excessive Usage Of Taskkill](#excessive-usage-of-taskkill)



- [Excessive Usage of NSLOOKUP App](#excessive-usage-of-nslookup-app)



- [Excessive number of distinct processes created in Windows Temp folder](#excessive-number-of-distinct-processes-created-in-windows-temp-folder)



- [Excessive number of service control start as disabled](#excessive-number-of-service-control-start-as-disabled)



- [Excessive number of taskhost processes](#excessive-number-of-taskhost-processes)



- [Executables Or Script Creation In Suspicious Path](#executables-or-script-creation-in-suspicious-path)



- [Execute Javascript With Jscript COM CLSID](#execute-javascript-with-jscript-com-clsid)



- [Execution of File with Multiple Extensions](#execution-of-file-with-multiple-extensions)



- [Extract SAM from Registry](#extract-sam-from-registry)



- [File with Samsam Extension](#file-with-samsam-extension)



- [First Time Seen Child Process of Zoom](#first-time-seen-child-process-of-zoom)



- [First Time Seen Running Windows Service](#first-time-seen-running-windows-service)



- [First time seen command line argument](#first-time-seen-command-line-argument)



- [FodHelper UAC Bypass](#fodhelper-uac-bypass)









- [GPUpdate with no Command Line Arguments with Network](#gpupdate-with-no-command-line-arguments-with-network)











- [Get Backup Logs For Endpoint](#get-backup-logs-for-endpoint)





















- [Get Logon Rights Modifications For Endpoint](#get-logon-rights-modifications-for-endpoint)



- [Get Logon Rights Modifications For User](#get-logon-rights-modifications-for-user)



- [Get Notable History](#get-notable-history)





- [Get Parent Process Info](#get-parent-process-info)



- [Get Process File Activity](#get-process-file-activity)



- [Get Process Info](#get-process-info)



- [Get Process Information For Port Activity](#get-process-information-for-port-activity)



- [Get Process Responsible For The DNS Traffic](#get-process-responsible-for-the-dns-traffic)



- [Get Sysmon WMI Activity for Host](#get-sysmon-wmi-activity-for-host)





- [Grant Permission Using Cacls Utility](#grant-permission-using-cacls-utility)



- [Hide User Account From Sign-In Screen](#hide-user-account-from-sign-in-screen)



- [Hiding Files And Directories With Attrib exe](#hiding-files-and-directories-with-attrib-exe)



- [High File Deletion Frequency](#high-file-deletion-frequency)





- [High Process Termination Frequency](#high-process-termination-frequency)





- [ICACLS Grant Command](#icacls-grant-command)



- [Icacls Deny Command](#icacls-deny-command)



- [IcedID Exfiltrated Archived File Creation](#icedid-exfiltrated-archived-file-creation)







- [Identify Systems Using Remote Desktop](#identify-systems-using-remote-desktop)



- [Illegal Access To User Content via PowerSploit modules](#illegal-access-to-user-content-via-powersploit-modules)



- [Illegal Account Creation via PowerSploit modules](#illegal-account-creation-via-powersploit-modules)



- [Illegal Deletion of Logs via Mimikatz modules](#illegal-deletion-of-logs-via-mimikatz-modules)



- [Illegal Enabling or Disabling of Accounts via DSInternals modules](#illegal-enabling-or-disabling-of-accounts-via-dsinternals-modules)



- [Illegal Management of Active Directory Elements and Policies via DSInternals modules](#illegal-management-of-active-directory-elements-and-policies-via-dsinternals-modules)



- [Illegal Management of Computers and Active Directory Elements via PowerSploit modules](#illegal-management-of-computers-and-active-directory-elements-via-powersploit-modules)



- [Illegal Privilege Elevation and Persistence via PowerSploit modules](#illegal-privilege-elevation-and-persistence-via-powersploit-modules)



- [Illegal Privilege Elevation via Mimikatz modules](#illegal-privilege-elevation-via-mimikatz-modules)



- [Illegal Service and Process Control via Mimikatz modules](#illegal-service-and-process-control-via-mimikatz-modules)



- [Illegal Service and Process Control via PowerSploit modules](#illegal-service-and-process-control-via-powersploit-modules)







- [Investigate Failed Logins for Multiple Destinations](#investigate-failed-logins-for-multiple-destinations)









- [Investigate Pass the Hash Attempts](#investigate-pass-the-hash-attempts)



- [Investigate Pass the Ticket Attempts](#investigate-pass-the-ticket-attempts)



- [Investigate Previous Unseen User](#investigate-previous-unseen-user)



- [Investigate Successful Remote Desktop Authentications](#investigate-successful-remote-desktop-authentications)









- [Kerberoasting spn request with RC4 encryption](#kerberoasting-spn-request-with-rc4-encryption)



- [Known Services Killed by Ransomware](#known-services-killed-by-ransomware)







- [MacOS - Re-opened Applications](#macos---re-opened-applications)



- [Mailsniper Invoke functions](#mailsniper-invoke-functions)



- [Malicious PowerShell Process - Connect To Internet With Hidden Window](#malicious-powershell-process---connect-to-internet-with-hidden-window)



- [Malicious PowerShell Process - Encoded Command](#malicious-powershell-process---encoded-command)



- [Malicious PowerShell Process - Execution Policy Bypass](#malicious-powershell-process---execution-policy-bypass)



- [Malicious PowerShell Process With Obfuscation Techniques](#malicious-powershell-process-with-obfuscation-techniques)



- [Malicious Powershell Executed As A Service](#malicious-powershell-executed-as-a-service)



- [Modification Of Wallpaper](#modification-of-wallpaper)



- [Modify ACL permission To Files Or Folder](#modify-acl-permission-to-files-or-folder)



- [Modify ACLs Permission Of Files Or Folders](#modify-acls-permission-of-files-or-folders)





- [Monitor Registry Keys for Print Monitors](#monitor-registry-keys-for-print-monitors)



- [Monitor Successful Backups](#monitor-successful-backups)



- [Monitor Unsuccessful Backups](#monitor-unsuccessful-backups)





- [More than usual number of LOLBAS applications in short time period](#more-than-usual-number-of-lolbas-applications-in-short-time-period)



- [Mshta spawning Rundll32 OR Regsvr32 Process](#mshta-spawning-rundll32-or-regsvr32-process)



- [Msmpeng Application DLL Side Loading](#msmpeng-application-dll-side-loading)





- [Multiple Disabled Users Failing To Authenticate From Host Using Kerberos](#multiple-disabled-users-failing-to-authenticate-from-host-using-kerberos)



- [Multiple Invalid Users Failing To Authenticate From Host Using Kerberos](#multiple-invalid-users-failing-to-authenticate-from-host-using-kerberos)



- [Multiple Invalid Users Failing To Authenticate From Host Using NTLM](#multiple-invalid-users-failing-to-authenticate-from-host-using-ntlm)





- [Multiple Users Attempting To Authenticate Using Explicit Credentials](#multiple-users-attempting-to-authenticate-using-explicit-credentials)



- [Multiple Users Failing To Authenticate From Host Using Kerberos](#multiple-users-failing-to-authenticate-from-host-using-kerberos)



- [Multiple Users Failing To Authenticate From Host Using NTLM](#multiple-users-failing-to-authenticate-from-host-using-ntlm)



- [Multiple Users Failing To Authenticate From Process](#multiple-users-failing-to-authenticate-from-process)



- [Multiple Users Remotely Failing To Authenticate From Host](#multiple-users-remotely-failing-to-authenticate-from-host)



- [NET Profiler UAC bypass](#net-profiler-uac-bypass)



- [NLTest Domain Trust Discovery](#nltest-domain-trust-discovery)





- [Nishang PowershellTCPOneLine](#nishang-powershelltcponeline)





- [Ntdsutil Export NTDS](#ntdsutil-export-ntds)

























- [Office Application Spawn Regsvr32 process](#office-application-spawn-regsvr32-process)



- [Office Application Spawn rundll32 process](#office-application-spawn-rundll32-process)



- [Office Document Creating Schedule Task](#office-document-creating-schedule-task)



- [Office Document Executing Macro Code](#office-document-executing-macro-code)



- [Office Document Spawned Child Process To Download](#office-document-spawned-child-process-to-download)



- [Office Product Spawn CMD Process](#office-product-spawn-cmd-process)



- [Office Product Spawning BITSAdmin](#office-product-spawning-bitsadmin)



- [Office Product Spawning CertUtil](#office-product-spawning-certutil)



- [Office Product Spawning MSHTA](#office-product-spawning-mshta)



- [Office Product Spawning Rundll32 with no DLL](#office-product-spawning-rundll32-with-no-dll)



- [Office Product Spawning Wmic](#office-product-spawning-wmic)









- [Overwriting Accessibility Binaries](#overwriting-accessibility-binaries)



- [Permission Modification using Takeown App](#permission-modification-using-takeown-app)







- [PowerShell Domain Enumeration](#powershell-domain-enumeration)



- [PowerShell Loading DotNET into Memory via System Reflection Assembly](#powershell-loading-dotnet-into-memory-via-system-reflection-assembly)



- [PowerShell Start-BitsTransfer](#powershell-start-bitstransfer)



- [Powershell Creating Thread Mutex](#powershell-creating-thread-mutex)



- [Powershell Disable Security Monitoring](#powershell-disable-security-monitoring)



- [Powershell Enable SMB1Protocol Feature](#powershell-enable-smb1protocol-feature)



- [Powershell Fileless Process Injection via GetProcAddress](#powershell-fileless-process-injection-via-getprocaddress)



- [Powershell Fileless Script Contains Base64 Encoded Content](#powershell-fileless-script-contains-base64-encoded-content)



- [Powershell Processing Stream Of Data](#powershell-processing-stream-of-data)



- [Powershell Remote Thread To Known Windows Process](#powershell-remote-thread-to-known-windows-process)



- [Powershell Using memory As Backing Store](#powershell-using-memory-as-backing-store)



- [Prevent Automatic Repair Mode using Bcdedit](#prevent-automatic-repair-mode-using-bcdedit)

















































- [Previously Seen Running Windows Services - Initial](#previously-seen-running-windows-services---initial)



- [Previously Seen Running Windows Services - Update](#previously-seen-running-windows-services---update)







- [Previously Seen Zoom Child Processes - Initial](#previously-seen-zoom-child-processes---initial)



- [Previously Seen Zoom Child Processes - Update](#previously-seen-zoom-child-processes---update)







- [Previously seen command line arguments](#previously-seen-command-line-arguments)





- [Print Spooler Adding A Printer Driver](#print-spooler-adding-a-printer-driver)



- [Print Spooler Failed to Load a Plug-in](#print-spooler-failed-to-load-a-plug-in)



- [Probing Access with Stolen Credentials via PowerSploit modules](#probing-access-with-stolen-credentials-via-powersploit-modules)



- [Process Creating LNK file in Suspicious Location](#process-creating-lnk-file-in-suspicious-location)



- [Process Deleting Its Process File Path](#process-deleting-its-process-file-path)



- [Process Execution via WMI](#process-execution-via-wmi)



- [Process Kill Base On File Path](#process-kill-base-on-file-path)



- [Processes Tapping Keyboard Events](#processes-tapping-keyboard-events)



- [Processes launching netsh](#processes-launching-netsh)









- [Ransomware Notes bulk creation](#ransomware-notes-bulk-creation)



- [Rare Parent-Child Process Relationship](#rare-parent-child-process-relationship)



- [Recon AVProduct Through Pwh or WMI](#recon-avproduct-through-pwh-or-wmi)



- [Recon Using WMI Class](#recon-using-wmi-class)



- [Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules](#reconnaissance-and-access-to-accounts-groups-and-policies-via-powersploit-modules)



- [Reconnaissance and Access to Accounts and Groups via Mimikatz modules](#reconnaissance-and-access-to-accounts-and-groups-via-mimikatz-modules)



- [Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules](#reconnaissance-and-access-to-active-directoty-infrastructure-via-powersploit-modules)



- [Reconnaissance and Access to Computers and Domains via PowerSploit modules](#reconnaissance-and-access-to-computers-and-domains-via-powersploit-modules)



- [Reconnaissance and Access to Computers via Mimikatz modules](#reconnaissance-and-access-to-computers-via-mimikatz-modules)



- [Reconnaissance and Access to Operating System Elements via PowerSploit modules](#reconnaissance-and-access-to-operating-system-elements-via-powersploit-modules)



- [Reconnaissance and Access to Processes and Services via Mimikatz modules](#reconnaissance-and-access-to-processes-and-services-via-mimikatz-modules)



- [Reconnaissance and Access to Shared Resources via Mimikatz modules](#reconnaissance-and-access-to-shared-resources-via-mimikatz-modules)



- [Reconnaissance and Access to Shared Resources via PowerSploit modules](#reconnaissance-and-access-to-shared-resources-via-powersploit-modules)



- [Reconnaissance of Access and Persistence Opportunities via PowerSploit modules](#reconnaissance-of-access-and-persistence-opportunities-via-powersploit-modules)



- [Reconnaissance of Connectivity via PowerSploit modules](#reconnaissance-of-connectivity-via-powersploit-modules)



- [Reconnaissance of Credential Stores and Services via Mimikatz modules](#reconnaissance-of-credential-stores-and-services-via-mimikatz-modules)



- [Reconnaissance of Defensive Tools via PowerSploit modules](#reconnaissance-of-defensive-tools-via-powersploit-modules)



- [Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules](#reconnaissance-of-privilege-escalation-opportunities-via-powersploit-modules)



- [Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules](#reconnaissance-of-process-or-service-hijacking-opportunities-via-mimikatz-modules)



- [Recursive Delete of Directory In Batch CMD](#recursive-delete-of-directory-in-batch-cmd)



- [Reg exe Manipulating Windows Services Registry Keys](#reg-exe-manipulating-windows-services-registry-keys)



- [Registry Keys Used For Persistence](#registry-keys-used-for-persistence)



- [Registry Keys Used For Privilege Escalation](#registry-keys-used-for-privilege-escalation)



- [Registry Keys for Creating SHIM Databases](#registry-keys-for-creating-shim-databases)







- [Remote Desktop Process Running On System](#remote-desktop-process-running-on-system)



- [Remote Process Instantiation via WMI](#remote-process-instantiation-via-wmi)



- [Remote WMI Command Attempt](#remote-wmi-command-attempt)



- [Resize ShadowStorage volume](#resize-shadowstorage-volume)



- [Resize Shadowstorage Volume](#resize-shadowstorage-volume)



- [Revil Common Exec Parameter](#revil-common-exec-parameter)



- [Revil Registry Entry](#revil-registry-entry)



- [RunDLL Loading DLL By Ordinal](#rundll-loading-dll-by-ordinal)



- [Rundll32 Create Remote Thread To A Process](#rundll32-create-remote-thread-to-a-process)



- [Rundll32 CreateRemoteThread In Browser](#rundll32-createremotethread-in-browser)



- [Rundll32 DNSQuery](#rundll32-dnsquery)



- [Rundll32 Process Creating Exe Dll Files](#rundll32-process-creating-exe-dll-files)



- [Rundll32 with no Command Line Arguments with Network](#rundll32-with-no-command-line-arguments-with-network)



- [Ryuk Test Files Detected](#ryuk-test-files-detected)



- [Ryuk Wake on LAN Command](#ryuk-wake-on-lan-command)



- [SAM Database File Access Attempt](#sam-database-file-access-attempt)



- [SLUI RunAs Elevated](#slui-runas-elevated)



- [SLUI Spawning a Process](#slui-spawning-a-process)









- [Samsam Test File Write](#samsam-test-file-write)



- [Sc exe Manipulating Windows Services](#sc-exe-manipulating-windows-services)



- [Schedule Task with HTTP Command Arguments](#schedule-task-with-http-command-arguments)



- [Schedule Task with Rundll32 Command Trigger](#schedule-task-with-rundll32-command-trigger)



- [Scheduled Task Deleted Or Created via CMD](#scheduled-task-deleted-or-created-via-cmd)



- [Schtasks Run Task On Demand](#schtasks-run-task-on-demand)



- [Schtasks scheduling job on remote system](#schtasks-scheduling-job-on-remote-system)



- [Schtasks used for forcing a reboot](#schtasks-used-for-forcing-a-reboot)



- [Script Execution via WMI](#script-execution-via-wmi)



- [Sdclt UAC Bypass](#sdclt-uac-bypass)



- [SearchProtocolHost with no Command Line with Network](#searchprotocolhost-with-no-command-line-with-network)



- [SecretDumps Offline NTDS Dumping Tool](#secretdumps-offline-ntds-dumping-tool)



- [Services Escalate Exe](#services-escalate-exe)



- [Set Default PowerShell Execution Policy To Unrestricted or Bypass](#set-default-powershell-execution-policy-to-unrestricted-or-bypass)



- [Setting Credentials via DSInternals modules](#setting-credentials-via-dsinternals-modules)



- [Setting Credentials via Mimikatz modules](#setting-credentials-via-mimikatz-modules)



- [Setting Credentials via PowerSploit modules](#setting-credentials-via-powersploit-modules)



- [Shim Database File Creation](#shim-database-file-creation)



- [Shim Database Installation With Suspicious Parameters](#shim-database-installation-with-suspicious-parameters)



- [Short Lived Windows Accounts](#short-lived-windows-accounts)



- [SilentCleanup UAC Bypass](#silentcleanup-uac-bypass)



- [Single Letter Process On Endpoint](#single-letter-process-on-endpoint)



- [Spike in File Writes](#spike-in-file-writes)



- [Spoolsv Spawning Rundll32](#spoolsv-spawning-rundll32)



- [Spoolsv Suspicious Loaded Modules](#spoolsv-suspicious-loaded-modules)



- [Spoolsv Suspicious Process Access](#spoolsv-suspicious-process-access)



- [Spoolsv Writing a DLL](#spoolsv-writing-a-dll)



- [Spoolsv Writing a DLL - Sysmon](#spoolsv-writing-a-dll---sysmon)



- [Sqlite Module In Temp Folder](#sqlite-module-in-temp-folder)



- [Start Up During Safe Mode Boot](#start-up-during-safe-mode-boot)



- [Sunburst Correlation DLL and Network Event](#sunburst-correlation-dll-and-network-event)





- [Suspicious Curl Network Connection](#suspicious-curl-network-connection)



- [Suspicious DLLHost no Command Line Arguments](#suspicious-dllhost-no-command-line-arguments)



- [Suspicious Driver Loaded Path](#suspicious-driver-loaded-path)





- [Suspicious Event Log Service Behavior](#suspicious-event-log-service-behavior)



- [Suspicious GPUpdate no Command Line Arguments](#suspicious-gpupdate-no-command-line-arguments)



- [Suspicious IcedID Regsvr32 Cmdline](#suspicious-icedid-regsvr32-cmdline)



- [Suspicious IcedID Rundll32 Cmdline](#suspicious-icedid-rundll32-cmdline)





- [Suspicious MSBuild Rename](#suspicious-msbuild-rename)



- [Suspicious MSBuild Spawn](#suspicious-msbuild-spawn)



- [Suspicious PlistBuddy Usage](#suspicious-plistbuddy-usage)



- [Suspicious PlistBuddy Usage via OSquery](#suspicious-plistbuddy-usage-via-osquery)



- [Suspicious Process File Path](#suspicious-process-file-path)



- [Suspicious Reg exe Process](#suspicious-reg-exe-process)



- [Suspicious Regsvr32 Register Suspicious Path](#suspicious-regsvr32-register-suspicious-path)



- [Suspicious Rundll32 PluginInit](#suspicious-rundll32-plugininit)



- [Suspicious Rundll32 Rename](#suspicious-rundll32-rename)



- [Suspicious Rundll32 StartW](#suspicious-rundll32-startw)



- [Suspicious Rundll32 dllregisterserver](#suspicious-rundll32-dllregisterserver)



- [Suspicious Rundll32 no Command Line Arguments](#suspicious-rundll32-no-command-line-arguments)



- [Suspicious SQLite3 LSQuarantine Behavior](#suspicious-sqlite3-lsquarantine-behavior)



- [Suspicious Scheduled Task from Public Directory](#suspicious-scheduled-task-from-public-directory)



- [Suspicious SearchProtocolHost no Command Line Arguments](#suspicious-searchprotocolhost-no-command-line-arguments)



- [Suspicious microsoft workflow compiler rename](#suspicious-microsoft-workflow-compiler-rename)



- [Suspicious microsoft workflow compiler usage](#suspicious-microsoft-workflow-compiler-usage)



- [Suspicious msbuild path](#suspicious-msbuild-path)



- [Suspicious mshta child process](#suspicious-mshta-child-process)



- [Suspicious mshta spawn](#suspicious-mshta-spawn)



- [Suspicious wevtutil Usage](#suspicious-wevtutil-usage)



- [Suspicious writes to windows Recycle Bin](#suspicious-writes-to-windows-recycle-bin)



- [System Information Discovery Detection](#system-information-discovery-detection)



- [System Process Running from Unexpected Location](#system-process-running-from-unexpected-location)



- [System Processes Run From Unexpected Locations](#system-processes-run-from-unexpected-locations)



- [Systems Ready for Spectre-Meltdown Windows Patch](#systems-ready-for-spectre-meltdown-windows-patch)





- [Trickbot Named Pipe](#trickbot-named-pipe)



- [UAC Bypass MMC Load Unsigned Dll](#uac-bypass-mmc-load-unsigned-dll)



- [USN Journal Deletion](#usn-journal-deletion)



- [Unified Messaging Service Spawning a Process](#unified-messaging-service-spawning-a-process)



- [Unload Sysmon Filter Driver](#unload-sysmon-filter-driver)



- [Unloading AMSI via Reflection](#unloading-amsi-via-reflection)



- [Unusually Long Command Line](#unusually-long-command-line)



- [Unusually Long Command Line](#unusually-long-command-line)



- [Unusually Long Command Line - MLTK](#unusually-long-command-line---mltk)







- [W3WP Spawning Shell](#w3wp-spawning-shell)



- [WBAdmin Delete System Backups](#wbadmin-delete-system-backups)



- [WMI Permanent Event Subscription](#wmi-permanent-event-subscription)



- [WMI Permanent Event Subscription - Sysmon](#wmi-permanent-event-subscription---sysmon)



- [WMI Recon Running Process Or Services](#wmi-recon-running-process-or-services)



- [WMI Temporary Event Subscription](#wmi-temporary-event-subscription)



- [WSReset UAC Bypass](#wsreset-uac-bypass)



- [Wbemprox COM Object Execution](#wbemprox-com-object-execution)





- [Wermgr Process Connecting To IP Check Web Services](#wermgr-process-connecting-to-ip-check-web-services)



- [Wermgr Process Create Executable File](#wermgr-process-create-executable-file)



- [Wermgr Process Spawned CMD Or Powershell Process](#wermgr-process-spawned-cmd-or-powershell-process)



- [WevtUtil Usage To Clear Logs](#wevtutil-usage-to-clear-logs)



- [Wevtutil Usage To Disable Logs](#wevtutil-usage-to-disable-logs)



- [WinEvent Scheduled Task Created Within Public Path](#winevent-scheduled-task-created-within-public-path)



- [WinEvent Scheduled Task Created to Spawn Shell](#winevent-scheduled-task-created-to-spawn-shell)



- [WinRM Spawning a Process](#winrm-spawning-a-process)



- [Windows AdFind Exe](#windows-adfind-exe)



- [Windows DisableAntiSpyware Registry](#windows-disableantispyware-registry)



- [Windows Event Log Cleared](#windows-event-log-cleared)



- [Windows Security Account Manager Stopped](#windows-security-account-manager-stopped)



- [Windows Updates Install Failures](#windows-updates-install-failures)



- [Windows Updates Install Successes](#windows-updates-install-successes)



- [Winword Spawning Cmd](#winword-spawning-cmd)



- [Winword Spawning PowerShell](#winword-spawning-powershell)



- [Winword Spawning Windows Script Host](#winword-spawning-windows-script-host)



- [Write Executable in SMB Share](#write-executable-in-smb-share)



- [XMRIG Driver Loaded](#xmrig-driver-loaded)












</details>

## Network
<details>
  <summary>details</summary>



































































































































- [Baseline of DNS Query Length - MLTK](#baseline-of-dns-query-length---mltk)











- [Baseline of SMB Traffic - MLTK](#baseline-of-smb-traffic---mltk)



























































- [Count of Unique IPs Connecting to Ports](#count-of-unique-ips-connecting-to-ports)



















































- [DNS Query Length Outliers - MLTK](#dns-query-length-outliers---mltk)



- [DNS Query Length With High Standard Deviation](#dns-query-length-with-high-standard-deviation)



- [DNSTwist Domain Names](#dnstwist-domain-names)















- [Detect ARP Poisoning](#detect-arp-poisoning)



















































- [Detect IPv6 Network Infrastructure Threats](#detect-ipv6-network-infrastructure-threats)





- [Detect Large Outbound ICMP Packets](#detect-large-outbound-icmp-packets)



















- [Detect Outbound SMB Traffic](#detect-outbound-smb-traffic)









- [Detect Port Security Violation](#detect-port-security-violation)



































- [Detect Rogue DHCP Server](#detect-rogue-dhcp-server)













- [Detect SNICat SNI Exfiltration](#detect-snicat-sni-exfiltration)









- [Detect Software Download To Network Device](#detect-software-download-to-network-device)











- [Detect Traffic Mirroring](#detect-traffic-mirroring)



- [Detect Unauthorized Assets by MAC address](#detect-unauthorized-assets-by-mac-address)







- [Detect Windows DNS SIGRed via Splunk Stream](#detect-windows-dns-sigred-via-splunk-stream)



- [Detect Windows DNS SIGRed via Zeek](#detect-windows-dns-sigred-via-zeek)



- [Detect Zerologon via Zeek](#detect-zerologon-via-zeek)





- [Detect hosts connecting to dynamic domain providers](#detect-hosts-connecting-to-dynamic-domain-providers)



















































- [Discover DNS records](#discover-dns-records)































- [Excessive DNS Failures](#excessive-dns-failures)

























































- [Get Certificate logs for a domain](#get-certificate-logs-for-a-domain)



- [Get DNS Server History for a host](#get-dns-server-history-for-a-host)



- [Get DNS traffic ratio](#get-dns-traffic-ratio)







- [Get Email Info](#get-email-info)



- [Get Emails From Specific Sender](#get-emails-from-specific-sender)



- [Get First Occurrence and Last Occurrence of a MAC Address](#get-first-occurrence-and-last-occurrence-of-a-mac-address)



- [Get History Of Email Sources](#get-history-of-email-sources)









- [Get Outbound Emails to Hidden Cobra Threat Actors](#get-outbound-emails-to-hidden-cobra-threat-actors)















- [Get Web Session Information via session id](#get-web-session-information-via-session-id)















- [Hosts receiving high volume of network traffic from email server](#hosts-receiving-high-volume-of-network-traffic-from-email-server)









- [Identify Systems Creating Remote Desktop Traffic](#identify-systems-creating-remote-desktop-traffic)



- [Identify Systems Receiving Remote Desktop Traffic](#identify-systems-receiving-remote-desktop-traffic)































- [Investigate Network Traffic From src ip](#investigate-network-traffic-from-src-ip)















- [Investigate Suspicious Strings in HTTP Header](#investigate-suspicious-strings-in-http-header)





- [Investigate Web POSTs From src](#investigate-web-posts-from-src)









- [Large Volume of DNS ANY Queries](#large-volume-of-dns-any-queries)







































- [Multiple Archive Files Http Post Traffic](#multiple-archive-files-http-post-traffic)

























































































- [Plain HTTP POST Exfiltrated Data](#plain-http-post-exfiltrated-data)















































































































- [Prohibited Network Traffic Allowed](#prohibited-network-traffic-allowed)



- [Protocol or Port Mismatch](#protocol-or-port-mismatch)



- [Protocols passing authentication in cleartext](#protocols-passing-authentication-in-cleartext)



















































- [Remote Desktop Network Bruteforce](#remote-desktop-network-bruteforce)



- [Remote Desktop Network Traffic](#remote-desktop-network-traffic)







































- [SMB Traffic Spike](#smb-traffic-spike)



- [SMB Traffic Spike - MLTK](#smb-traffic-spike---mltk)











































































































































- [TOR Traffic](#tor-traffic)





















- [Unusually Long Content-Type Length](#unusually-long-content-type-length)






































































</details>

## Application
<details>
  <summary>details</summary>











































































































































































































































































































































- [Detect New Login Attempts to Routers](#detect-new-login-attempts-to-routers)































































































































































- [Email Attachments With Lots Of Spaces](#email-attachments-with-lots-of-spaces)



- [Email files written outside of the Outlook directory](#email-files-written-outside-of-the-outlook-directory)



- [Email servers sending high volume traffic to hosts](#email-servers-sending-high-volume-traffic-to-hosts)



















































































































































































































- [Monitor Email For Brand Abuse](#monitor-email-for-brand-abuse)

























- [Multiple Okta Users With Invalid Credentials From The Same IP](#multiple-okta-users-with-invalid-credentials-from-the-same-ip)





















- [No Windows Updates in a time frame](#no-windows-updates-in-a-time-frame)

















































- [Okta Account Lockout Events](#okta-account-lockout-events)



- [Okta Failed SSO Attempts](#okta-failed-sso-attempts)



- [Okta User Logins From Multiple Cities](#okta-user-logins-from-multiple-cities)







- [Phishing Email Detection by Machine Learning Method - SSA](#phishing-email-detection-by-machine-learning-method---ssa)



























































































































































































































































































- [Suspicious Email Attachment Extensions](#suspicious-email-attachment-extensions)











- [Suspicious Java Classes](#suspicious-java-classes)































































































- [Web Servers Executing Suspicious Processes](#web-servers-executing-suspicious-processes)


















































</details>

## Web
<details>
  <summary>details</summary>

















































































































































































































































































































- [Detect F5 TMUI RCE CVE-2020-5902](#detect-f5-tmui-rce-cve-2020-5902)

























































































































- [Detect attackers scanning for vulnerable JBoss servers](#detect-attackers-scanning-for-vulnerable-jboss-servers)





- [Detect malicious requests to exploit JBoss servers](#detect-malicious-requests-to-exploit-jboss-servers)



























































































































































































































































































- [Monitor Web Traffic For Brand Abuse](#monitor-web-traffic-for-brand-abuse)

















































































































































































































































































































- [SQL Injection with Long URLs](#sql-injection-with-long-urls)

































































- [Supernova Webshell](#supernova-webshell)


































































































































































</details>




### AWS Create Policy Version to allow all resources
This search looks for AWS CloudTrail events where a user created a policy version that allows them to access any resource in their account

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078/004/)
- **Last Updated**: 2021-02-22

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=CreatePolicyVersion eventSource = iam.amazonaws.com errorCode = success 
| spath input=requestParameters.policyDocument output=key_policy_statements path=Statement{} 
| mvexpand key_policy_statements 
| spath input=key_policy_statements output=key_policy_action_1 path=Action 
| search key_policy_action_1 = "*" 
| stats count min(_time) as firstTime max(_time) as lastTime values(key_policy_statements) as policy_added by eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
|`aws_create_policy_version_to_allow_all_resources_filter`
```
#### Associated Analytic Story

* AWS IAM Privilege Escalation


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* _time

* eventName

* userAgent

* errorCode

* requestParameters.userName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.004 | Cloud Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
While this search has no known false positives, it is possible that an AWS admin has legitimately created a policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources

#### Reference


* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws

* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_create_policy_version/aws_cloudtrail_events.json


_version_: 2
</details>

---

### AWS CreateAccessKey
This search looks for AWS CloudTrail events where a user A who has already permission to create access keys, makes an API call to create access keys for another user B. Attackers have been know to use this technique for Privilege Escalation in case new victim(user B) has more permissions than old victim(user B)

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136/003/)
- **Last Updated**: 2021-07-19

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName = CreateAccessKey userAgent !=console.amazonaws.com errorCode = success
| search userIdentity.userName!=requestParameters.userName  
|  stats count min(_time) as firstTime max(_time) as lastTime  by requestParameters.userName src eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
|`aws_createaccesskey_filter`
```
#### Associated Analytic Story

* AWS IAM Privilege Escalation


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* _time

* eventName

* userAgent

* errorCode

* requestParameters.userName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1136.003 | Cloud Account | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user.

#### Reference


* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws

* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createaccesskey/aws_cloudtrail_events.json


_version_: 2
</details>

---

### AWS CreateLoginProfile
This search looks for AWS CloudTrail events where a user A(victim A) creates a login profile for user B, followed by a AWS Console login event from user B from the same src_ip as user B. This correlated event can be indicative of privilege escalation since both events happened from the same src_ip

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136/003/)
- **Last Updated**: 2021-07-19

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName = CreateLoginProfile 
| rename requestParameters.userName as new_login_profile 
| table src_ip eventName new_login_profile userIdentity.userName  
| join new_login_profile src_ip [
| search `cloudtrail` eventName = ConsoleLogin 
| rename userIdentity.userName  as new_login_profile 
| stats count values(eventName) min(_time) as firstTime max(_time) as lastTime by eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn new_login_profile src_ip 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`] 
| `aws_createloginprofile_filter`
```
#### Associated Analytic Story

* AWS IAM Privilege Escalation


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* _time

* eventName

* userAgent

* errorCode

* requestParameters.userName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1136.003 | Cloud Account | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
While this search has no known false positives, it is possible that an AWS admin has legitimately created a login profile for another user.

#### Reference


* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws

* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_createloginprofile/aws_cloudtrail_events.json


_version_: 2
</details>

---

### AWS Cross Account Activity From Previously Unseen Account
This search looks for AssumeRole events where an IAM role in a different account is requested for the first time.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Authentication
- **ATT&CK**: 
- **Last Updated**: 2020-05-28

<details>
  <summary>details</summary>

#### Search
```

| tstats min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role 
| `drop_dm_object_name(Authentication)` 
| rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" 
| where vendor_account != dest_account 
| rename vendor_account as requestingAccountId dest_account as requestedAccountId 
| lookup previously_seen_aws_cross_account_activity requestingAccountId, requestedAccountId, OUTPUTNEW firstTime 
| eval status = if(firstTime > relative_time(now(), "-24h@h"),"New Cross Account Activity","Previously Seen") 
|  where status = "New Cross Account Activity" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `aws_cross_account_activity_from_previously_unseen_account_filter`
```
#### Associated Analytic Story

* Suspicious Cloud Authentication Activities


#### How To Implement
You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen AWS Cross Account Activity - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen AWS Cross Account Activity - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `aws_cross_account_activity_from_previously_unseen_account_filter` macro.

#### Required field

* _time

* Authentication.signature

* Authentication.vendor_account

* Authentication.user

* Authentication.user_role

* Authentication.src




#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### AWS Detect Users creating keys with encrypt policy without MFA
This search provides detection of KMS keys where action kms:Encrypt is accessible for everyone (also outside of your organization). This is an indicator that your account is compromised and the attacker uses the encryption key to compromise another company.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/)
- **Last Updated**: 2021-01-11

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=CreateKey OR eventName=PutKeyPolicy 
| spath input=requestParameters.policy output=key_policy_statements path=Statement{} 
| mvexpand key_policy_statements 
| spath input=key_policy_statements output=key_policy_action_1 path=Action 
| spath input=key_policy_statements output=key_policy_action_2 path=Action{} 
| eval key_policy_action=mvappend(key_policy_action_1, key_policy_action_2) 
| spath input=key_policy_statements output=key_policy_principal path=Principal.AWS 
| search key_policy_action="kms:Encrypt" AND key_policy_principal="*" 
| stats count min(_time) as firstTime max(_time) as lastTime by eventName eventSource eventID awsRegion userIdentity.principalId 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|`aws_detect_users_creating_keys_with_encrypt_policy_without_mfa_filter`
```
#### Associated Analytic Story

* Ransomware Cloud


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs

#### Required field

* _time

* eventName

* eventSource

* eventID

* awsRegion

* requestParameters.policy

* userIdentity.principalId



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1486 | Data Encrypted for Impact | Impact |


#### Kill Chain Phase


#### Known False Positives
unknown

#### Reference


* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/

* https://github.com/d1vious/git-wild-hunt

* https://www.youtube.com/watch?v=PgzNib37g0M



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/aws_kms_key/aws_cloudtrail_events.json


_version_: 1
</details>

---

### AWS Detect Users with KMS keys performing encryption S3
This search provides detection of users with KMS keys performing encryption specifically against S3 buckets.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/)
- **Last Updated**: 2021-01-11

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=CopyObject requestParameters.x-amz-server-side-encryption="aws:kms" 
| rename requestParameters.bucketName AS bucket_name, requestParameters.x-amz-copy-source AS src_file, requestParameters.key AS dest_file 
| stats count min(_time) as firstTime max(_time) as lastTime values(src_file) AS src_file values(dest_file) AS dest_file values(userAgent) AS userAgent values(region) AS region values(src) AS src by user 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|`aws_detect_users_with_kms_keys_performing_encryption_s3_filter`
```
#### Associated Analytic Story

* Ransomware Cloud


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs

#### Required field

* _time

* eventName

* requestParameters.x-amz-server-side-encryption

* requestParameters.bucketName

* requestParameters.x-amz-copy-source

* requestParameters.key

* userAgent

* region



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1486 | Data Encrypted for Impact | Impact |


#### Kill Chain Phase


#### Known False Positives
bucket with S3 encryption

#### Reference


* https://rhinosecuritylabs.com/aws/s3-ransomware-part-1-attack-vector/

* https://github.com/d1vious/git-wild-hunt

* https://www.youtube.com/watch?v=PgzNib37g0M



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/s3_file_encryption/aws_cloudtrail_events.json


_version_: 1
</details>

---

### AWS ECR Container Scanning Findings High
This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). You need to activate image scanning in order to get the event DescribeImageScanFindings with the results.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1204.003](https://attack.mitre.org/techniques/T1204/003/)
- **Last Updated**: 2021-08-17

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings 
| spath path=responseElements.imageScanFindings.findings{} output=findings 
| mvexpand findings 
| spath input=findings
| search severity=HIGH 
| rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repositoryName 
| eval finding = finding_name.", ".finding_description 
| stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, imageDigest, repositoryName, user, userName, src_ip, finding 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_ecr_container_scanning_findings_high_filter`
```
#### Associated Analytic Story

* Dev Sec Ops


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* eventSource

* eventName

* responseElements.imageScanFindings.findings{}

* awsRegion

* requestParameters.imageId.imageDigest

* requestParameters.repositoryName

* user

* userName

* src_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1204.003 | Malicious Image | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
unknown

#### Reference


* https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html



#### Test Dataset


_version_: 1
</details>

---

### AWS ECR Container Scanning Findings Low Informational Unknown
This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). You need to activate image scanning in order to get the event DescribeImageScanFindings with the results.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1204.003](https://attack.mitre.org/techniques/T1204/003/)
- **Last Updated**: 2021-08-17

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings 
| spath path=responseElements.imageScanFindings.findings{} output=findings 
| mvexpand findings 
| spath input=findings
| search severity IN (LOW, INFORMATIONAL, UNKNWON) 
| rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repositoryName 
| eval finding = finding_name.", ".finding_description 
| stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, imageDigest, repositoryName, user, userName, src_ip, finding 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_ecr_container_scanning_findings_low_informational_unknown_filter`
```
#### Associated Analytic Story

* Dev Sec Ops


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* eventSource

* eventName

* responseElements.imageScanFindings.findings{}

* awsRegion

* requestParameters.imageId.imageDigest

* requestParameters.repositoryName

* user

* userName

* src_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1204.003 | Malicious Image | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
unknown

#### Reference


* https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html



#### Test Dataset


_version_: 1
</details>

---

### AWS ECR Container Scanning Findings Medium
This search looks for AWS CloudTrail events from AWS Elastic Container Service (ECR). You need to activate image scanning in order to get the event DescribeImageScanFindings with the results.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1204.003](https://attack.mitre.org/techniques/T1204/003/)
- **Last Updated**: 2021-08-17

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventSource=ecr.amazonaws.com eventName=DescribeImageScanFindings 
| spath path=responseElements.imageScanFindings.findings{} output=findings 
| mvexpand findings 
| spath input=findings
| search severity=MEDIUM 
| rename name as finding_name, description as finding_description, requestParameters.imageId.imageDigest as imageDigest, requestParameters.repositoryName as repositoryName 
| eval finding = finding_name.", ".finding_description 
| stats min(_time) as firstTime max(_time) as lastTime by awsRegion, eventName, eventSource, imageDigest, repositoryName, user, userName, src_ip, finding 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_ecr_container_scanning_findings_medium_filter`
```
#### Associated Analytic Story

* Dev Sec Ops


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* eventSource

* eventName

* responseElements.imageScanFindings.findings{}

* awsRegion

* requestParameters.imageId.imageDigest

* requestParameters.repositoryName

* user

* userName

* src_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1204.003 | Malicious Image | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
unknown

#### Reference


* https://docs.aws.amazon.com/AmazonECR/latest/userguide/image-scanning.html



#### Test Dataset


_version_: 1
</details>

---

### AWS Excessive Security Scanning
This search looks for AWS CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/)
- **Last Updated**: 2021-04-13

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=Describe* OR eventName=List* OR eventName=Get*  
| stats dc(eventName) as dc_events min(_time) as firstTime max(_time) as lastTime values(eventName) as eventName values(src) as src values(userAgent) as userAgent by user userIdentity.arn 
| where dc_events > 50 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
|`aws_excessive_security_scanning_filter`
```
#### Associated Analytic Story

* AWS User Monitoring


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* _time

* eventName

* src

* userAgent

* user

* userIdentity.arn



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1526 | Cloud Service Discovery | Discovery |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
While this search has no known false positives.

#### Reference


* https://github.com/aquasecurity/cloudsploit



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1526/aws_security_scanner/aws_security_scanner.json


_version_: 1
</details>

---

### AWS IAM AccessDenied Discovery Events
The following detection identifies excessive AccessDenied events within an hour timeframe. It is possible that an access key to AWS may have been stolen and is being misused to perform discovery events. In these instances, the access is not available with the key stolen therefore these events will be generated.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: [T1580](https://attack.mitre.org/techniques/T1580/)
- **Last Updated**: 2021-04-05

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` (errorCode = "AccessDenied") user_type=IAMUser (userAgent!=*.amazonaws.com) 
| bucket _time span=1h 
| stats count as failures min(_time) as firstTime max(_time) as lastTime, dc(eventName) as methods, dc(eventSource) as sources values(userIdentity.arn) by src_ip, userIdentity.arn, _time 
| where failures >= 5 and methods >= 1 and sources >= 1 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_iam_accessdenied_discovery_events_filter`
```
#### Associated Analytic Story

* Suspicious Cloud User Activities


#### How To Implement
The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.

#### Required field

* _time

* eventName

* eventSource

* userAgent

* errorCode

* userIdentity.type



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1580 | Cloud Infrastructure Discovery | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
It is possible to start this detection will need to be tuned by source IP or user. In addition, change the count values to an upper threshold to restrict false positives.

#### Reference


* https://aws.amazon.com/premiumsupport/knowledge-center/troubleshoot-iam-permission-errors/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_accessdenied_discovery_events/aws_iam_accessdenied_discovery_events.json


_version_: 1
</details>

---

### AWS IAM Assume Role Policy Brute Force
The following detection identifies any malformed policy document exceptions with a status of `failure`. A malformed policy document exception occurs in instances where roles are attempted to be assumed, or brute forced. In a brute force attempt, using a tool like CloudSploit or Pacu, an attempt will look like `arn:aws:iam::111111111111:role/aws-service-role/rds.amazonaws.com/AWSServiceRoleForRDS`.  Meaning, when an adversary is attempting to identify a role name, multiple failures will occur. This detection focuses on the errors of a remote attempt that is failing.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: [T1580](https://attack.mitre.org/techniques/T1580/), [T1110](https://attack.mitre.org/techniques/T1110/)
- **Last Updated**: 2021-04-01

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` (errorCode=MalformedPolicyDocumentException) status=failure (userAgent!=*.amazonaws.com) 
| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyName) as policy_name by src eventName eventSource aws_account_id errorCode requestParameters.policyDocument userAgent eventID awsRegion userIdentity.principalId user_arn 
| where count >= 2 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_iam_assume_role_policy_brute_force_filter`
```
#### Associated Analytic Story

* AWS IAM Privilege Escalation


#### How To Implement
The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs. Set the `where count` greater than a value to identify suspicious activity in your environment.

#### Required field

* _time

* eventName

* userAgent

* errorCode

* requestParameters.policyName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1580 | Cloud Infrastructure Discovery | Discovery |
| T1110 | Brute Force | Credential Access |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users.

#### Reference


* https://www.praetorian.com/blog/aws-iam-assume-role-vulnerabilities

* https://rhinosecuritylabs.com/aws/assume-worst-aws-assume-role-enumeration/

* https://www.elastic.co/guide/en/security/current/aws-iam-brute-force-of-assume-role-policy.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1580/aws_iam_assume_role_policy_brute_force/aws_iam_assume_role_policy_brute_force.json


_version_: 1
</details>

---

### AWS IAM Delete Policy
The following detection identifes when a policy is deleted on AWS. This does not identify whether successful or failed, but the error messages tell a story of suspicious attempts. There is a specific process to follow when deleting a policy. First, detach the policy from all users, groups, and roles that the policy is attached to, using DetachUserPolicy , DetachGroupPolicy , or DetachRolePolicy.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: [T1098](https://attack.mitre.org/techniques/T1098/)
- **Last Updated**: 2021-04-01

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=DeletePolicy (userAgent!=*.amazonaws.com) 
| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policyArn by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId userIdentity.arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_iam_delete_policy_filter`
```
#### Associated Analytic Story

* AWS IAM Privilege Escalation


#### How To Implement
The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.

#### Required field

* _time

* eventName

* userAgent

* errorCode

* requestParameters.policyArn



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1098 | Account Manipulation | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete policies (least privilege). In addition, this may be saved seperately and tuned for failed or success attempts only.

#### Reference


* https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeletePolicy.html

* https://docs.aws.amazon.com/cli/latest/reference/iam/delete-policy.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_delete_policy/aws_iam_delete_policy.json


_version_: 1
</details>

---

### AWS IAM Failure Group Deletion
This detection identifies failure attempts to delete groups. We want to identify when a group is attempting to be deleted, but either access is denied, there is a conflict or there is no group. This is indicative of administrators performing an action, but also could be suspicious behavior occurring. Review parallel IAM events - recently added users, new groups and so forth.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: [T1098](https://attack.mitre.org/techniques/T1098/)
- **Last Updated**: 2021-04-01

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode IN (NoSuchEntityException,DeleteConflictException, AccessDenied) (userAgent!=*.amazonaws.com) 
| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) as group_name by src eventName eventSource aws_account_id errorCode errorMessage userAgent eventID awsRegion userIdentity.principalId user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_iam_failure_group_deletion_filter`
```
#### Associated Analytic Story

* AWS IAM Privilege Escalation


#### How To Implement
The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.

#### Required field

* _time

* eventName

* userAgent

* errorCode

* requestParameters.groupName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1098 | Account Manipulation | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).

#### Reference


* https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html

* https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_failure_group_deletion/aws_iam_failure_group_deletion.json


_version_: 1
</details>

---

### AWS IAM Successful Group Deletion
The following query uses IAM events to track the success of a group being deleted on AWS. This is typically not indicative of malicious behavior, but a precurser to additional events thay may unfold. Review parallel IAM events - recently added users, new groups and so forth. Inversely, review failed attempts in a similar manner.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: [T1069.003](https://attack.mitre.org/techniques/T1069/003/), [T1098](https://attack.mitre.org/techniques/T1098/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventSource=iam.amazonaws.com eventName=DeleteGroup errorCode=success (userAgent!=*.amazonaws.com) 
| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.groupName) as group_deleted by src eventName eventSource errorCode user_agent awsRegion userIdentity.principalId user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_iam_successful_group_deletion_filter`
```
#### Associated Analytic Story

* AWS IAM Privilege Escalation


#### How To Implement
The Splunk AWS Add-on and Splunk App for AWS is required to utilize this data. The search requires AWS Cloudtrail logs.

#### Required field

* _time

* eventName

* userAgent

* errorCode

* requestParameters.groupName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1069.003 | Cloud Groups | Discovery |
| T1098 | Account Manipulation | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
This detection will require tuning to provide high fidelity detection capabilties. Tune based on src addresses (corporate offices, VPN terminations) or by groups of users. Not every user with AWS access should have permission to delete groups (least privilege).

#### Reference


* https://awscli.amazonaws.com/v2/documentation/api/latest/reference/iam/delete-group.html

* https://docs.aws.amazon.com/IAM/latest/APIReference/API_DeleteGroup.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/aws_iam_successful_group_deletion/aws_iam_successful_group_deletion.json


_version_: 1
</details>

---

### AWS Investigate Security Hub alerts by dest
This search retrieves the all the alerts created by AWS Security Hub for a specific dest(instance_id).

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-06-08

<details>
  <summary>details</summary>

#### Search
```
`aws_securityhub_firehose` "findings{}.Resources{}.Type"=AWSEC2Instance 
| rex field=findings{}.Resources{}.Id .*instance/(?<instance>.*)
| rename instance as dest
| search dest = $dest$ 
|rename findings{}.* as * 
| rename Remediation.Recommendation.Text as Remediation 
|  table dest Title ProductArn Description FirstObservedAt RecordState Remediation
```
#### Associated Analytic Story

* Cloud Compute Instance

* Cloud Cryptomining

* Suspicious AWS EC2 Activities

* AWS Suspicious Provisioning Activities


#### 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.

#### Required field

* _time

* findings{}.Resources{}.Type

* findings{}.Resources{}.Id

* instance

* Remediation.Recommendation.Text

* Title

* ProductArn

* Description

* FirstObservedAt

* RecordState




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### AWS Investigate User Activities By ARN
This search lists all the logged CloudTrail activities by a specific user ARN and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and all the user's identity information.

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2019-04-30

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` 
| search user=$user$
| table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType
```
#### Associated Analytic Story

* AWS Cryptomining

* AWS Network ACL Activity

* Cloud Cryptomining

* Command and Control

* Suspicious AWS EC2 Activities

* Suspicious AWS Login Activities

* Suspicious AWS S3 Activities

* Suspicious AWS Traffic

* Unusual AWS EC2 Modifications

* Suspicious Cloud User Activities

* AWS Suspicious Provisioning Activities

* Suspicious Cloud Instance Activities

* AWS Security Hub Alerts


#### 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.

#### Required field

* _time

* user

* userIdentity.type

* userIdentity.userName

* userIdentity.arn

* aws_account_id

* src

* awsRegion

* eventName

* eventType




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 2
</details>

---

### AWS Investigate User Activities By AccessKeyId
This search retrieves the times, ARN, source IPs, AWS regions, event names, and the result of the event for specific credentials.

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-06-08

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` 
| rename userIdentity.accessKeyId as accessKeyId
| search accessKeyId=$accessKeyId$ 
| spath output=user path=userIdentity.arn  
| rename sourceIPAddress as src_ip 
| table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage
```
#### Associated Analytic Story

* AWS Cross Account Activity


#### 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.

#### Required field

* _time

* userIdentity.accessKeyId

* userIdentity.arn

* sourceIPAddress

* awsRegion

* eventName

* errorCode

* errorMessage




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### AWS Network ACL Details from ID
This search queries AWS description logs and returns all the information about a specific network ACL via network ACL ID

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-01-22

<details>
  <summary>details</summary>

#### Search
```
`aws_description` 
| rename id as networkAclId 
| search  networkAclId=$networkAclId$ 
| table id account_id vpc_id network_acl_entries{}.*
```
#### Associated Analytic Story

* AWS Network ACL Activity

* Command and Control

* Suspicious AWS Traffic


#### How To Implement
In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs.

#### Required field

* _time

* id

* account_id

* vpc_id

* network_acl_entries{}.*




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### AWS Network Access Control List Created with All Open Ports
The search looks for AWS CloudTrail events to detect if any network ACLs were created with all the ports open to a specified CIDR.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562/007/)
- **Last Updated**: 2021-01-11

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol=-1 
| append [search `cloudtrail` eventName=CreateNetworkAclEntry OR eventName=ReplaceNetworkAclEntry requestParameters.ruleAction=allow requestParameters.egress=false requestParameters.aclProtocol!=-1 
| eval port_range='requestParameters.portRange.to' - 'requestParameters.portRange.from' 
| where port_range>1024] 
| fillnull 
| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.ruleAction requestParameters.egress requestParameters.aclProtocol requestParameters.portRange.to requestParameters.portRange.from src userAgent requestParameters.cidrBlock 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `aws_network_access_control_list_created_with_all_open_ports_filter`
```
#### Associated Analytic Story

* AWS Network ACL Activity


#### 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 AWS CloudTrail inputs.

#### Required field

* _time

* eventName

* requestParameters.ruleAction

* requestParameters.egress

* requestParameters.aclProtocol

* requestParameters.portRange.to

* requestParameters.portRange.from

* requestParameters.cidrBlock

* userName

* userIdentity.principalId

* userAgent



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_create_acl/aws_cloudtrail_events.json


_version_: 2
</details>

---

### AWS Network Access Control List Deleted
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 AWS CloudTrail logs to detect users deleting network ACLs.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562/007/)
- **Last Updated**: 2021-01-12

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=DeleteNetworkAclEntry requestParameters.egress=false 
| fillnull 
| stats count min(_time) as firstTime max(_time) as lastTime by userName userIdentity.principalId eventName requestParameters.egress src userAgent 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `aws_network_access_control_list_deleted_filter`
```
#### Associated Analytic Story

* AWS Network ACL Activity


#### 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 AWS CloudTrail inputs.

#### Required field

* _time

* eventName

* requestParameters.egress

* userName

* userIdentity.principalId

* src

* userAgent



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
It's possible that a user has legitimately deleted a network ACL.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/aws_delete_acl/aws_cloudtrail_events.json


_version_: 2
</details>

---

### AWS Network Interface details via resourceId
This search queries AWS configuration logs and returns the information about a specific network interface via network interface ID. The information will include the ARN of the network interface, its relationships with other AWS resources, the public and the private IP associated with the network interface.

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-05-07

<details>
  <summary>details</summary>

#### Search
```
`aws_config` resourceId=$resourceId$ 
| table _time ARN relationships{}.resourceType relationships{}.name relationships{}.resourceId  configuration.privateIpAddresses{}.privateIpAddress configuration.privateIpAddresses{}.association.publicIp
```
#### Associated Analytic Story

* AWS Network ACL Activity

* Command and Control

* Suspicious AWS Traffic


#### How To Implement
In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS configuration inputs

#### Required field

* _time

* resourceId

* ARN

* relationships{}.resourceType

* relationships{}.name

* relationships{}.resourceId

* configuration.privateIpAddresses{}.privateIpAddress

* configuration.privateIpAddresses{}.association.publicIp




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### AWS S3 Bucket details via bucketName
This search queries AWS configuration logs and returns the information about a specific S3 bucket. The information returned includes the time the S3 bucket was created, the resource ID, the region it belongs to, the value of action performed, AWS account ID, and configuration values of the access-control lists associated with the bucket.

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-06-26

<details>
  <summary>details</summary>

#### Search
```
`aws_config` 
| rename resourceId as bucketName 
|search bucketName=$bucketName$ 
| table resourceCreationTime bucketName vendor_region action aws_account_id supplementaryConfiguration.AccessControlList
```
#### Associated Analytic Story

* Suspicious AWS S3 Activities


#### How To Implement
To implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later) and configure your AWS inputs.

#### Required field

* _time

* resourceId

* bucketName

* resourceCreationTime

* vendor_region

* action

* aws_account_id

* supplementaryConfiguration.AccessControlList




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### AWS SAML Access by Provider User and Principal
This search provides specific SAML access from specific Service Provider, user and targeted principal at AWS. This search provides specific information to detect abnormal access or potential credential hijack or forgery, specially in federated environments using SAML protocol inside the perimeter or cloud provider.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2021-01-26

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=Assumerolewithsaml 
| stats count min(_time) as firstTime max(_time) as lastTime by requestParameters.principalArn requestParameters.roleArn requestParameters.roleSessionName recipientAccountId responseElements.issuer sourceIPAddress userAgent 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|`aws_saml_access_by_provider_user_and_principal_filter`
```
#### Associated Analytic Story

* Cloud Federated Credential Abuse


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs

#### Required field

* _time

* eventName

* requestParameters.principalArn

* requestParameters.roleArn

* requestParameters.roleSessionName

* recipientAccountId

* responseElements.issuer

* sourceIPAddress

* userAgent



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### Known False Positives
Attacks using a Golden SAML or SAML assertion hijacks or forgeries are very difficult to detect as accessing cloud providers with these assertions looks exactly like normal access, however things such as source IP sourceIPAddress user, and principal targeted at receiving cloud provider along with endpoint credential access and abuse detection searches can provide the necessary context to detect these attacks.

#### Reference


* https://us-cert.cisa.gov/ncas/alerts/aa21-008a

* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html

* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf

* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/assume_role_with_saml/assume_role_with_saml.json


_version_: 1
</details>

---

### AWS SAML Update identity provider
This search provides detection of updates to SAML provider in AWS. Updates to SAML provider need to be monitored closely as they may indicate possible perimeter compromise of federated credentials, or backdoor access from another cloud provider set by attacker.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2021-01-26

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=UpdateSAMLProvider 
| stats count min(_time) as firstTime max(_time) as lastTime by eventType eventName requestParameters.sAMLProviderArn userIdentity.sessionContext.sessionIssuer.arn sourceIPAddress userIdentity.accessKeyId userIdentity.principalId 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|`aws_saml_update_identity_provider_filter`
```
#### Associated Analytic Story

* Cloud Federated Credential Abuse


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* _time

* eventName

* eventType

* requestParameters.sAMLProviderArn

* userIdentity.sessionContext.sessionIssuer.arn

* sourceIPAddress

* userIdentity.accessKeyId

* userIdentity.principalId



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### Known False Positives
Updating a SAML provider or creating a new one may not necessarily be malicious however it needs to be closely monitored.

#### Reference


* https://us-cert.cisa.gov/ncas/alerts/aa21-008a

* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html

* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf

* https://www.cyberark.com/resources/threat-research-blog/golden-saml-newly-discovered-attack-technique-forges-authentication-to-cloud-apps



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/update_saml_provider/update_saml_provider.json


_version_: 1
</details>

---

### AWS SetDefaultPolicyVersion
This search looks for AWS CloudTrail events where a user has set a default policy versions. Attackers have been know to use this technique for Privilege Escalation in case the previous versions of the policy had permissions to access more resources than the current version of the policy

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078/004/)
- **Last Updated**: 2021-03-02

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=SetDefaultPolicyVersion eventSource = iam.amazonaws.com 
| stats count min(_time) as firstTime max(_time) as lastTime values(requestParameters.policyArn) as policy_arn by src requestParameters.versionId eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.principalId user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `aws_setdefaultpolicyversion_filter`
```
#### Associated Analytic Story

* AWS IAM Privilege Escalation


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* _time

* eventName

* userAgent

* errorCode

* requestParameters.userName

* eventSource



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.004 | Cloud Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
While this search has no known false positives, it is possible that an AWS admin has legitimately set a default policy to allow a user to access all resources. That said, AWS strongly advises against granting full control to all AWS resources

#### Reference


* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws

* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_setdefaultpolicyversion/aws_cloudtrail_events.json


_version_: 1
</details>

---

### AWS UpdateLoginProfile
This search looks for AWS CloudTrail events where a user A who has already permission to update login profile, makes an API call to update login profile for another user B . Attackers have been know to use this technique for Privilege Escalation in case new victim(user B) has more permissions than old victim(user B)

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136/003/)
- **Last Updated**: 2021-07-19

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName = UpdateLoginProfile userAgent !=console.amazonaws.com errorCode = success
| search userIdentity.userName!=requestParameters.userName  
|  stats count min(_time) as firstTime max(_time) as lastTime  by requestParameters.userName src eventName eventSource aws_account_id errorCode userAgent eventID awsRegion userIdentity.userName user_arn 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
|`aws_updateloginprofile_filter`
```
#### Associated Analytic Story

* AWS IAM Privilege Escalation


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* _time

* eventName

* userAgent

* errorCode

* requestParameters.userName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1136.003 | Cloud Account | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
While this search has no known false positives, it is possible that an AWS admin has legitimately created keys for another user.

#### Reference


* https://labs.bishopfox.com/tech-blog/privilege-escalation-in-aws

* https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation-part-2/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078/aws_updateloginprofile/aws_cloudtrail_events.json


_version_: 2
</details>

---

### Abnormally High Number Of Cloud Infrastructure API Calls
This search will detect a spike in the number of API calls made to your cloud infrastructure environment by a user.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078/004/)
- **Last Updated**: 2020-09-07

<details>
  <summary>details</summary>

#### Search
```

| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] 
| where cardinality >=16 
| apply cloud_excessive_api_calls_v1 threshold=0.005 
| rename "IsOutlier(api_calls)" as isOutlier 
| where isOutlier=1 
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
| where api_calls > expected_upper_threshold 
| eval distance_from_threshold = api_calls - expected_upper_threshold 
| table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold 
| `abnormally_high_number_of_cloud_infrastructure_api_calls_filter`
```
#### Associated Analytic Story

* Suspicious Cloud User Activities


#### How To Implement
You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Infrastructure API Calls Per User` to create the probability density function.

#### Required field

* _time

* All_Changes.command

* All_Changes.user

* All_Changes.status



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.004 | Cloud Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives


#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Abnormally High Number Of Cloud Instances Destroyed
This search finds for the number successfully destroyed cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078/004/)
- **Last Updated**: 2020-08-21

<details>
  <summary>details</summary>

#### Search
```

| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| join HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1] 
| where cardinality >=16 
| apply cloud_excessive_instances_destroyed_v1 threshold=0.005 
| rename "IsOutlier(instances_destroyed)" as isOutlier 
| where isOutlier=1 
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
| eval distance_from_threshold = instances_destroyed - expected_upper_threshold 
| table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id 
| `abnormally_high_number_of_cloud_instances_destroyed_filter`
```
#### Associated Analytic Story

* Suspicious Cloud Instance Activities


#### How To Implement
You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Destroyed` to create the probability density function.

#### Required field

* _time

* All_Changes.object_id

* All_Changes.action

* All_Changes.status

* All_Changes.object_category

* All_Changes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.004 | Cloud Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Many service accounts configured within a cloud 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.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Abnormally High Number Of Cloud Instances Launched
This search finds for the number successfully created cloud instances for every 4 hour block. This is split up between weekdays and the weekend. It then applies the probability densitiy model previously created and alerts on any outliers.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078/004/)
- **Last Updated**: 2020-08-21

<details>
  <summary>details</summary>

#### Search
```

| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| join HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] 
| where cardinality >=16 
| apply cloud_excessive_instances_created_v1 threshold=0.005 
| rename "IsOutlier(instances_launched)" as isOutlier 
| where isOutlier=1 
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
| eval distance_from_threshold = instances_launched - expected_upper_threshold 
| table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id 
| `abnormally_high_number_of_cloud_instances_launched_filter`
```
#### Associated Analytic Story

* Cloud Cryptomining

* Suspicious Cloud Instance Activities


#### How To Implement
You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Instances Launched` to create the probability density function.

#### Required field

* _time

* All_Changes.object_id

* All_Changes.action

* All_Changes.status

* All_Changes.object_category

* All_Changes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.004 | Cloud Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Abnormally High Number Of Cloud Security Group API Calls
This search will detect a spike in the number of API calls made to your cloud infrastructure environment about security groups by a user.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078/004/)
- **Last Updated**: 2020-09-07

<details>
  <summary>details</summary>

#### Search
```

| tstats count as security_group_api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.object_category=firewall AND All_Changes.status=success by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| join user HourOfDay isWeekend [ summary cloud_excessive_security_group_api_calls_v1] 
| where cardinality >=16 
| apply cloud_excessive_security_group_api_calls_v1 threshold=0.005 
| rename "IsOutlier(security_group_api_calls)" as isOutlier 
| where isOutlier=1 
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
| where security_group_api_calls > expected_upper_threshold 
| eval distance_from_threshold = security_group_api_calls - expected_upper_threshold 
| table _time, user, command, security_group_api_calls, expected_upper_threshold, distance_from_threshold 
| `abnormally_high_number_of_cloud_security_group_api_calls_filter`
```
#### Associated Analytic Story

* Suspicious Cloud User Activities


#### How To Implement
You must be ingesting your cloud infrastructure logs. You also must run the baseline search `Baseline Of Cloud Security Group API Calls Per User` to create the probability density function model.

#### Required field

* _time

* All_Changes.command

* All_Changes.object_category

* All_Changes.status

* All_Changes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.004 | Cloud Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives


#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Access LSASS Memory for Dump Creation
Detect memory dumping of the LSASS process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003/001/)
- **Last Updated**: 2019-12-06

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=10 TargetImage=*lsass.exe CallTrace=*dbgcore.dll* OR CallTrace=*dbghelp.dll* 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, TargetImage, TargetProcessId, SourceImage, SourceProcessId 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `access_lsass_memory_for_dump_creation_filter` 
```
#### Associated Analytic Story

* Credential Dumping


#### 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.

#### Required field

* _time

* EventCode

* TargetImage

* CallTrace

* Computer

* TargetProcessId

* SourceImage

* SourceProcessId



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.001 | LSASS Memory | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.

#### Reference


* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log


_version_: 2
</details>

---

### Account Discovery With Net App
this search is to detect a potential account discovery series of command used by several malware or attack to recon the target machine. This technique is also seen in some note worthy malware like trickbot where it runs a cmd process, or even drop its module that will execute the said series of net command. This series of command are good correlation search and indicator of attacker recon if seen in the machines within a none technical user or department (HR, finance, ceo and etc) network.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1087.002](https://attack.mitre.org/techniques/T1087/002/)
- **Last Updated**: 2021-05-03

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND (Processes.process="*user*" OR  Processes.process="*config*" OR Processes.process="*view /all*") by  Processes.process_name Processes.dest Processes.user Processes.parent_process_name 
| where count >=5 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `account_discovery_with_net_app_filter`
```
#### Associated Analytic Story

* Trickbot

* Icedid


#### 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. Tune and filter known instances where renamed rundll32.exe may be used.

#### Required field

* _time

* Processes.parent_process_name

* Processes.parent_process_id

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.process_guid

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1087.002 | Domain Account | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
admin or power user may used this series of command.

#### Reference


* https://labs.vipre.com/trickbot-and-its-modules/

* https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html

* https://app.any.run/tasks/48414a33-3d66-4a46-afe5-c2003bb55ccf/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log


_version_: 1
</details>

---

### All backup logs for host
Retrieve the backup logs for the last 2 weeks for a specific host in order to investigate why backups are not completing successfully.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-09-12

<details>
  <summary>details</summary>

#### Search
```

| search `netbackup` dest=$dest$
```
#### Associated Analytic Story

* Monitor Backup Solution


#### How To Implement
The successfully implement this search you must first send your backup logs to Splunk.

#### Required field

* _time

* dest




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Allow File And Printing Sharing In Firewall
This search is to detect a suspicious modification of firewall to allow file and printer sharing. This technique was seen in ransomware to be able to discover more machine connected to the compromised host to encrypt more files

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562/007/)
- **Last Updated**: 2021-06-23

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe Processes.process= "*firewall*" Processes.process= "*group=\"File and Printer Sharing\"*"  Processes.process="*enable=Yes*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `allow_file_and_printing_sharing_in_firewall_filter`
```
#### Associated Analytic Story

* Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id

* Processes.parent_process_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network admin may modify this firewall feature that may cause this rule to be triggered.

#### Reference


* https://kb.fortinet.com/kb/documentLink.do?externalID=FD52469

* https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log


_version_: 1
</details>

---

### Allow Inbound Traffic By Firewall Rule Registry
This analytic detects a potential suspicious modification of firewall rule registry allowing inbound traffic in specific port with public profile. This technique was identified when an adversary wants to grant remote access to a machine by allowing the traffic in a firewall rule.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021/001/)
- **Last Updated**: 2021-05-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\System\\CurrentControlSet\\Services\\SharedAccess\\Parameters\\FirewallPolicy\\FirewallRules\\*" Registry.registry_value_name = "*
|Action=Allow
|*" Registry.registry_value_name = "*
|Dir=In
|*" Registry.registry_value_name = "*
|Profile=Public
|*" Registry.registry_value_name = "*
|LPort=*" by Registry.registry_path Registry.registry_key_name Registry.user Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `allow_inbound_traffic_by_firewall_rule_registry_filter`
```
#### Associated Analytic Story

* Prohibited Traffic Allowed or Protocol Mismatch


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_path

* Registry.registry_value_name

* Registry.registry_key_name

* Registry.dest

* Registry.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.001 | Remote Desktop Protocol | Lateral Movement |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network admin may add/remove/modify public inbound firewall rule that may cause this rule to be triggered.

#### Reference


* https://docs.microsoft.com/en-us/powershell/module/netsecurity/new-netfirewallrule?view=windowsserver2019-ps



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log


_version_: 1
</details>

---

### Allow Inbound Traffic In Firewall Rule
The following analytic identifies suspicious PowerShell command to allow inbound traffic inbound to a specific local port within the public profile. This technique was seen in some attacker want to have a remote access to a machine by allowing the traffic in firewall rule.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021/001/)
- **Last Updated**: 2021-05-19

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message = "*firewall*" Message = "*Public*" Message = "*Inbound*" Message = "*Allow*"  Message = "*-LocalPort*" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `allow_inbound_traffic_in_firewall_rule_filter`
```
#### Associated Analytic Story

* Prohibited Traffic Allowed or Protocol Mismatch


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.

#### Required field

* _time

* EventCode

* Message

* ComputerName

* User



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.001 | Remote Desktop Protocol | Lateral Movement |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
administrator may allow inbound traffic in certain network or machine.

#### Reference


* https://docs.microsoft.com/en-us/powershell/module/netsecurity/new-netfirewallrule?view=windowsserver2019-ps



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-powershell.log


_version_: 1
</details>

---

### Allow Network Discovery In Firewall
This search is to detect a suspicious modification to the firewall to allow network discovery on a machine. This technique was seen in couple of ransomware (revil, reddot) to discover other machine connected to the compromised host to encrypt more files.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562/007/)
- **Last Updated**: 2021-06-23

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe Processes.process= "*firewall*" Processes.process= "*group=\"Network Discovery\"*"  Processes.process="*enable*" Processes.process="*Yes*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `allow_network_discovery_in_firewall_filter`
```
#### Associated Analytic Story

* Ransomware

* Revil Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process

* Processes.parent_process_id

* Processes.parent_process_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network admin may modify this firewall feature that may cause this rule to be triggered.

#### Reference


* https://kb.fortinet.com/kb/documentLink.do?externalID=FD52469

* https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log


_version_: 1
</details>

---

### Allow Operation with Consent Admin
This analytic identifies a potential privilege escalation attempt to perform malicious task. This registry modification is designed to allow the `Consent Admin` to perform an operation that requires elevation without consent or credentials. We also found this in some attacker to gain privilege escalation to the compromise machine.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1548](https://attack.mitre.org/techniques/T1548/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\Microsoft\\Windows\\CurrentVersion\\Policies\\System*" Registry.registry_key_name = ConsentPromptBehaviorAdmin Registry.registry_value_name = "DWORD (0x00000000)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `allow_operation_with_consent_admin_filter`
```
#### Associated Analytic Story

* Ransomware


#### 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 product, 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.

#### Required field

* _time

* Registry.registry_path

* Registry.registry_key_name

* Registry.registry_value_name

* Registry.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548 | Abuse Elevation Control Mechanism | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://docs.microsoft.com/en-us/openspecs/windows_protocols/ms-gpsb/341747f5-6b5d-4d30-85fc-fa1cc04038d4

* https://www.trendmicro.com/vinfo/no/threat-encyclopedia/malware/Ransom.Win32.MRDEC.MRA/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log


_version_: 1
</details>

---

### Amazon EKS Kubernetes Pod scan detection
This search provides detection information on unauthenticated requests against Kubernetes' Pods API

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/)
- **Last Updated**: 2020-04-15

<details>
  <summary>details</summary>

#### Search
```
`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" verb=list objectRef.resource=pods requestURI="/api/v1/pods" 
| rename source as cluster_name sourceIPs{} as src_ip 
| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(responseStatus.code) values(userAgent) values(verb) values(requestURI) by src_ip cluster_name user.username user.groups{} 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `amazon_eks_kubernetes_pod_scan_detection_filter` 
```
#### Associated Analytic Story

* Kubernetes Scanning Activity


#### 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.

#### Required field

* _time

* user.username

* verb

* objectRef.resource

* requestURI

* source

* sourceIPs{}

* responseStatus.reason

* responseStatus.code

* userAgent

* src_ip

* user.groups{}



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1526 | Cloud Service Discovery | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
Not all unauthenticated requests are malicious, but frequency, UA and source IPs and direct request to API provide context.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Amazon EKS Kubernetes activity by src ip
This search provides investigation data about requests via user agent, authentication request URI, verb and cluster name data against Kubernetes cluster from a specific IP address

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-04-13

<details>
  <summary>details</summary>

#### Search
```
`aws_cloudwatchlogs_eks` 
|rename sourceIPs{} as src_ip 
|search src_ip=$src_ip$ 
| stats count min(_time) as firstTime max(_time) as lastTime values(user.username) values(requestURI) values(verb) values(userAgent) by source annotations.authorization.k8s.io/decision src_ip
```
#### Associated Analytic Story

* Kubernetes Scanning Activity


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Cloud Watch EKS inputs.

#### Required field

* _time

* sourceIPs{}

* user.username

* requestURI

* verb

* userAgent

* annotations.authorization.k8s.io/decision




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Amazon EKS Kubernetes cluster scan detection
This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster in AWS

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/)
- **Last Updated**: 2020-04-15

<details>
  <summary>details</summary>

#### Search
```
`aws_cloudwatchlogs_eks` "user.username"="system:anonymous" userAgent!="AWS Security Scanner" 
| rename sourceIPs{} as src_ip 
| stats count min(_time) as firstTime max(_time) as lastTime values(responseStatus.reason) values(source) as cluster_name values(responseStatus.code) values(userAgent) as http_user_agent values(verb) values(requestURI) by src_ip user.username user.groups{} 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
|`amazon_eks_kubernetes_cluster_scan_detection_filter` 
```
#### Associated Analytic Story

* Kubernetes Scanning Activity


#### 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.

#### Required field

* _time

* user.username

* userAgent

* sourceIPs{}

* responseStatus.reason

* source

* responseStatus.code

* verb

* requestURI

* src_ip

* user.groups{}



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1526 | Cloud Service Discovery | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
Not all unauthenticated requests are malicious, but frequency, UA and source IPs will provide context.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Anomalous usage of 7zip
The following detection identifies a 7z.exe spawned from `Rundll32.exe` or `Dllhost.exe`. It is assumed that the adversary has brought in `7z.exe` and `7z.dll`. It has been observed where an adversary will rename `7z.exe`. Additional coverage may be required to identify the behavior of renamed instances of `7z.exe`. During triage, identify the source of injection into `Rundll32.exe` or `Dllhost.exe`. Capture any files written to disk and analyze as needed. Review parallel processes for additional behaviors. Typically, archiving files will result in exfiltration.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1560.001](https://attack.mitre.org/techniques/T1560/001/)
- **Last Updated**: 2021-04-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("rundll32.exe", "dllhost.exe") Processes.process_name=*7z* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `anomalous_usage_of_7zip_filter`
```
#### Associated Analytic Story

* Cobalt Strike

* NOBELIUM Group


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1560.001 | Archive via Utility | Collection |


#### Kill Chain Phase

* Actions on Objective


#### Known False Positives
False positives should be limited as this behavior is not normal for `rundll32.exe` or `dllhost.exe` to spawn and run 7zip.

#### Reference


* https://attack.mitre.org/techniques/T1560/001/

* https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/

* https://thedfirreport.com/2021/01/31/bazar-no-ryuk/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log


_version_: 1
</details>

---

### Any Powershell DownloadFile
The following analytic identifies the use of PowerShell downloading a file using `DownloadFile` method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2021-03-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=powershell.exe OR Processes.process_name=pwsh.exe OR Processes.process_name=PowerShell_ISE.exe) Processes.process=*DownloadFile* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `any_powershell_downloadfile_filter`
```
#### Associated Analytic Story

* Malicious PowerShell

* Ingress Tool Transfer


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives may be present and filtering will need to occur by parent process or command line argument. It may be required to modify this query to an EDR product for more granular coverage.

#### Reference


* https://docs.microsoft.com/en-us/dotnet/api/system.net.webclient.downloadfile?view=net-5.0

* https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Any Powershell DownloadString
The following analytic identifies the use of PowerShell downloading a file using `DownloadString` method. This particular method is utilized in many different PowerShell frameworks to download files and output to disk. Identify the source (IP/domain) and destination file and triage appropriately. If AMSI logging or PowerShell transaction logs are available, review for further details of the implant.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2021-03-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe OR Processes.process_name=pwsh.exe OR Processes.process_name=PowerShell_ISE.exe Processes.process=*.DownloadString*  by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `any_powershell_downloadstring_filter`
```
#### Associated Analytic Story

* Malicious PowerShell

* HAFNIUM Group

* Ingress Tool Transfer


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives may be present and filtering will need to occur by parent process or command line argument. It may be required to modify this query to an EDR product for more granular coverage.

#### Reference


* https://docs.microsoft.com/en-us/dotnet/api/system.net.webclient.downloadstring?view=net-5.0

* https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Applying Stolen Credentials via Mimikatz modules
This detection indicates use of Mimikatz modules that facilitate Pass-the-Token attack, Golden or Silver kerberos ticket attack, and Skeleton key attack.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/), [T1134](https://attack.mitre.org/techniques/T1134/), [T1543](https://attack.mitre.org/techniques/T1543/), [T1547](https://attack.mitre.org/techniques/T1547/), [T1548](https://attack.mitre.org/techniques/T1548/), [T1554](https://attack.mitre.org/techniques/T1554/), [T1556](https://attack.mitre.org/techniques/T1556/), [T1558](https://attack.mitre.org/techniques/T1558/)
- **Last Updated**: 2020-11-03

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)kerberos::ptt/)=true OR match_regex(cmd_line, /(?i)kerberos::golden/)=true OR match_regex(cmd_line, /(?i)kerberos::silver/)=true OR match_regex(cmd_line, /(?i)misc::skeleton/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1098 | Account Manipulation | Persistence |
| T1134 | Access Token Manipulation | Defense Evasion, Privilege Escalation |
| T1543 | Create or Modify System Process | Persistence, Privilege Escalation |
| T1547 | Boot or Logon Autostart Execution | Persistence, Privilege Escalation |
| T1548 | Abuse Elevation Control Mechanism | Privilege Escalation, Defense Evasion |
| T1554 | Compromise Client Software Binary | Persistence |
| T1556 | Modify Authentication Process | Credential Access, Defense Evasion, Persistence |
| T1558 | Steal or Forge Kerberos Tickets | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz

* https://adsecurity.org/?p=1275



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/applying_stolen_credentials/logAllMimikatzModules.log


_version_: 1
</details>

---

### Applying Stolen Credentials via PowerSploit modules
Stolen credentials are applied by methods such as user impersonation, credential injection, spoofing of authentication processes or getting hold of critical accounts. This detection indicates such activities carried out by PowerSploit exploit kit APIs.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/), [T1134](https://attack.mitre.org/techniques/T1134/), [T1543](https://attack.mitre.org/techniques/T1543/), [T1547](https://attack.mitre.org/techniques/T1547/), [T1548](https://attack.mitre.org/techniques/T1548/), [T1554](https://attack.mitre.org/techniques/T1554/), [T1555](https://attack.mitre.org/techniques/T1555/), [T1558](https://attack.mitre.org/techniques/T1558/)
- **Last Updated**: 2020-11-03

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-CredentialInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-TokenManipulation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserImpersonation/)=true OR match_regex(cmd_line, /(?i)Get-System/)=true OR match_regex(cmd_line, /(?i)Invoke-RevertToSelf/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1098 | Account Manipulation | Persistence |
| T1134 | Access Token Manipulation | Defense Evasion, Privilege Escalation |
| T1543 | Create or Modify System Process | Persistence, Privilege Escalation |
| T1547 | Boot or Logon Autostart Execution | Persistence, Privilege Escalation |
| T1548 | Abuse Elevation Control Mechanism | Privilege Escalation, Defense Evasion |
| T1554 | Compromise Client Software Binary | Persistence |
| T1555 | Credentials from Password Stores | Credential Access |
| T1558 | Steal or Forge Kerberos Tickets | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1555/applying_stolen_credentials/logAllPowerSploitModulesWithOldNames.log


_version_: 1
</details>

---

### Assessment of Credential Strength via DSInternals modules
This detection identifies use of DSInternals modules that verify password strength, i.e., identify week accounts that would be easily compromised.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/), [T1087](https://attack.mitre.org/techniques/T1087/), [T1201](https://attack.mitre.org/techniques/T1201/), [T1552](https://attack.mitre.org/techniques/T1552/), [T1555](https://attack.mitre.org/techniques/T1555/)
- **Last Updated**: 2020-11-03

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-PasswordQuality/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1098 | Account Manipulation | Persistence |
| T1087 | Account Discovery | Discovery |
| T1201 | Password Policy Discovery | Discovery |
| T1552 | Unsecured Credentials | Credential Access |
| T1555 | Credentials from Password Stores | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/MichaelGrafnetter/DSInternals



#### Test Dataset


_version_: 1
</details>

---

### Attacker Tools On Endpoint
This search looks for execution of commonly used attacker tools on an endpoint.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1036.005](https://attack.mitre.org/techniques/T1036/005/), [T1595](https://attack.mitre.org/techniques/T1595/), [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2021-06-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process) as process values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes where Processes.dest!=unknown Processes.user!=unknown by Processes.dest Processes.user Processes.process_name Processes.process 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name(Processes)` 
| lookup attacker_tools attacker_tool_names AS process_name OUTPUT description 
| search description=* 
| `attacker_tools_on_endpoint_filter`
```
#### Associated Analytic Story

* Monitor for Unauthorized Software

* XMRig

* SamSam Ransomware

* Unusual Processes


#### 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 product, 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.

#### Required field

* Processes.dest

* Processes.user

* Processes.process_name

* Processes.parent_process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1036.005 | Match Legitimate Name or Location | Defense Evasion |
| T1595 | Active Scanning | Reconnaissance |
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Installation

* Command and Control

* Actions on Objectives


#### Known False Positives
Some administrator activity can be potentially triggered, please add those users to the filter macro.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1595/attacker_scan_tools/windows-sysmon.log


_version_: 1
</details>

---

### Attempt To Add Certificate To Untrusted Store
Attempt To Add Certificate To Untrusted Store

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1553.004](https://attack.mitre.org/techniques/T1553/004/)
- **Last Updated**: 2020-11-03

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=*certutil* (Processes.process=*-addstore*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `attempt_to_add_certificate_to_untrusted_store_filter`
```
#### Associated Analytic Story

* Disabling Security Tools


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1553.004 | Install Root Certificate | Defense Evasion |


#### Kill Chain Phase

* Installation

* Actions on Objectives


#### 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.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1553.004/T1553.004.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1553.004/atomic_red_team/windows-sysmon.log


_version_: 6
</details>

---

### Attempt To Disable Services
This analytic will identify suspicious series of command-line to disable several services. This technique is seen where the adversary attempts to disable security app services or other malware services to complete the objective on the compromised system.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: Endpoint
- **ATT&CK**: [T1489](https://attack.mitre.org/techniques/T1489/)
- **Last Updated**: 2021-06-18

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval _datamodels=ucast(map_get(input_event, "_datamodels"), "collection<string>", []), body={} 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=lower(ucast(map_get(input_event, "process"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line IS NOT NULL AND like(cmd_line, "%disabled%") AND like(cmd_line, "%config%") AND process_name="sc.exe" 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name, "process_path", process_path]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* XMRig

* Ransomware


#### 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. Tune and filter known instances where renamed sc.exe may be used.

#### Required field

* _time

* dest_device_id

* process_name

* parent_process_name

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1489 | Service Stop | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

* https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/ssa_data1/sc_disable.log


_version_: 1
</details>

---

### Attempt To Stop Security Service
This search looks for attempts to stop security-related services on the endpoint.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name = net.exe OR  Processes.process_name = sc.exe) Processes.process="* stop *" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
|lookup security_services_lookup service as process OUTPUTNEW category, description 
| search category=security 
| `attempt_to_stop_security_service_filter`
```
#### Associated Analytic Story

* Disabling Security Tools

* Trickbot


#### 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.,

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Installation

* Actions on Objectives


#### Known False Positives
None identified. Attempts to disable security-related services should be identified and understood.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1562.001/T1562.001.md#atomic-test-14---disable-arbitrary-security-windows-service



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Attempt To delete Services
This analytic identifies suspicious series of attempt to kill multiple services on a system using either `net.exe` or `sc.exe`. This technique is use by adversaries to terminate security services or other related services to continue there objective and evade detections.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: Endpoint
- **ATT&CK**: [T1489](https://attack.mitre.org/techniques/T1489/)
- **Last Updated**: 2021-06-18

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=lower(ucast(map_get(input_event, "process"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line IS NOT NULL AND like(cmd_line, "%delete%") AND process_name = "sc.exe" 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name, "process_path", process_path]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* XMRig

* Ransomware


#### 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. Tune and filter known instances where renamed sc.exe may be used.

#### Required field

* _time

* dest_device_id

* process_name

* parent_process_name

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1489 | Service Stop | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/ssa_data1/sc_del.log


_version_: 1
</details>

---

### Attempted Credential Dump From Registry via Reg exe
Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003/002/)
- **Last Updated**: 2019-12-02

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=reg.exe OR Processes.process_name=cmd.exe) Processes.process=*save* (Processes.process=*HKEY_LOCAL_MACHINE\\Security* OR Processes.process=*HKEY_LOCAL_MACHINE\\SAM* OR Processes.process=*HKEY_LOCAL_MACHINE\\System* OR Processes.process=*HKLM\\Security* OR Processes.process=*HKLM\\System* OR Processes.process=*HKLM\\SAM*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `attempted_credential_dump_from_registry_via_reg_exe_filter`
```
#### Associated Analytic Story

* Credential Dumping

* DarkSide Ransomware


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.002 | Security Account Manager | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.002/T1003.002.md#atomic-test-1---registry-dump-of-sam-creds-and-secrets



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Attempted Credential Dump From Registry via Reg exe
Monitor for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2020-6-04

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where process_name="cmd.exe" OR process_name="reg.exe" 
| where cmd_line != null  AND match_regex(cmd_line, /(?i)save\s+/)=true AND ( match_regex(cmd_line, /(?i)HKLM\\Security/)=true OR match_regex(cmd_line, /(?i)HKLM\\SAM/)=true OR match_regex(cmd_line, /(?i)HKLM\\System/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\Security/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\SAM/)=true OR match_regex(cmd_line, /(?i)HKEY_LOCAL_MACHINE\\System/)=true ) 
| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events(); 
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
You must be ingesting windows endpoint data that tracks process activity, including parent-child relationships from your endpoints.

#### Required field

* process_name

* _time

* dest_device_id

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/splunk/security_content/blob/55a17c65f9f56c2220000b62701765422b46125d/detections/attempted_credential_dump_from_registry_via_reg_exe.yml



#### Test Dataset


_version_: 1
</details>

---

### BCDEdit Failure Recovery Modification
This search looks for flags passed to bcdedit.exe modifications to the built-in Windows error recovery boot configurations. This is typically used by ransomware to prevent recovery.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/)
- **Last Updated**: 2020-12-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*recoveryenabled*" (Processes.process="* no*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `bcdedit_failure_recovery_modification_filter`
```
#### Associated Analytic Story

* Ryuk Ransomware

* Ransomware


#### 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. Tune based on parent process names.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1490 | Inhibit System Recovery | Impact |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Administrators may modify the boot configuration.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md#atomic-test-4---windows---disable-windows-recovery-console-repair



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### BITS Job Persistence
The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` scheduling a BITS job to persist on an endpoint. The query identifies the parameters used to create, resume or add a file to a BITS job. Typically seen combined in a oneliner or ran in sequence. If identified, review the BITS job created and capture any files written to disk. It is possible for BITS to be used to upload files and this may require further network data analysis to identify. You can use `bitsadmin /list /verbose` to list out the jobs during investigation.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1197](https://attack.mitre.org/techniques/T1197/)
- **Last Updated**: 2021-03-29

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=bitsadmin.exe Processes.process IN (*create*, *addfile*, *setnotifyflags*, *setnotifycmdline*, *setminretrydelay*, *setcustomheaders*, *resume* ) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `bits_job_persistence_filter`
```
#### Associated Analytic Story

* BITS Jobs


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1197 | BITS Jobs | Defense Evasion, Persistence |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives will be present. Typically, applications will use `BitsAdmin.exe`. Any filtering should be done based on command-line arguments (legitimate applications) or parent process.

#### Reference


* https://attack.mitre.org/techniques/T1197/

* https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/bitsadmin

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1197/T1197.md#atomic-test-3---persist-download--execute

* https://lolbas-project.github.io/lolbas/Binaries/Bitsadmin/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### BITSAdmin Download File
The following query identifies Microsoft Background Intelligent Transfer Service utility `bitsadmin.exe` using the `transfer` parameter to download a remote object. In addition, look for `download` or `upload` on the command-line, the switches are not required to perform a transfer. Capture any files downloaded. Review the reputation of the IP or domain used. Typically once executed, a follow on command will be used to execute the dropped file. Note that the network connection or file modification events related will not spawn or create from `bitsadmin.exe`, but the artifacts will appear in a parallel process of `svchost.exe` with a command-line similar to `svchost.exe -k netsvcs -s BITS`. It's important to review all parallel and child processes to capture any behaviors and artifacts. In some suspicious and malicious instances, BITS jobs will be created. You can use `bitsadmin /list /verbose` to list out the jobs during investigation.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1197](https://attack.mitre.org/techniques/T1197/), [T1105](https://attack.mitre.org/techniques/T1105/)
- **Last Updated**: 2021-03-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=bitsadmin.exe Processes.process=*transfer* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `bitsadmin_download_file_filter`
```
#### Associated Analytic Story

* Ingress Tool Transfer

* BITS Jobs

* DarkSide Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1197 | BITS Jobs | Defense Evasion, Persistence |
| T1105 | Ingress Tool Transfer | Command And Control |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives, however it may be required to filter based on parent process name or network connection.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/8eb52117b748d378325f7719554a896e37bccec7/atomics/T1105/T1105.md#atomic-test-9---windows---bitsadmin-bits-download

* https://github.com/redcanaryco/atomic-red-team/blob/bc705cb7aaa5f26f2d96585fac8e4c7052df0ff9/atomics/T1197/T1197.md

* https://docs.microsoft.com/en-us/windows/win32/bits/bitsadmin-tool

* https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Baseline Of Cloud Infrastructure API Calls Per User
This search is used to build a Machine Learning Toolkit (MLTK) model for how many API calls are performed by each user. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances created in a small time window.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-09-07

<details>
  <summary>details</summary>

#### Search
```

| tstats count as api_calls from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| table _time api_calls, user, HourOfDay, isWeekend 
| eventstats dc(api_calls) as api_calls by user, HourOfDay, isWeekend 
| where api_calls >= 1 
| fit DensityFunction api_calls by "user,HourOfDay,isWeekend" into cloud_excessive_api_calls_v1 dist=norm show_density=true
```
#### Associated Analytic Story

* Suspicious Cloud User Activities


#### How To Implement
You must have Enterprise Security 6.0 or later, if not you will need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 90 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.

#### Required field

* _time

* All_Changes.user

* All_Changes.status




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline Of Cloud Instances Destroyed
This search is used to build a Machine Learning Toolkit (MLTK) model for how many instances are destroyed in the environment. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances destroyed in a small time window.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-08-25

<details>
  <summary>details</summary>

#### Search
```

| tstats count as instances_destroyed from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance by _time span=1h 
| makecontinuous span=1h _time 
| eval instances_destroyed=coalesce(instances_destroyed, (random()%2)*0.0000000001) 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| table _time instances_destroyed, HourOfDay, isWeekend 
| fit DensityFunction instances_destroyed by "HourOfDay,isWeekend" into cloud_excessive_instances_destroyed_v1 dist=expon show_density=true
```
#### Associated Analytic Story

* Suspicious Cloud Instance Activities

* Cloud Cryptomining


#### How To Implement
You must have Enterprise Security 6.0 or later, if not you will need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.

#### Required field

* _time

* All_Changes.action

* All_Changes.status

* All_Changes.object_category




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline Of Cloud Instances Launched
This search is used to build a Machine Learning Toolkit (MLTK) model for how many instances are created in the environment. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of instances created in a small time window.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-08-14

<details>
  <summary>details</summary>

#### Search
```

| tstats count as instances_launched from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance by _time span=1h 
| makecontinuous span=1h _time 
| eval instances_launched=coalesce(instances_launched, (random()%2)*0.0000000001) 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| table _time instances_launched, HourOfDay, isWeekend 
| fit DensityFunction instances_launched by "HourOfDay,isWeekend" into cloud_excessive_instances_created_v1 dist=expon show_density=true
```
#### Associated Analytic Story

* Cloud Cryptomining

* Suspicious Cloud Instance Activities


#### How To Implement
You must have Enterprise Security 6.0 or later, if not you will need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 90 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.

#### Required field

* _time

* All_Changes.action

* All_Changes.status

* All_Changes.object_category




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline Of Cloud Security Group API Calls Per User
This search is used to build a Machine Learning Toolkit (MLTK) model for how many API calls for security groups are performed by each user. By default, the search uses the last 90 days of data to build the model and the model is rebuilt weekly.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-09-07

<details>
  <summary>details</summary>

#### Search
```

| tstats count as security_group_api_calls from datamodel=Change where All_Changes.object_category=firewall All_Changes.status=success by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| table _time security_group_api_calls, user, HourOfDay, isWeekend 
| eventstats dc(security_group_api_calls) as security_group_api_calls by user, HourOfDay, isWeekend 
| where security_group_api_calls >= 1 
| fit DensityFunction security_group_api_calls by "user,HourOfDay,isWeekend" into cloud_excessive_security_group_api_calls_v1 dist=norm show_density=true
```
#### Associated Analytic Story

* Suspicious Cloud User Activities


#### How To Implement
You must have Enterprise Security 6.0 or later, if not you will need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 90 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.

#### Required field

* _time

* All_Changes.user

* All_Changes.status

* All_Changes.object_category




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline of API Calls per User ARN
This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-04-09

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventType=AwsApiCall 
| spath output=arn path=userIdentity.arn 
| bucket _time span=1h 
| stats count as apiCalls by _time, arn 
| stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn 
| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls 
| outputlookup api_call_by_user_baseline 
| stats count
```
#### Associated Analytic Story

* AWS User Monitoring


#### 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.

#### Required field

* _time

* eventType

* userIdentity.arn




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline of Command Line Length - MLTK
This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the command lines observed for each user in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies outliers in the length of the command line.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2019-05-08

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process 
| `drop_dm_object_name(Processes)` 
| search user!=unknown 
| `security_content_ctime(start_time)`
| `security_content_ctime(end_time)`
| eval processlen=len(process) 
| fit DensityFunction processlen by user into cmdline_pdfmodel
```
#### Associated Analytic Story

* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

* Ransomware

* Suspicious Command-Line Executions

* Suspicious MSHTA Activity

* Unusual Processes


#### How To Implement
You must be ingesting endpoint data and populating the Endpoint data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.

#### Required field

* _time

* Processes.user

* Processes.dest

* Processes.process_name

* Processes.process




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline of DNS Query Length - MLTK
This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the length of the DNS queries for each DNS record type observed in the environment. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search, which uses it to identify outliers in the length of the DNS query.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Resolution
- **ATT&CK**: 
- **Last Updated**: 2019-05-08

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Network_Resolution by DNS.query DNS.record_type 
| search DNS.record_type=* 
| `drop_dm_object_name("DNS")` 
| eval query_length = len(query) 
| fit DensityFunction query_length by record_type into dns_query_pdfmodel
```
#### Associated Analytic Story

* Command and Control

* Hidden Cobra Malware

* Suspicious DNS Traffic


#### How To Implement
To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.

#### Required field

* _time

* DNS.query

* DNS.record_type




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline of Excessive AWS Instances Launched by User - MLTK
This search is used to build a Machine Learning Toolkit (MLTK) model for how many RunInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of RunInstances performed by a user in a small time window.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2019-11-14

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=RunInstances errorCode=success `ec2_excessive_runinstances_mltk_input_filter` 
| bucket span=10m _time 
| stats count as instances_launched by _time src_user 
| fit DensityFunction instances_launched threshold=0.0005 into ec2_excessive_runinstances_v1
```
#### Associated Analytic Story

* AWS Cryptomining

* Suspicious AWS EC2 Activities


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\
In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.

#### Required field

* _time

* eventName

* errorCode

* src_user




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline of Excessive AWS Instances Terminated by User - MLTK
This search is used to build a Machine Learning Toolkit (MLTK) model for how many TerminateInstances users do in the environment. By default, the search uses the last 90 days of data to build the model. The model created by this search is then used in the corresponding detection search, which identifies subsequent outliers in the number of TerminateInstances performed by a user in a small time window.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2019-11-14

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=TerminateInstances errorCode=success `ec2_excessive_terminateinstances_mltk_input_filter` 
| bucket span=10m _time 
| stats count as instances_terminated by _time src_user 
| fit DensityFunction instances_terminated threshold=0.0005 into ec2_excessive_terminateinstances_v1
```
#### Associated Analytic Story

* Suspicious AWS EC2 Activities


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs.\
In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. Depending on the number of users in your environment, you may also need to adjust the value for max_inputs in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data.\
More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.

#### Required field

* _time

* eventName

* errorCode

* src_user




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline of Network ACL Activity by ARN
This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-05-21

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` `network_acl_events` 
| spath output=arn path=userIdentity.arn 
| bucket _time span=1h 
| stats count as apiCalls by _time, arn 
| stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn 
| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls 
| outputlookup network_acl_activity_baseline 
| stats count
```
#### Associated Analytic Story

* AWS Network ACL Activity


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `network_acl_events`.

#### Required field

* _time

* userIdentity.arn




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline of S3 Bucket deletion activity by ARN
This search establishes, on a per-hour basis, the average and standard deviation for the number of API calls related to deleting an S3 bucket by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-07-17

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=DeleteBucket 
| spath output=arn path=userIdentity.arn 
| bucket _time span=1h 
| stats count as apiCalls by _time, arn 
| stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn 
| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls 
| outputlookup s3_deletion_baseline 
| stats count
```
#### Associated Analytic Story

* Suspicious AWS S3 Activities


#### 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.

#### Required field

* _time

* userIdentity.arn




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline of SMB Traffic - MLTK
This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: 
- **Last Updated**: 2019-05-08

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=10m, All_Traffic.src 
| eval HourOfDay=strftime(_time, "%H") 
| eval DayOfWeek=strftime(_time, "%A") 
| `drop_dm_object_name("All_Traffic")` 
| fit DensityFunction count by "HourOfDay,DayOfWeek" into smb_pdfmodel
```
#### Associated Analytic Story

* DHS Report TA18-074A

* Disabling Security Tools

* Emotet Malware  DHS Report TA18-201A 

* Hidden Cobra Malware

* Netsh Abuse

* Ransomware


#### How To Implement
You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, you may consider adding "src" to the by clause, which will build the model for each unique source in your enviornment. However, if you have a large number of hosts in your environment, this search may be very resource intensive. In this case, you may need to raise the value of max_inputs and/or max_groups in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.

#### Required field

* _time

* All_Traffic.dest_port

* All_Traffic.app

* All_Traffic.src




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline of Security Group Activity by ARN
This search establishes, on a per-hour basis, the average and the standard deviation for the number of API calls related to security groups made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-04-17

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` `security_group_api_calls` 
| spath output=arn path=userIdentity.arn 
| bucket _time span=1h 
| stats count as apiCalls by _time, arn 
| stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn 
| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls 
| outputlookup security_group_activity_baseline 
| stats count
```
#### Associated Analytic Story

* AWS User Monitoring


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for security groups, edit the macro `security_group_api_calls`.

#### Required field

* _time

* userIdentity.arn




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Baseline of blocked outbound traffic from AWS
This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-05-07

<details>
  <summary>details</summary>

#### Search
```
`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) 
| bucket _time span=1h 
| stats count as numberOfBlockedConnections by _time, src_ip 
| stats count(numberOfBlockedConnections) as numDataPoints, latest(numberOfBlockedConnections) as latestCount, avg(numberOfBlockedConnections) as avgBlockedConnections, stdev(numberOfBlockedConnections) as stdevBlockedConnections by src_ip 
| table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections 
| outputlookup baseline_blocked_outbound_connections 
| stats count
```
#### Associated Analytic Story

* AWS Network ACL Activity

* Command and Control

* Suspicious AWS Traffic


#### 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.`.

#### Required field

* _time

* action

* src_ip

* dest_ip




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Batch File Write to System32
The search looks for a batch file (.bat) written to the Windows system directory tree.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1204.002](https://attack.mitre.org/techniques/T1204/002/)
- **Last Updated**: 2018-12-14

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)`
| rex field=file_name "(?<file_extension>\.[^\.]+)$" 
| search file_path=*system32* AND file_extension=.bat 
| `batch_file_write_to_system32_filter`
```
#### Associated Analytic Story

* SamSam Ransomware


#### 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.

#### Required field

* _time

* Filesystem.dest

* Filesystem.file_name

* Filesystem.user

* Filesystem.file_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1204.002 | Malicious File | Execution |


#### Kill Chain Phase

* Delivery


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/batch_file_in_system32/windows-sysmon.log


_version_: 1
</details>

---

### CHCP Command Execution
This search is to detect execution of chcp.exe application. this utility is used to change the active code page of the console. This technique was seen in icedid malware to know the locale region/language/country of the compromise host.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/)
- **Last Updated**: 2021-07-27

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=chcp.com Processes.parent_process_name = cmd.exe Processes.parent_process=*/c* by  Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `chcp_command_execution_filter`
```
#### Associated Analytic Story

* Icedid


#### 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. Tune and filter known instances where renamed chcp.com may be used.

#### Required field

* _time

* process_name

* process

* parent_process_name

* parent_process

* process_id

* parent_process_id

* dest

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059 | Command and Scripting Interpreter | Execution |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
other tools or script may used this to change code page to UTF-* or others

#### Reference


* https://ss64.com/nt/chcp.html

* https://twitter.com/tccontre18/status/1419941156633329665?s=20



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log


_version_: 1
</details>

---

### CMD Echo Pipe - Escalation
This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via `jump` (Cobalt Strike PTH) or `getsystem`, using named-pipe impersonation. A suspicious event will look like `cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53`.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059/003/), [T1543.003](https://attack.mitre.org/techniques/T1543/003/)
- **Last Updated**: 2021-05-20

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=cmd.exe OR Processes.process=*%comspec%*) (Processes.process=*echo* AND Processes.process=*pipe*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `cmd_echo_pipe___escalation_filter`
```
#### Associated Analytic Story

* Cobalt Strike


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.003 | Windows Command Shell | Execution |
| T1543.003 | Windows Service | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation

* Privilege Escalation


#### Known False Positives
Unknown. It is possible filtering may be required to ensure fidelity.

#### Reference


* https://redcanary.com/threat-detection-report/threats/cobalt-strike/

* https://github.com/rapid7/meterpreter/blob/master/source/extensions/priv/server/elevate/namedpipe.c



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log


_version_: 1
</details>

---

### CMLUA Or CMSTPLUA UAC Bypass
This analytic detects a potential process using COM Object like CMLUA or CMSTPLUA to bypass UAC. This technique has been used by ransomware adversaries to gain administrative privileges to its running process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.003](https://attack.mitre.org/techniques/T1218/003/)
- **Last Updated**: 2021-05-13

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=7  ImageLoaded IN ("*\\CMLUA.dll", "*\\CMSTPLUA.dll", "*\\CMLUAUTIL.dll") NOT(process_name IN("CMSTP.exe", "CMMGR32.exe")) NOT(Image IN("*\\windows\\*", "*\\program files*")) 
| stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name Computer EventCode Signed ProcessId 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `cmlua_or_cmstplua_uac_bypass_filter`
```
#### Associated Analytic Story

* DarkSide Ransomware

* Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* Image

* ImageLoaded

* process_name

* Computer

* EventCode

* Signed

* ProcessId



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.003 | CMSTP | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Legitimate windows application that are not on the list loading this dll. Filter as needed.

#### Reference


* https://attack.mitre.org/techniques/T1218/003/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/darkside_cmstp_com/windows-sysmon.log


_version_: 1
</details>

---

### CertUtil Download With URLCache and Split Arguments
Certutil.exe may download a file from a remote destination using `-urlcache`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. However, it is uncommon for `certutil.exe` to write files to world writeable paths.\ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1105](https://attack.mitre.org/techniques/T1105/)
- **Last Updated**: 2021-03-23

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process=*urlcache* Processes.process=*split* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `certutil_download_with_urlcache_and_split_arguments_filter`
```
#### Associated Analytic Story

* Ingress Tool Transfer

* DarkSide Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1105 | Ingress Tool Transfer | Command And Control |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.

#### Reference


* https://attack.mitre.org/techniques/T1105/

* https://www.avira.com/en/blog/certutil-abused-by-attackers-to-spread-threats

* https://www.fireeye.com/blog/threat-research/2019/10/certutil-qualms-they-came-to-drop-fombs.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### CertUtil Download With VerifyCtl and Split Arguments
Certutil.exe may download a file from a remote destination using `-VerifyCtl`. This behavior does require a URL to be passed on the command-line. In addition, `-f` (force) and `-split` (Split embedded ASN.1 elements, and save to files) will be used. It is not entirely common for `certutil.exe` to contact public IP space. \ During triage, capture any files on disk and review. Review the reputation of the remote IP or domain in question. Using `-VerifyCtl`, the file will either be written to the current working directory or `%APPDATA%\..\LocalLow\Microsoft\CryptnetUrlCache\Content\<hash>`. 

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1105](https://attack.mitre.org/techniques/T1105/)
- **Last Updated**: 2021-03-23

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process=*verifyctl* Processes.process=*split* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `certutil_download_with_verifyctl_and_split_arguments_filter`
```
#### Associated Analytic Story

* Ingress Tool Transfer

* DarkSide Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1105 | Ingress Tool Transfer | Command And Control |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives in most environments, however tune as needed based on parent-child relationship or network connection.

#### Reference


* https://attack.mitre.org/techniques/T1105/

* https://www.hexacorn.com/blog/2020/08/23/certutil-one-more-gui-lolbin/

* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc732443(v=ws.11)#-verifyctl

* https://www.avira.com/en/blog/certutil-abused-by-attackers-to-spread-threats



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1105/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### CertUtil With Decode Argument
CertUtil.exe may be used to `encode` and `decode` a file, including PE and script code. Encoding will convert a file to base64 with `-----BEGIN CERTIFICATE-----` and `-----END CERTIFICATE-----` tags. Malicious usage will include decoding a encoded file that was downloaded. Once decoded, it will be loaded by a parallel process. Note that there are two additional command switches that may be used - `encodehex` and `decodehex`. Similarly, the file will be encoded in HEX and later decoded for further execution. During triage, identify the source of the file being decoded. Review its contents or execution behavior for further analysis.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1140](https://attack.mitre.org/techniques/T1140/)
- **Last Updated**: 2021-03-23

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process=*decode* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `certutil_with_decode_argument_filter`
```
#### Associated Analytic Story

* Deobfuscate-Decode Files or Information


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1140 | Deobfuscate/Decode Files or Information | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Typically seen used to `encode` files, but it is possible to see legitimate use of `decode`. Filter based on parent-child relationship, file paths, endpoint or user.

#### Reference


* https://attack.mitre.org/techniques/T1140/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1140/T1140.md

* https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/certutil

* https://www.bleepingcomputer.com/news/security/certutilexe-could-allow-attackers-to-download-malware-while-bypassing-av/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1140/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Certutil exe certificate extraction
This search looks for arguments to certutil.exe indicating the manipulation or extraction of Certificate. This certificate can then be used to sign new authentication tokens specially inside Federated environments such as Windows ADFS.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2021-01-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe Processes.process = "* -exportPFX *" Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `certutil_exe_certificate_extraction_filter`
```
#### Associated Analytic Story

* Windows Persistence Techniques

* Cloud Federated Credential Abuse


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id




#### Kill Chain Phase

* Installation


#### Known False Positives
Unless there are specific use cases, manipulating or exporting certificates using certutil is uncommon. Extraction of certificate has been observed during attacks such as Golden SAML and other campaigns targeting Federated services.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/certutil_exe_certificate_extraction/windows-sysmon.log


_version_: 1
</details>

---

### Child Processes of Spoolsv exe
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/)
- **Last Updated**: 2020-03-16

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `child_processes_of_spoolsv_exe_filter` 
```
#### Associated Analytic Story

* Windows Privilege Escalation


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.process_name

* Processes.dest

* Processes.parent_process

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### 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.

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Clear Unallocated Sector Using Cipher App
this search is to detect execution of `cipher.exe` to clear the unallocated sectors of a specific disk. This technique was seen in some ransomware to make it impossible to forensically recover deleted files.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1070.004](https://attack.mitre.org/techniques/T1070/004/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cipher.exe" Processes.process = "*/w:*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `clear_unallocated_sector_using_cipher_app_filter`
```
#### Associated Analytic Story

* Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070.004 | File Deletion | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
administrator may execute this app to manage disk

#### Reference


* https://unit42.paloaltonetworks.com/vatet-pyxie-defray777/3/

* https://www.sophos.com/en-us/medialibrary/PDFs/technical-papers/sophoslabs-ransomware-behavior-report.pdf



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log


_version_: 1
</details>

---

### Clop Common Exec Parameter
The following analytics are designed to identifies some CLOP ransomware variant that using arguments to execute its main code or feature of its code. In this variant if the parameter is "runrun", CLOP ransomware will try to encrypt files in network shares and if it is "temp.dat", it will try to read from some stream pipe or file start encrypting files within the infected local machines. This technique can be also identified as an anti-sandbox technique to make its code non-responsive since it is waiting for some parameter to execute properly.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1204](https://attack.mitre.org/techniques/T1204/)
- **Last Updated**: 2021-03-17

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name != "*temp.dat*" Processes.process = "*runrun*" OR Processes.process = "*temp.dat*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `clop_common_exec_parameter_filter`
```
#### Associated Analytic Story

* Clop Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1204 | User Execution | Execution |


#### Kill Chain Phase

* Obfuscation


#### Known False Positives
Operators can execute third party tools using these parameters.

#### Reference


* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_b/windows-sysmon.log


_version_: 1
</details>

---

### Clop Ransomware Known Service Name
This detection is to identify the common service name created by the CLOP ransomware as part of its persistence and high privilege code execution in the infected machine. Ussually CLOP ransomware use StartServiceCtrlDispatcherW API in creating this service entry.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1543](https://attack.mitre.org/techniques/T1543/)
- **Last Updated**: 2021-03-17

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_system` EventCode=7045 Service_Name IN ("SecurityCenterIBM", "WinCheckDRVs") 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `clop_ransomware_known_service_name_filter`
```
#### Associated Analytic Story

* Clop Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.

#### Required field

* EventCode

* cmdline

* _time

* parent_process_name

* process_name

* OriginalFileName

* process_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1543 | Create or Modify System Process | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Privilege Escalation


#### Known False Positives
unknown

#### Reference


* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-system.log


_version_: 1
</details>

---

### Cloud API Calls From Previously Unseen User Roles
This search looks for new commands from each user role.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2020-09-04

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command All_Changes.object 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_api_calls_per_user_role user as user, command as command OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenUserApiCall=min(firstTimeSeen) 
| where isnull(firstTimeSeenUserApiCall) OR firstTimeSeenUserApiCall > relative_time(now(),"-24h@h") 
| table firstTime, user, object, command 
|`security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `cloud_api_calls_from_previously_unseen_user_roles_filter`
```
#### Associated Analytic Story

* Suspicious Cloud User Activities


#### How To Implement
You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud API Calls Per User Role - Initial` to build the initial table of user roles, commands, and times. You must also enable the second baseline search `Previously Seen Cloud API Calls Per User Role - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `cloud_api_calls_from_previously_unseen_user_roles_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_api_calls_from_previously_unseen_user_roles_filter`

#### Required field

* _time

* All_Changes.user

* All_Changes.user_type

* All_Changes.status

* All_Changes.command

* All_Changes.object



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### Known False Positives
.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Cloud Compute Instance Created By Previously Unseen User
This search looks for cloud compute instances created by users who have not created them before.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078/004/)
- **Last Updated**: 2021-07-13

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object) as dest from datamodel=Change where All_Changes.action=created by All_Changes.user All_Changes.vendor_region 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_compute_creations_by_user user as user OUTPUTNEW firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenUser=min(firstTimeSeen) 
| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") 
| table firstTime, user, dest, count vendor_region 
| `security_content_ctime(firstTime)` 
| `cloud_compute_instance_created_by_previously_unseen_user_filter`
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting the appropriate cloud-infrastructure logs Run the "Previously Seen Cloud Compute Creations By User" support search to create of baseline of previously seen users.

#### Required field

* _time

* All_Changes.object

* All_Changes.action

* All_Changes.user

* All_Changes.vendor_region



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.004 | Cloud Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 2
</details>

---

### Cloud Compute Instance Created In Previously Unused Region
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.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/)
- **Last Updated**: 2020-09-02

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region, All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_regions vendor_region as vendor_region OUTPUTNEW firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenRegion=min(firstTimeSeen) 
| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), "-24h@h") 
| table firstTime, user, dest, count , vendor_region 
| `security_content_ctime(firstTime)` 
| `cloud_compute_instance_created_in_previously_unused_region_filter`
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Regions - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Regions - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_in_previously_unused_region_filter` macro.

#### Required field

* _time

* All_Changes.object_id

* All_Changes.action

* All_Changes.vendor_region

* All_Changes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Cloud Compute Instance Created With Previously Unseen Image
This search looks for cloud compute instances being created with previously unseen image IDs.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2018-10-12

<details>
  <summary>details</summary>

#### Search
```

| tstats count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id, All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| `drop_dm_object_name("Instance_Changes")` 
| where image_id != "unknown" 
| lookup previously_seen_cloud_compute_images image_id as image_id OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenImage=min(firstTimeSeen) 
| where isnull(firstTimeSeenImage) OR firstTimeSeenImage > relative_time(now(), "-24h@h") 
| table firstTime, user, image_id, count, dest 
| `security_content_ctime(firstTime)` 
| `cloud_compute_instance_created_with_previously_unseen_image_filter`
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Images - Initial` to build the initial table of images observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Images - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_image_filter` macro.

#### Required field

* _time

* All_Changes.object_id

* All_Changes.action

* All_Changes.Instance_Changes.image_id

* All_Changes.user




#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Cloud Compute Instance Created With Previously Unseen Instance Type
Find EC2 instances being created with previously unseen instance types.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-09-12

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| `drop_dm_object_name("Instance_Changes")` 
| where instance_type != "unknown" 
| lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenInstanceType=min(firstTimeSeen) 
| where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") 
| table firstTime, user, dest, count, instance_type 
| `security_content_ctime(firstTime)` 
| `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search `Previously Seen Cloud Compute Instance Types - Initial` to build the initial table of instance types observed and times. You must also enable the second baseline search `Previously Seen Cloud Compute Instance Types - Update` to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the `cloud_compute_instance_created_with_previously_unseen_instance_type_filter` macro.

#### Required field

* _time

* All_Changes.object_id

* All_Changes.action

* All_Changes.Instance_Changes.instance_type

* All_Changes.user




#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Cloud Instance Modified By Previously Unseen User
This search looks for cloud instances being modified by users who have not previously modified them.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078.004](https://attack.mitre.org/techniques/T1078/004/)
- **Last Updated**: 2020-07-29

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as object_id values(All_Changes.command) as command from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_instance_modifications_by_user user as user OUTPUTNEW firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenUser=min(firstTimeSeen) 
| where isnull(firstTimeSeenUser) OR firstTimeSeenUser > relative_time(now(), "-24h@h") 
| table firstTime user command object_id count 
| `security_content_ctime(firstTime)` 
| `cloud_instance_modified_by_previously_unseen_user_filter`
```
#### Associated Analytic Story

* Suspicious Cloud Instance Activities


#### How To Implement
This search has a dependency on other searches to create and update a baseline of users observed to be associated with this activity. The search "Previously Seen Cloud Instance Modifications By User - Update" should be enabled for this detection to properly work.

#### Required field

* _time

* All_Changes.object_id

* All_Changes.command

* All_Changes.action

* All_Changes.change_type

* All_Changes.status

* All_Changes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.004 | Cloud Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Cloud Provisioning Activity From Previously Unseen City
This search looks for cloud provisioning activities from previously unseen cities. Provisioning activities are defined broadly as any event that runs or creates something.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2020-10-09

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| iplocation src 
| where isnotnull(City) 
| lookup previously_seen_cloud_provisioning_activity_sources City as City OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenCity=min(firstTimeSeen) 
| where isnull(firstTimeSeenCity) OR firstTimeSeenCity > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) 
| table firstTime, src, City, user, object, command 
| `cloud_provisioning_activity_from_previously_unseen_city_filter` 
| `security_content_ctime(firstTime)`
```
#### Associated Analytic Story

* Suspicious Cloud Provisioning Activities


#### How To Implement
You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_city_filter` macro.

#### Required field

* _time

* All_Changes.action

* All_Changes.status

* All_Changes.src

* All_Changes.user

* All_Changes.object

* All_Changes.command



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Cloud Provisioning Activity From Previously Unseen Country
This search looks for cloud provisioning activities from previously unseen countries. Provisioning activities are defined broadly as any event that runs or creates something.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2020-10-09

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| iplocation src 
| where isnotnull(Country) 
| lookup previously_seen_cloud_provisioning_activity_sources Country as Country OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenCountry=min(firstTimeSeen) 
| where isnull(firstTimeSeenCountry) OR firstTimeSeenCountry > relative_time(now(), "-24h@h") 
| table firstTime, src, Country, user, object, command 
| `cloud_provisioning_activity_from_previously_unseen_country_filter` 
| `security_content_ctime(firstTime)`
```
#### Associated Analytic Story

* Suspicious Cloud Provisioning Activities


#### How To Implement
You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_country_filter` macro.

#### Required field

* _time

* All_Changes.action

* All_Changes.status

* All_Changes.src

* All_Changes.user

* All_Changes.object

* All_Changes.command



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Cloud Provisioning Activity From Previously Unseen IP Address
This search looks for cloud provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that runs or creates something.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2020-08-16

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime, latest(_time) as lastTime, values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| lookup previously_seen_cloud_provisioning_activity_sources src as src OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenSrc=min(firstTimeSeen) 
| where isnull(firstTimeSeenSrc) OR firstTimeSeenSrc > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) 
| table firstTime, src, user, object_id, command 
| `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` 
| `security_content_ctime(firstTime)`
```
#### Associated Analytic Story

* Suspicious Cloud Provisioning Activities


#### How To Implement
You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_ip_address_filter` macro.

#### Required field

* _time

* All_Changes.object_id

* All_Changes.action

* All_Changes.status

* All_Changes.src

* All_Changes.user

* All_Changes.command



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Cloud Provisioning Activity From Previously Unseen Region
This search looks for cloud provisioning activities from previously unseen regions. Provisioning activities are defined broadly as any event that runs or creates something.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2020-08-16

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime, latest(_time) as lastTime from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src, All_Changes.user, All_Changes.object, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| iplocation src 
| where isnotnull(Region) 
| lookup previously_seen_cloud_provisioning_activity_sources Region as Region OUTPUT firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenRegion=min(firstTimeSeen) 
| where isnull(firstTimeSeenRegion) OR firstTimeSeenRegion > relative_time(now(), `previously_unseen_cloud_provisioning_activity_window`) 
| table firstTime, src, Region, user, object, command 
| `cloud_provisioning_activity_from_previously_unseen_region_filter` 
| `security_content_ctime(firstTime)`
```
#### Associated Analytic Story

* Suspicious Cloud Provisioning Activities


#### How To Implement
You must be ingesting your cloud infrastructure logs from your cloud provider.  You should run the baseline search `Previously Seen Cloud Provisioning Activity Sources - Initial` to build the initial table of source IP address, geographic locations, and times. You must also enable the second baseline search `Previously Seen Cloud Provisioning Activity Sources - Update` to keep this table up to date and to age out old data. You can adjust the time window for this search by updating the `previously_unseen_cloud_provisioning_activity_window` macro. You can also provide additional filtering for this search by customizing the `cloud_provisioning_activity_from_previously_unseen_region_filter` macro.

#### Required field

* _time

* All_Changes.action

* All_Changes.status

* All_Changes.src

* All_Changes.user

* All_Changes.object

* All_Changes.command



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Cobalt Strike Named Pipes
The following analytic identifies the use of default or publicly known named pipes used with Cobalt Strike. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. Cobalt Strike uses named pipes in many ways and has default values used with the Artifact Kit and Malleable C2 Profiles. The following query assists with identifying these default named pipes. Each EDR product presents named pipes a little different. Consider taking the values and generating a query based on the product of choice. \
Upon triage, review the process performing the named pipe. If it is explorer.exe, It is possible it was injected into by another process. Review recent parallel processes to identify suspicious patterns or behaviors. A parallel process may have a network connection, review and follow the connection back to identify any file modifications.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-02-22

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=17 OR EventID=18 PipeName IN (\\msagent_*, \\wkssvc*, \\DserNamePipe*, \\srvsvc_*, \\mojo.*, \\postex_*, \\status_*, \\MSSE-*, \\spoolss_*, \\win_svc*, \\ntsvcs*, \\winsock*, \\UIA_PIPE*) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, process_name, process_id process_path, PipeName 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `cobalt_strike_named_pipes_filter`
```
#### Associated Analytic Story

* Cobalt Strike

* Trickbot

* DarkSide Ransomware


#### 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.

#### Required field

* _time

* EventID

* PipeName

* Computer

* process_name

* process_path

* process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
The idea of using named pipes with Cobalt Strike is to blend in. Therefore, some of the named pipes identified and added may cause false positives. Filter by process name or pipe name to reduce false positives.

#### Reference


* https://attack.mitre.org/techniques/T1218/009/

* https://docs.microsoft.com/en-us/windows/win32/ipc/named-pipes

* https://www.cobaltstrike.com/help-smb-beacon

* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/

* https://gist.github.com/MHaggis/6c600e524045a6d49c35291a21e10752

* https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log


_version_: 1
</details>

---

### Common Ransomware Extensions
The search looks for file modifications with extensions commonly used by Ransomware

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1485](https://attack.mitre.org/techniques/T1485/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)`
| rex field=file_name "(?<file_extension>\.[^\.]+)$" 
| `ransomware_extensions` 
| `common_ransomware_extensions_filter`
```
#### Associated Analytic Story

* SamSam Ransomware

* Ryuk Ransomware

* Ransomware

* Clop Ransomware


#### How To Implement
You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.\
This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** Name, **Field:** Name\
1. \
1. **Label:** File Extension, **Field:** file_extension\
Detailed documentation on how to create a new field within Incident Review may be found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`

#### Required field

* _time

* Filesystem.user

* Filesystem.dest

* Filesystem.file_path

* Filesystem.file_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1485 | Data Destruction | Impact |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_extensions/windows-sysmon.log


_version_: 4
</details>

---

### Common Ransomware Notes
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1485](https://attack.mitre.org/techniques/T1485/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `ransomware_notes` 
| `common_ransomware_notes_filter`
```
#### Associated Analytic Story

* SamSam Ransomware

* Ransomware

* Ryuk Ransomware

* Clop Ransomware


#### 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 product, 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.

#### Required field

* _time

* Filesystem.user

* Filesystem.dest

* Filesystem.file_path

* Filesystem.file_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1485 | Data Destruction | Impact |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
It's possible that a legitimate file could be created with the same name used by ransomware note files.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1485/ransomware_notes/windows-sysmon.log


_version_: 4
</details>

---

### Conti Common Exec parameter
This search detects the suspicious commandline argument of revil ransomware to encrypt specific or all local drive and network shares of the compromised machine or host.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1204](https://attack.mitre.org/techniques/T1204/)
- **Last Updated**: 2021-06-02

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*-m local*" OR Processes.process = "*-m net*" OR Processes.process = "*-m all*" OR Processes.process = "*-nomutex*" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `conti_common_exec_parameter_filter`
```
#### Associated Analytic Story

* Ransomware


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1204 | User Execution | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
3rd party tool may have commandline parameter that can trigger this detection.

#### Reference


* https://malpedia.caad.fkie.fraunhofer.de/details/win.conti



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/conti/inf1/windows-sysmon.log


_version_: 1
</details>

---

### Count of Unique IPs Connecting to Ports
The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: 
- **Last Updated**: 2017-09-13

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port 
| `drop_dm_object_name("All_Traffic")` 
| sort - count
```
#### Associated Analytic Story


#### How To Implement
To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model.

#### Required field

* _time

* All_Traffic.dest_port

* All_Traffic.src




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Count of assets by category
This search shows you every asset category you have and the assets that belong to those categories.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-09-13

<details>
  <summary>details</summary>

#### Search
```

| from datamodel Identity_Management.All_Assets 
| stats count values(nt_host) by category 
| sort -count
```
#### Associated Analytic Story

* Asset Tracking


#### How To Implement
To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated.

#### Required field

* _time

* Identity_Management.All_Assets

* category




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Create Remote Thread In Shell Application
This search is to detect suspicious process injection in command shell. This technique was seen in IcedID where it execute cmd.exe process to inject its shellcode as part of its execution as banking trojan. It is really uncommon to have a create remote thread execution in the following application.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-08-04

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=8 TargetImage IN ("*\\cmd.exe", "*\\powershell*") 
| stats count min(_time) as firstTime max(_time) as lastTime by  TargetImage TargetProcessId SourceProcessId  EventCode StartAddress SourceImage Computer 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `create_remote_thread_in_shell_application_filter`
```
#### Associated Analytic Story

* Icedid


#### 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.

#### Required field

* _time

* SourceImage

* TargetImage

* TargetProcessId

* SourceProcessId

* StartAddress

* EventCode

* Computer



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://thedfirreport.com/2021/07/19/icedid-and-cobalt-strike-vs-antivirus/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Create Remote Thread into LSASS
Detect remote thread creation into LSASS consistent with credential dumping.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003/001/)
- **Last Updated**: 2019-12-06

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=8 TargetImage=*lsass.exe 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, EventCode, TargetImage, TargetProcessId 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `create_remote_thread_into_lsass_filter`
```
#### Associated Analytic Story

* 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.

#### Required field

* _time

* EventID

* TargetImage

* Computer

* EventCode

* TargetImage

* TargetProcessId

* dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.001 | LSASS Memory | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Create Service In Suspicious File Path
This detection is to identify a creation of "user mode service" where the service file path is located in non-common service folder in windows.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1569.002](https://attack.mitre.org/techniques/T1569/002/)
- **Last Updated**: 2021-03-12

<details>
  <summary>details</summary>

#### Search
```
 `wineventlog_system` EventCode=7045  Service_File_Name = "*\.exe" NOT (Service_File_Name IN ("C:\\Windows\\*", "C:\\Program File*", "C:\\Programdata\\*", "%systemroot%\\*")) Service_Type = "user mode service" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `create_service_in_suspicious_file_path_filter`
```
#### Associated Analytic Story

* Clop Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.

#### Required field

* EventCode

* Service_File_Name

* Service_Type

* _time

* Service_Name

* Service_Start_Type



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1569.002 | Service Execution | Execution |


#### Kill Chain Phase

* Privilege Escalation


#### Known False Positives
unknown

#### Reference


* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-system.log


_version_: 1
</details>

---

### Create a list of approved AWS service accounts
This search looks for successful API activity in CloudTrail within the last 30 days, filters out known users from the identity table, and outputs values of users into `aws_service_accounts.csv` lookup file.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-12-03

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` errorCode=success 
| rename userName as identity 
| search NOT [inputlookup identity_lookup_expanded 
| fields identity] 
| stats count by identity 
| table identity 
| outputlookup aws_service_accounts 
| stats count
```
#### Associated Analytic Story

* AWS User Monitoring


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the service account entires in `aws_service_accounts.csv`, which is a lookup file created as a result of running this support search. Please remove the entries of service accounts that are not legitimate.

#### Required field

* _time

* errorCode

* userName




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Create local admin accounts using net exe
This search looks for the creation of local administrator accounts using net.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1136.001](https://attack.mitre.org/techniques/T1136/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=net.exe OR Processes.process_name=net1.exe) AND (Processes.process=*localgroup* OR Processes.process=*/add* OR Processes.process=*user*) by Processes.process Processes.process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|`create_local_admin_accounts_using_net_exe_filter` 
```
#### Associated Analytic Story

* DHS Report TA18-074A


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1136.001 | Local Account | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Administrators often leverage net.exe to create admin accounts.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Create or delete windows shares using net exe
This search looks for the creation or deletion of hidden shares using net.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1070.005](https://attack.mitre.org/techniques/T1070/005/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| search process=*share* 
| `create_or_delete_windows_shares_using_net_exe_filter` 
```
#### Associated Analytic Story

* Hidden Cobra Malware


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070.005 | Network Share Connection Removal | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


* https://attack.mitre.org/techniques/T1070/005



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.005/atomic_red_team/windows-sysmon.log


_version_: 5
</details>

---

### Creation of Shadow Copy
Monitor for signs that Vssadmin or Wmic has been used to create a shadow copy.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003/003/)
- **Last Updated**: 2019-12-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe Processes.process=*create* Processes.process=*shadow*) OR (Processes.process_name=wmic.exe Processes.process=*shadowcopy* Processes.process=*create*) by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `creation_of_shadow_copy_filter`
```
#### Associated Analytic Story

* Credential Dumping


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.003 | NTDS | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Legitimate administrator usage of Vssadmin or Wmic will create false positives.

#### Reference


* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Creation of Shadow Copy with wmic and powershell
This search detects the use of wmic and Powershell to create a shadow copy.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003/003/)
- **Last Updated**: 2019-12-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic* OR Processes.process_name=powershell* Processes.process=*shadowcopy* Processes.process=*create* by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `creation_of_shadow_copy_with_wmic_and_powershell_filter`
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.003 | NTDS | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Legtimate administrator usage of wmic to create a shadow copy.

#### Reference


* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Creation of lsass Dump with Taskmgr
Detect the hands on keyboard behavior of Windows Task Manager creating a process dump of lsass.exe. Upon this behavior occurring, a file write/modification will occur in the users profile under \AppData\Local\Temp. The dump file, lsass.dmp, cannot be renamed, however if the dump occurs more than once, it will be named lsass (2).dmp.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003/001/)
- **Last Updated**: 2020-02-03

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=11 process_name=taskmgr.exe TargetFilename=*lsass*.dmp 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, object_category, process_name, TargetFilename 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `creation_of_lsass_dump_with_taskmgr_filter`
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
This search requires Sysmon Logs and a Sysmon configuration, which includes EventCode 11 for detecting file create of lsass.dmp. 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.

#### Required field

* _time

* EventID

* process_name

* TargetFilename

* Computer

* object_category



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.001 | LSASS Memory | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Administrators can create memory dumps for debugging purposes, but memory dumps of the LSASS process would be unusual.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-5---dump-lsassexe-memory-using-windows-task-manager

* https://attack.mitre.org/techniques/T1003/001/

* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Credential Dumping via Copy Command from Shadow Copy
This search detects credential dumping using copy command from a shadow copy.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003/003/)
- **Last Updated**: 2019-12-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process=*\\system32\\config\\sam* OR Processes.process=*\\system32\\config\\security* OR Processes.process=*\\system32\\config\\system* OR Processes.process=*\\windows\\ntds\\ntds.dit*) by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `credential_dumping_via_copy_command_from_shadow_copy_filter` 
```
#### Associated Analytic Story

* Credential Dumping


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.003 | NTDS | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
unknown

#### Reference


* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Credential Dumping via Symlink to Shadow Copy
This search detects the creation of a symlink to a shadow copy.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003/003/)
- **Last Updated**: 2019-12-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*mklink* Processes.process=*HarddiskVolumeShadowCopy* by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `credential_dumping_via_symlink_to_shadow_copy_filter` 
```
#### Associated Analytic Story

* Credential Dumping


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.003 | NTDS | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
unknown

#### Reference


* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Credential Extraction indicative of FGDump and CacheDump with s option
Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2020-10-18

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND process_name != null AND parent_process_name != null AND match_regex(parent_process_name, /(?i)System32\\services.exe/)=true AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-s/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Unusual Processes

* Credential Dumping


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* process_name

* parent_process_name

* _time

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logFgdump.log


_version_: 1
</details>

---

### Credential Extraction indicative of FGDump and CacheDump with v option
Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. FGdump is a newer version of pwdump tool that extracts NTLM and LanMan password hashes from Windows. Cachedump is a publicly-available tool that extracts cached password hashes from a system's registry.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2020-10-18

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND process_name != null AND process_path != null AND match_regex(process_name, /(?i)cachedump\d{0,2}.exe/)=true AND match_regex(process_path, /(?i)\\Temp/)=true AND match_regex(cmd_line, /(?i)\-v/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Unusual Processes

* Credential Dumping


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* process_name

* _time

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logFgdump.log


_version_: 1
</details>

---

### Credential Extraction indicative of Lazagne command line options
Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. LaZagne is a tool that extracts various kinds of credentials from a local computer, including account passwords, domain passwords, browser passwords, etc.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/), [T1555](https://attack.mitre.org/techniques/T1555/)
- **Last Updated**: 2020-10-18

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND match_regex(cmd_line, /(?i)all\s+\-oA\s+\-output/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |
| T1555 | Credentials from Password Stores | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logLazagneCredDump.log


_version_: 1
</details>

---

### Credential Extraction indicative of use of DSInternals credential conversion modules
Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2020-10-21

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)ConvertFrom-ADManagedPasswordBlob/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertFrom-UnicodePassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-GPPrefPassword/)=true OR match_regex(cmd_line, /(?i)ConvertTo-KerberosKey/)=true OR match_regex(cmd_line, /(?i)ConvertTo-LMHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-NTHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-OrgIdHash/)=true OR match_regex(cmd_line, /(?i)ConvertTo-UnicodePassword/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping

* Malicious PowerShell


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* process_name

* parent_process_name

* _time

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/MichaelGrafnetter/DSInternals



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllDSInternalsModules.log


_version_: 1
</details>

---

### Credential Extraction indicative of use of DSInternals modules
Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. DSInternals is a collection of PowerShell modules commonly employed in exploits.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2020-10-21

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ADDBBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Get-ADDBKdsRootKey/)=true OR match_regex(cmd_line, /(?i)Get-ADDBSchemaAttribute/)=true OR match_regex(cmd_line, /(?i)Get-ADKeyCredential/)=true OR match_regex(cmd_line, /(?i)Get-ADReplAccount/)=true OR match_regex(cmd_line, /(?i)Get-ADReplBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-ADSIAccount/)=true OR match_regex(cmd_line, /(?i)Get-AzureADUserEx/)=true OR match_regex(cmd_line, /(?i)Get-BootKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaBackupKey/)=true OR match_regex(cmd_line, /(?i)Get-LsaPolicyInformation/)=true OR match_regex(cmd_line, /(?i)Get-SamPasswordPolicy/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping

* Malicious PowerShell


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* process_name

* parent_process_name

* _time

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/MichaelGrafnetter/DSInternals



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllDSInternalsModules.log


_version_: 1
</details>

---

### Credential Extraction indicative of use of Mimikatz modules
Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Mimikatz is a collection of tools and modules commonly employed in Windows exploits.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2020-10-21

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)CRYPTO::Certificates/)=true OR match_regex(cmd_line, /(?i)CRYPTO::keys/)=true OR match_regex(cmd_line, /(?i)kerberos::list/)=true OR match_regex(cmd_line, /(?i)kerberos::tgt/)=true OR match_regex(cmd_line, /(?i)lsadump::sam/)=true OR match_regex(cmd_line, /(?i)lsadump::secrets/)=true OR match_regex(cmd_line, /(?i)lsadump::cache/)=true OR match_regex(cmd_line, /(?i)lsadump::lsa/)=true OR match_regex(cmd_line, /(?i)lsadump::trust/)=true OR match_regex(cmd_line, /(?i)lsadump::backupkeys/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping

* Unusual Processes


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllMimikatzModules.log


_version_: 1
</details>

---

### Credential Extraction indicative of use of PowerSploit modules
Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit is a collection of Microsoft PowerShell modules commonly employed in exploits.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2020-10-21

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ApplicationHost/)=true OR match_regex(cmd_line, /(?i)Get-CachedGPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-GPPAutologon/)=true OR match_regex(cmd_line, /(?i)Get-GPPPassword/)=true OR match_regex(cmd_line, /(?i)Get-RegistryAutoLogon/)=true OR match_regex(cmd_line, /(?i)Get-SiteListPassword/)=true OR match_regex(cmd_line, /(?i)Get-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Request-SPNTicket/)=true OR match_regex(cmd_line, /(?i)Get-VaultCredential/)=true OR match_regex(cmd_line, /(?i)Invoke-Kerberoast/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping

* Malicious PowerShell


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllPowerSploitModulesWithOldNames.log


_version_: 1
</details>

---

### Credential Extraction native Microsoft debuggers peek into the kernel
Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2020-10-18

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND parent_process_name != null AND process_name != null  AND ( match_regex(parent_process_name, /(?i)ntkd\.exe/)=true OR match_regex(parent_process_name, /(?i)livekd\.exe/)=true ) AND match_regex(process_name, /(?i)conhost\.exe/)=true AND match_regex(cmd_line, /(?i)0xffffffff/)=true AND match_regex(cmd_line, /(?i)\-ForceV1/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping

* Unusual Processes


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* process_name

* parent_process_name

* _time

* dest_device_id

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps.

#### Reference


* https://medium.com/@clermont1050/covid-19-cyber-infection-c615ead7c29



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logLiveKDFullKernelDump.log


_version_: 1
</details>

---

### Credential Extraction native Microsoft debuggers via z command line option
Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. Native Microsoft debuggers, such as kd, ntkd, livekd and windbg, can be leveraged to read credential material directly from memory and process dumps.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2020-10-18

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND process_name != null AND ( match_regex(process_name, /^(?i)ntkd\.exe/)=true OR match_regex(process_name, /^(?i)kd\.exe/)=true ) AND match_regex(cmd_line, /(?i)\-z\s+/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping

* Unusual Processes


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* process_name

* _time

* dest_device_id

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, using debuggers this way may be indicative of developers analyzing crash dumps of their code. Note, even for developers this is an unusual way of working on code - debuggers are mostly used to step through code, not analyze its crash dumps.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logLiveKDFullKernelDump.log


_version_: 1
</details>

---

### Credential Extraction via Get-ADDBAccount module present in PowerSploit and DSInternals
Credential extraction is often an illegal recovery of credential material from secured authentication resources and repositories. This process may also involve decryption or other transformations of the stored credential material. PowerSploit and DSInternals are common exploit APIs offering PowerShell modules for various exploits of Windows and Active Directory environments.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2020-10-18

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND match_regex(cmd_line, /(?i)Get-ADDBAccount/)=true AND match_regex(cmd_line, /(?i)\-dbpath[\s;:\.\
|]+/)=true

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping

* Malicious PowerShell


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logPowerShellModule.log


_version_: 1
</details>

---

### DLLHost with no Command Line Arguments with Network
The following analytic identifies DLLHost.exe with no command line arguments with a network connection. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-04-19

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=dllhost.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| regex process="(dllhost\.exe.{0,4}$)" 
| join  process_id [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port 
| `drop_dm_object_name(Ports)` 
| rename  dest as connection_to_CNC] 
| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port 
| `dllhost_with_no_command_line_arguments_with_network_filter`
```
#### Associated Analytic Story

* Cobalt Strike


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `port` node.

#### Required field

* _time

* EventID

* process_name

* process_id

* parent_process_name

* dest_port

* process_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, some legitimate third party applications may use a moved copy of dllhost, triggering a false positive.

#### Reference


* https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile

* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_dllhost.log


_version_: 1
</details>

---

### DNS Exfiltration Using Nslookup App
this search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type, TXT, A, AAAA, that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1048](https://attack.mitre.org/techniques/T1048/)
- **Last Updated**: 2021-04-15

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id values(Processes.parent_process) as parent_process count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "nslookup.exe" Processes.process = "*-querytype=*" OR Processes.process="*-qt=*" OR Processes.process="*-q=*" OR Processes.process="-type=*" OR Processes.process="*-retry=*" by Processes.dest Processes.user Processes.process_name  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dns_exfiltration_using_nslookup_app_filter`
```
#### Associated Analytic Story

* Suspicious DNS Traffic

* Dynamic DNS

* Command and Control

* Data Exfiltration


#### 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. Tune and filter known instances of nslookup.exe may be used.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1048 | Exfiltration Over Alternative Protocol | Exfiltration |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin nslookup usage

#### Reference


* https://www.fireeye.com/blog/threat-research/2017/03/fin7_spear_phishing.html

* https://www.varonis.com/blog/dns-tunneling/

* https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/windows-sysmon.log


_version_: 1
</details>

---

### DNS Query Length Outliers - MLTK
This search allows you to identify DNS requests that are unusually large for the record type being requested in your environment.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Resolution
- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071/004/)
- **Last Updated**: 2020-01-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as start_time max(_time) as end_time values(DNS.src) as src values(DNS.dest) as dest from datamodel=Network_Resolution by DNS.query DNS.record_type 
| search DNS.record_type=* 
|  `drop_dm_object_name(DNS)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| eval query_length = len(query) 
| apply dns_query_pdfmodel threshold=0.01 
| rename "IsOutlier(query_length)" as isOutlier 
| search isOutlier > 0 
| sort -query_length 
| table start_time end_time query record_type count src dest query_length 
| `dns_query_length_outliers___mltk_filter` 
```
#### Associated Analytic Story

* Hidden Cobra Malware

* Suspicious DNS Traffic

* Command and Control


#### How To Implement
To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of DNS Query Length - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\
This search produces fields (`query`,`query_length`,`count`) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable. To see the additional metadata, add the following fields, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\
1. \
1. **Label:** DNS Query Length, **Field:** query_length\
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`

#### Required field

* _time

* DNS.src

* DNS.dest

* DNS.query

* DNS.record_type



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1071.004 | DNS | Command And Control |


#### Kill Chain Phase

* Command and Control


#### 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.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### DNS Query Length With High Standard Deviation
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Resolution
- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048/003/)
- **Last Updated**: 2021-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where NOT DNS.message_type IN("Pointer","PTR") by DNS.query 
| `drop_dm_object_name("DNS")` 
| eval query_length = len(query) 
| table query query_length record_type count 
| eventstats stdev(query_length) AS stdev avg(query_length) AS avg p50(query_length) AS p50
| where query_length>(avg+stdev*2) 
| eval z_score=(query_length-avg)/stdev 
| `dns_query_length_with_high_standard_deviation_filter` 
```
#### Associated Analytic Story

* Hidden Cobra Malware

* Suspicious DNS Traffic

* Command and Control


#### How To Implement
To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model.

#### Required field

* _time

* DNS.query



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration |


#### Kill Chain Phase

* Command and Control


#### Known False Positives
It's possible there can be long domain names that are legitimate.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/long_dns_queries/windows-sysmon.log


_version_: 3
</details>

---

### DNSTwist Domain Names
This search creates permutations of your existing domains, removes the valid domain names and stores them in a specified lookup file so they can be checked for in the associated detection searches.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-10-08

<details>
  <summary>details</summary>

#### Search
```

| dnstwist domainlist=domains.csv 
| `remove_valid_domains` 
| eval domain_abuse="true" 
| table domain, domain_abuse 
| outputlookup brandMonitoring_lookup 
| stats count
```
#### Associated Analytic Story

* Brand Monitoring

* Suspicious Emails


#### How To Implement
To successfully implement this search you need to update the file called domains.csv in the DA-ESS-SOC/lookup directory. Or `cim_corporate_email_domains.csv` and `cim_corporate_web_domains.csv` from **Splunk\_SA\_CIM**.

#### Required field

* _time




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### DSQuery Domain Discovery
The following analytic identifies "dsquery.exe" execution with arguments looking for `TrustedDomain` query directly on the command-line. This is typically indicative of an Administrator or adversary perform domain trust discovery. Note that this query does not identify any other variations of "Dsquery.exe" usage.\
Within this detection, it is assumed `dsquery.exe` is not moved or renamed.\
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 "dsquery.exe" and its parent process.\
DSQuery.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64` and only on Server operating system.\
The following DLL(s) are loaded when DSQuery.exe is launched `dsquery.dll`. If found loaded by another process, it is possible dsquery is running within that process context in memory.\
In addition to trust discovery, review parallel processes for additional behaviors performed. Identify the parent process and capture any files (batch files, for example) being used.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1482](https://attack.mitre.org/techniques/T1482/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=dsquery.exe Processes.process=*trustedDomain* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dsquery_domain_discovery_filter`
```
#### Associated Analytic Story

* Domain Trust Discovery


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1482 | Domain Trust Discovery | Discovery |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives. If there is a true false positive, filter based on command-line or parent process.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md

* http://www.harmj0y.net/blog/redteaming/a-guide-to-attacking-domain-trusts/

* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc732952(v=ws.11)

* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc754232(v=ws.11)



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Delete A Net User
This analytic will detect a suspicious net.exe/net1.exe command-line to delete a user on a system. This technique may be use by an administrator for legitimate purposes, however this behavior has been used in the wild to impair some user or deleting adversaries tracks created during its lateral movement additional systems. During triage, review parallel processes for additional behavior. Identify any other user accounts created before or after.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: Endpoint
- **ATT&CK**: [T1489](https://attack.mitre.org/techniques/T1489/)
- **Last Updated**: 2021-06-21

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=lower(ucast(map_get(input_event, "process"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line IS NOT NULL AND like(cmd_line, "%/delete%") AND (process_name="net1.exe" OR process_name="net.exe") 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name, "process_path", process_path]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* XMRig

* Ransomware


#### How To Implement
o 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. Tune and filter known instances where renamed net.exe may be used.

#### Required field

* _time

* dest_device_id

* process_name

* parent_process_name

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1489 | Service Stop | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
System administrators or scripts may delete user accounts via this technique. Filter as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/ssa_data1/net_user_del.log


_version_: 1
</details>

---

### Delete ShadowCopy With PowerShell
This following analytic detects PowerShell command to delete shadow copy using the WMIC PowerShell module. This technique was seen used by a recent adversary to deploy DarkSide Ransomware where it executed a child process of PowerShell to execute a hex encoded command to delete shadow copy. This hex encoded command was able to be decrypted by PowerShell log.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/)
- **Last Updated**: 2021-05-12

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message= "*ShadowCopy*" (Message = "*Delete*" OR Message = "*Remove*") 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `delete_shadowcopy_with_powershell_filter`
```
#### Associated Analytic Story

* DarkSide Ransomware

* Ransomware

* Revil Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.

#### Required field

* _time

* EventCode

* Message

* ComputerName

* User



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1490 | Inhibit System Recovery | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

* https://searchwindowsserver.techtarget.com/tutorial/Set-up-PowerShell-script-block-logging-for-added-security



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-powershell.log


_version_: 1
</details>

---

### Deleting Of Net Users
This analytic will detect a suspicious net.exe/net1.exe command-line to delete a user on a system. This technique may be use by an administrator for legitimate purposes, however this behavior has been used in the wild to impair some user or deleting adversaries tracks created during its lateral movement additional systems. During triage, review parallel processes for additional behavior. Identify any other user accounts created before or after.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1531](https://attack.mitre.org/techniques/T1531/)
- **Last Updated**: 2021-05-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND Processes.process="*user*" AND  Processes.process="*/delete*" by  Processes.process_name Processes.dest Processes.user Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `deleting_of_net_users_filter`
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed net.exe may be used.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1531 | Account Access Removal | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
System administrators or scripts may delete user accounts via this technique. Filter as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Deleting Shadow Copies
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=vssadmin.exe OR Processes.process_name=wmic.exe) Processes.process=*delete* Processes.process=*shadow* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `deleting_shadow_copies_filter`
```
#### Associated Analytic Story

* Windows Log Manipulation

* SamSam Ransomware

* Ransomware

* Clop Ransomware


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1490 | Inhibit System Recovery | Impact |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Deny Permission using Cacls Utility
This analytic identifies a potential adversary that changes the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft, ransomware or coinminer scripts. This behavior is meant to evade detection and prevent access to their component files.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: Endpoint
- **ATT&CK**: [T1222](https://attack.mitre.org/techniques/T1222/)
- **Last Updated**: 2021-06-14

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line IS NOT NULL AND match_regex(cmd_line, /(?i)deny/)=true AND (process_name="cacls.exe" OR process_name="xcacls.exe" OR process_name="icacls.exe") 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name, "process_path", process_path]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed icacls.exe may be used.

#### Required field

* _time

* dest_device_id

* process_name

* parent_process_name

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1222 | File and Directory Permissions Modification | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network administrator may use this windows utility but this is not a common practice.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/ssa_cacls/all_icalc.log


_version_: 1
</details>

---

### Detect ARP Poisoning
By enabling Dynamic ARP Inspection as a Layer 2 Security measure on the organization's network devices, we will be able to detect ARP Poisoning attacks in the Infrastructure.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557.002](https://attack.mitre.org/techniques/T1557/002/)
- **Last Updated**: 2020-08-11

<details>
  <summary>details</summary>

#### Search
```
`cisco_networks` facility="PM" mnemonic="ERR_DISABLE" disable_cause="arp-inspection" 
| eval src_interface=src_int_prefix_long+src_int_suffix 
| stats min(_time) AS firstTime max(_time) AS lastTime count BY host src_interface 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)`
| `detect_arp_poisoning_filter`
```
#### Associated Analytic Story

* Router and Infrastructure Security


#### How To Implement
This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and Dynamic ARP Inspection (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-2_2_e/security/configuration_guide/b_sec_1522e_2960x_cg/b_sec_1522e_2960x_cg_chapter_01111.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.

#### Required field

* _time

* facility

* mnemonic

* disable_cause

* src_int_prefix_long

* src_int_suffix

* host

* src_interface



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1200 | Hardware Additions | Initial Access |
| T1498 | Network Denial of Service | Impact |
| T1557.002 | ARP Cache Poisoning | Credential Access, Collection |


#### Kill Chain Phase

* Reconnaissance

* Delivery

* Actions on Objectives


#### Known False Positives
This search might be prone to high false positives if DHCP Snooping or ARP inspection has been incorrectly configured, or if a device normally sends many ARP packets (unlikely).

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect AWS Console Login by New User
This search looks for AWS 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

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Authentication
- **ATT&CK**: 
- **Last Updated**: 2020-05-28

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user 
| `drop_dm_object_name(Authentication)` 
| inputlookup append=t previously_seen_users_console_logins 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by user 
| eval userStatus=if(firstTime >=relative_time(now(),"-24h@h"), "First Time Logging into AWS Console", "Previously Seen User") 
|where userStatus="First Time Logging into AWS Console" 
|  `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `detect_aws_console_login_by_new_user_filter`
```
#### Associated Analytic Story

* Suspicious Cloud Authentication Activities


#### How To Implement
You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines.

#### Required field

* _time

* Authentication.signature

* Authentication.user




#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Detect AWS Console Login by User from New City
This search looks for AWS 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

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Authentication
- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/)
- **Last Updated**: 2020-10-07

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src 
| iplocation Authentication.src 
| `drop_dm_object_name(Authentication)` 
| table firstTime lastTime user City 
| join user  type=outer [
| inputlookup previously_seen_users_console_logins 
| stats earliest(firstTime) AS earliestseen by user City 
| fields earliestseen user City] 
| eval userCity=if(firstTime >= relative_time(now(), "-24h@h"), "New City","Previously Seen City") 
| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") 
| where userCity = "New City" AND userStatus != "Old User" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table firstTime lastTime user City  userStatus userCity  
| `detect_aws_console_login_by_user_from_new_city_filter`
```
#### Associated Analytic Story

* Suspicious AWS Login Activities

* Suspicious Cloud Authentication Activities


#### How To Implement
You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_city_filter` macro.

#### Required field

* _time

* Authentication.signature

* Authentication.user

* Authentication.src



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Detect AWS Console Login by User from New Country
This search looks for AWS 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

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Authentication
- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/)
- **Last Updated**: 2020-10-07

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src 
| iplocation Authentication.src 
| `drop_dm_object_name(Authentication)` 
| table firstTime lastTime user Country 
| join user  type=outer [
| inputlookup previously_seen_users_console_logins 
| stats earliest(firstTime) AS earliestseen by user Country 
| fields earliestseen user Country] 
| eval userCountry=if(firstTime >= relative_time(now(), "-24h@h"), "New Country","Previously Seen Country") 
| eval userStatus=if(earliestseen >= relative_time(now(),"-24h@h") OR isnull(earliestseen), "New User","Old User") 
| where userCountry = "New Country" AND userStatus != "Old User" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table firstTime lastTime user Country  userStatus userCountry  
| `detect_aws_console_login_by_user_from_new_country_filter`
```
#### Associated Analytic Story

* Suspicious AWS Login Activities

* Suspicious Cloud Authentication Activities


#### How To Implement
You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_country_filter` macro.

#### Required field

* _time

* Authentication.signature

* Authentication.user

* Authentication.src



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Detect AWS Console Login by User from New Region
This search looks for AWS 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

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Authentication
- **ATT&CK**: [T1535](https://attack.mitre.org/techniques/T1535/)
- **Last Updated**: 2020-10-07

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src 
| iplocation Authentication.src 
| `drop_dm_object_name(Authentication)` 
| table firstTime lastTime user Region 
| join user  type=outer [
| inputlookup previously_seen_users_console_logins 
| stats earliest(firstTime) AS earliestseen by user Region 
| fields earliestseen user Region] 
| eval userRegion=if(firstTime >= relative_time(now(), "-24h@h"), "New Region","Previously Seen Region") 
| eval userStatus=if(earliestseen >= relative_time(now(), "-24h@h") OR isnull(earliestseen), "New User","Old User") 
| where userRegion = "New Region" AND userStatus != "Old User" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table firstTime lastTime user Region  userStatus userRegion  
| `detect_aws_console_login_by_user_from_new_region_filter`
```
#### Associated Analytic Story

* Suspicious AWS Login Activities

* Suspicious Cloud Authentication Activities


#### How To Implement
You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Run the `Previously Seen Users in AWS CloudTrail - Initial` support search only once to create a baseline of previously seen IAM users within the last 30 days. Run `Previously Seen Users in AWS CloudTrail - Update` hourly (or more frequently depending on how often you run the detection searches) to refresh the baselines. You can also provide additional filtering for this search by customizing the `detect_aws_console_login_by_user_from_new_region_filter` macro.

#### Required field

* _time

* Authentication.signature

* Authentication.user

* Authentication.src



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1535 | Unused/Unsupported Cloud Regions | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/abnormally_high_cloud_instances_launched/cloudtrail_behavioural_detections.json


_version_: 1
</details>

---

### Detect Activity Related to Pass the Hash Attacks
This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1550.002](https://attack.mitre.org/techniques/T1550/002/)
- **Last Updated**: 2020-10-15

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4624 (Logon_Type=3 Logon_Process=NtLmSsp WorkstationName=WORKSTATION NOT AccountName="ANONYMOUS LOGON") OR (Logon_Type=9 Logon_Process=seclogo) 
| fillnull 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode, Logon_Type, WorkstationName, user, dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_activity_related_to_pass_the_hash_attacks_filter` 
```
#### Associated Analytic Story

* Lateral Movement


#### How To Implement
To successfully implement this search, you must ingest your Windows Security Event logs and leverage the latest TA for Windows.

#### Required field

* _time

* EventCode

* Logon_Type

* Logon_Process

* WorkstationName

* user

* dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1550.002 | Pass the Hash | Defense Evasion, Lateral Movement |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1550.002/atomic_red_team/windows-security.log


_version_: 5
</details>

---

### Detect AzureHound Command-Line Arguments
The following analytic identifies the common command-line argument used by AzureHound `Invoke-AzureHound`. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1087.002](https://attack.mitre.org/techniques/T1087/002/), [T1087.001](https://attack.mitre.org/techniques/T1087/001/), [T1482](https://attack.mitre.org/techniques/T1482/), [T1069.002](https://attack.mitre.org/techniques/T1069/002/), [T1069.001](https://attack.mitre.org/techniques/T1069/001/)
- **Last Updated**: 2021-06-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*invoke-azurehound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_azurehound_command_line_arguments_filter`
```
#### Associated Analytic Story

* Discovery Techniques


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1087.002 | Domain Account | Discovery |
| T1087.001 | Local Account | Discovery |
| T1482 | Domain Trust Discovery | Discovery |
| T1069.002 | Domain Groups | Discovery |
| T1069.001 | Local Groups | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
Unknown.

#### Reference


* https://attack.mitre.org/software/S0521/

* https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors

* https://posts.specterops.io/introducing-bloodhound-4-0-the-azure-update-9b2b26c5e350

* https://github.com/BloodHoundAD/BloodHound/blob/master/Collectors/AzureHound.ps1



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log


_version_: 1
</details>

---

### Detect AzureHound File Modifications
The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop `20210601090751-azurecollection.zip`. In addition to the zip, multiple .json files will be written to disk, which are in the zip.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1087.002](https://attack.mitre.org/techniques/T1087/002/), [T1087.001](https://attack.mitre.org/techniques/T1087/001/), [T1482](https://attack.mitre.org/techniques/T1482/), [T1069.002](https://attack.mitre.org/techniques/T1069/002/), [T1069.001](https://attack.mitre.org/techniques/T1069/001/)
- **Last Updated**: 2021-06-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*-azurecollection.zip", "*-azprivroleadminrights.json", "*-azglobaladminrights.json", "*-azcloudappadmins.json", "*-azapplicationadmins.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_azurehound_file_modifications_filter`
```
#### Associated Analytic Story

* Discovery Techniques


#### How To Implement
To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.

#### Required field

* _time

* file_path

* dest

* file_name

* process_id

* file_create_time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1087.002 | Domain Account | Discovery |
| T1087.001 | Local Account | Discovery |
| T1482 | Domain Trust Discovery | Discovery |
| T1069.002 | Domain Groups | Discovery |
| T1069.001 | Local Groups | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed.

#### Reference


* https://posts.specterops.io/introducing-bloodhound-4-0-the-azure-update-9b2b26c5e350

* https://raw.githubusercontent.com/BloodHoundAD/BloodHound/master/Collectors/AzureHound.ps1



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log


_version_: 1
</details>

---

### Detect Baron Samedit CVE-2021-3156
This search detects the heap-based buffer overflow of sudoedit

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/)
- **Last Updated**: 2021-01-27

<details>
  <summary>details</summary>

#### Search
```
`linux_hosts` 
| search "sudoedit -s \\" 
| `detect_baron_samedit_cve_2021_3156_filter`
```
#### Associated Analytic Story

* Baron Samedit CVE-2021-3156


#### How To Implement
Splunk Universal Forwarder running on Linux systems, capturing logs from the /var/log directory. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.

#### Required field

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit



#### Test Dataset


_version_: 1
</details>

---

### Detect Baron Samedit CVE-2021-3156 Segfault
This search detects the heap-based buffer overflow of sudoedit

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/)
- **Last Updated**: 2021-01-29

<details>
  <summary>details</summary>

#### Search
```
`linux_hosts` 
| search sudoedit segfault 
| stats count min(_time) as firstTime max(_time) as lastTime by host 
| search count > 5 
| `detect_baron_samedit_cve_2021_3156_segfault_filter`
```
#### Associated Analytic Story

* Baron Samedit CVE-2021-3156


#### How To Implement
Splunk Universal Forwarder running on Linux systems (tested on Centos and Ubuntu), where segfaults are being logged.  This also captures instances where the exploit has been compiled into a binary. The detection looks for greater than 5 instances of sudoedit combined with segfault over your search time period on a single host

#### Required field

* _time

* host



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
If sudoedit is throwing segfaults for other reasons this will pick those up too.

#### Reference


* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit



#### Test Dataset


_version_: 1
</details>

---

### Detect Baron Samedit CVE-2021-3156 via OSQuery
This search detects the heap-based buffer overflow of sudoedit

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/)
- **Last Updated**: 2021-01-28

<details>
  <summary>details</summary>

#### Search
```
`osquery_process` 
| search "columns.cmdline"="sudoedit -s \\*" 
| `detect_baron_samedit_cve_2021_3156_via_osquery_filter`
```
#### Associated Analytic Story

* Baron Samedit CVE-2021-3156


#### How To Implement
OSQuery installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. The vulnerability is exposed when a non privledged user tries passing in a single \ character at the end of the command while using the shell and edit flags.

#### Required field

* _time

* columns.cmdline



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://blog.qualys.com/vulnerabilities-research/2021/01/26/cve-2021-3156-heap-based-buffer-overflow-in-sudo-baron-samedit



#### Test Dataset


_version_: 1
</details>

---

### Detect Computer Changed with Anonymous Account
This search looks for Event Code 4742 (Computer Change) or EventCode 4624 (An account was successfully logged on) with an anonymous account.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1210](https://attack.mitre.org/techniques/T1210/)
- **Last Updated**: 2020-09-18

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4624 OR EventCode=4742 TargetUserName="ANONYMOUS LOGON" LogonType=3 
| stats count values(host) as host, values(TargetDomainName) as Domain, values(user) as user 
| `detect_computer_changed_with_anonymous_account_filter`
```
#### Associated Analytic Story

* Detect Zerologon Attack


#### How To Implement
This search requires audit computer account management to be enabled on the system in order to generate Event ID 4742. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Event 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.

#### Required field

* _time

* EventCode

* TargetUserName

* LogonType

* TargetDomainName

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1210 | Exploitation of Remote Services | Lateral Movement |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None thus far found

#### Reference


* https://www.lares.com/blog/from-lares-labs-defensive-guidance-for-zerologon-cve-2020-1472/



#### Test Dataset


_version_: 1
</details>

---

### Detect Copy of ShadowCopy with Script Block Logging
The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies `copy` or `[System.IO.File]::Copy` being used to capture the SAM, SYSTEM or SECURITY hives identified in script block. This will catch the most basic use cases for credentials being taken for offline cracking. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003/002/)
- **Last Updated**: 2021-07-21

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message IN ("*copy*","*[System.IO.File]::Copy*") AND Message IN ("*System32\\config\\SAM*", "*System32\\config\\SYSTEM*","*System32\\config\\SECURITY*") 
| stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_copy_of_shadowcopy_with_script_block_logging_filter`
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* Message

* OpCode

* ComputerName

* User

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.002 | Security Account Manager | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives as the scope is limited to SAM, SYSTEM and SECURITY hives.

#### Reference


* https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-36934

* https://github.com/GossiTheDog/HiveNightmare

* https://github.com/JumpsecLabs/Guidance-Advice/tree/main/SAM_Permissions



#### Test Dataset


_version_: 1
</details>

---

### Detect Credential Dumping through LSASS access
This search looks for reading lsass memory consistent with credential dumping.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003/001/)
- **Last Updated**: 2019-12-03

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=10 TargetImage=*lsass.exe (GrantedAccess=0x1010 OR GrantedAccess=0x1410) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, SourceImage, SourceProcessId, TargetImage, TargetProcessId, EventCode, GrantedAccess 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_credential_dumping_through_lsass_access_filter` 
```
#### Associated Analytic Story

* Credential Dumping

* Detect Zerologon Attack


#### 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.

#### Required field

* _time

* EventCode

* TargetImage

* GrantedAccess

* Computer

* SourceImage

* SourceProcessId

* TargetImage

* TargetProcessId



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.001 | LSASS Memory | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Detect Dump LSASS Memory using comsvcs
This search detects the memory of lsass.exe being dumped for offline credential theft attack.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003/003/)
- **Last Updated**: 2020-09-15

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval tenant=ucast(map_get(input_event, "_tenant"), "string", null), machine=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process=lower(ucast(map_get(input_event, "process"), "string", null)), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where process_name LIKE "%rundll32.exe%" AND match_regex(process, /(?i)comsvcs.dll[,\s]+MiniDump/)=true 
| eval start_time = timestamp, end_time = timestamp, entities = mvappend(machine), body=create_map(["event_id", event_id, "process_name", process_name]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
You must be ingesting endpoint data that tracks process activity, including Windows command line logging. You can see how we test this with [Event Code 4688](https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4688a) on the [attack_range](https://github.com/splunk/attack_range/blob/develop/ansible/roles/windows_common/tasks/windows-enable-4688-cmd-line-audit.yml).

#### Required field

* process_name

* _tenant

* _time

* dest_device_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.003 | NTDS | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf



#### Test Dataset


_version_: 1
</details>

---

### Detect Empire with PowerShell Script Block Logging
The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable on critical endpoints or all. \
This analytic identifies the common PowerShell stager used by PowerShell-Empire. Each stager that may use PowerShell all uses the same pattern. The initial HTTP will be base64 encoded and use `system.net.webclient`. Note that some obfuscation may evade the analytic. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2021-06-09

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104  (Message=*system.net.webclient* AND Message=*frombase64string*) 
| stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_empire_with_powershell_script_block_logging_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* Message

* OpCode

* ComputerName

* User

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives may only pertain to it not being related to Empire, but another framework. Filter as needed if any applications use the same pattern.

#### Reference


* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/

* https://github.com/BC-SECURITY/Empire



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell.log


_version_: 1
</details>

---

### Detect Excessive Account Lockouts From Endpoint
This search identifies endpoints that have caused a relatively high number of account lockouts in a short period.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078.002](https://attack.mitre.org/techniques/T1078/002/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result 
|`drop_dm_object_name("All_Changes")` 
|`drop_dm_object_name("Account_Management")`
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search count > 5 
| `detect_excessive_account_lockouts_from_endpoint_filter`
```
#### Associated Analytic Story

* Account Monitoring and Controls


#### How To Implement
You must ingest your Windows security event logs in the `Change` datamodel under the nodename is `Account_Management`, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. \
 **Splunk>Phantom Playbook Integration**\
If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk `https://splunkbase.splunk.com/app/3411/`, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. \
(Playbook Link:`https://my.phantom.us/4.1/playbook/excessive-account-lockouts-enrichment-and-response/`).\


#### Required field

* _time

* All_Changes.user

* nodename

* All_Changes.result

* All_Changes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.002 | Domain Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### Known False Positives
It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log


_version_: 5
</details>

---

### Detect Excessive User Account Lockouts
This search detects user accounts that have been locked out a relatively high number of times in a short period.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1078.003](https://attack.mitre.org/techniques/T1078/003/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user All_Changes.result 
|`drop_dm_object_name("All_Changes")` 
|`drop_dm_object_name("Account_Management")`
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search count > 5 
| `detect_excessive_user_account_lockouts_filter`
```
#### Associated Analytic Story

* Account Monitoring and Controls


#### 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.

#### Required field

* _time

* All_Changes.result

* nodename

* All_Changes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.003 | Local Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### Known False Positives
It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1078.002/account_lockout/windows-system.log


_version_: 3
</details>

---

### Detect Exchange Web Shell
The following query identifies suspicious .aspx created in 3 paths identified by Microsoft as known drop locations for Exchange exploitation related to HAFNIUM group. Paths include: `\HttpProxy\owa\auth\`, `\inetpub\wwwroot\aspnet_client\`, and `\HttpProxy\OAB\`. Upon triage, the suspicious .aspx file will likely look obvious on the surface. inspect the contents for script code inside. Identify additional log sources, IIS included, to review source and other potential exploitation.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1505.003](https://attack.mitre.org/techniques/T1505/003/)
- **Last Updated**: 2021-03-09

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=System  by _time span=1h Processes.process_id Processes.process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| join process_guid, _time [
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path IN ("*\\HttpProxy\\owa\\auth\\*", "*\\inetpub\\wwwroot\\aspnet_client\\*", "*\\HttpProxy\\OAB\\*") Filesystem.file_name="*.aspx" by _time span=1h Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path 
| `drop_dm_object_name(Filesystem)` 
| fields _time dest file_create_time file_name file_path process_name process_path process] 
| dedup file_create_time 
| table dest file_create_time, file_name, file_path, process_name 
| `detect_exchange_web_shell_filter`
```
#### Associated Analytic Story

* HAFNIUM Group


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.

#### Required field

* _time

* Filesystem.file_path

* Filesystem.process_id

* Filesystem.file_name

* Filesystem.file_hash

* Filesystem.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1505.003 | Web Shell | Persistence |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
The query is structured in a way that `action` (read, create) is not defined. Review the results of this query, filter, and tune as necessary. It may be necessary to generate this query specific to your endpoint product.

#### Reference


* https://raw.githubusercontent.com/Azure/Azure-Sentinel/master/Sample%20Data/Feeds/MSTICIoCs-ExchangeServerVulnerabilitiesDisclosedMarch2021.csv



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon_proxylogon.log


_version_: 2
</details>

---

### Detect F5 TMUI RCE CVE-2020-5902
This search detects remote code exploit attempts on F5 BIG-IP, BIG-IQ, and Traffix SDC devices

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/)
- **Last Updated**: 2020-08-02

<details>
  <summary>details</summary>

#### Search
```
`f5_bigip_rogue` 
| regex _raw="(hsqldb;
|.*\\.\\.;.*)" 
| search `detect_f5_tmui_rce_cve_2020_5902_filter`
```
#### Associated Analytic Story

* F5 TMUI RCE CVE-2020-5902


#### How To Implement
To consistently detect exploit attempts on F5 devices using the vulnerabilities contained within CVE-2020-5902 it is recommended to ingest logs via syslog.  As many BIG-IP devices will have SSL enabled on their management interfaces, detections via wire data may not pick anything up unless you are decrypting SSL traffic in order to inspect it.  I am using a regex string from a Cloudflare mitigation technique to try and always catch the offending string (..;), along with the other exploit of using (hsqldb;).

#### Required field

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1190 | Exploit Public-Facing Application | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.ptsecurity.com/ww-en/about/news/f5-fixes-critical-vulnerability-discovered-by-positive-technologies-in-big-ip-application-delivery-controller/

* https://support.f5.com/csp/article/K52145254

* https://blog.cloudflare.com/cve-2020-5902-helping-to-protect-against-the-f5-tmui-rce-vulnerability/



#### Test Dataset


_version_: 1
</details>

---

### Detect GCP Storage access from a new IP
This search looks at GCP Storage bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed a GCP Storage bucket.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/)
- **Last Updated**: 2020-08-10

<details>
  <summary>details</summary>

#### Search
```
`google_gcp_pubsub_message` 
| multikv 
| rename sc_status_ as status 
| rename cs_object_ as bucket_name 
| rename c_ip_ as remote_ip 
| rename cs_uri_ as request_uri 
| rename cs_method_ as operation 
| search status="\"200\"" 
| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip operation request_uri 
| table firstTime, lastTime, bucket_name, remote_ip, operation, request_uri 
| inputlookup append=t previously_seen_gcp_storage_access_from_remote_ip.csv 
| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip operation request_uri 
| outputlookup previously_seen_gcp_storage_access_from_remote_ip.csv 
| eval newIP=if(firstTime >= relative_time(now(),"-70m@m"), 1, 0) 
| where newIP=1 
| eval first_time=strftime(firstTime,"%m/%d/%y %H:%M:%S") 
| eval last_time=strftime(lastTime,"%m/%d/%y %H:%M:%S") 
| table  first_time last_time bucket_name remote_ip operation request_uri 
| `detect_gcp_storage_access_from_a_new_ip_filter`
```
#### Associated Analytic Story

* Suspicious GCP Storage Activities


#### How To Implement
This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview). In order to capture public GCP Storage Bucket access logs, you must also enable storage bucket logging to your PubSub Topic as per https://cloud.google.com/storage/docs/access-logs.  These logs are deposited into the nominated Storage Bucket on an hourly basis and typically show up by 15 minutes past the hour.  It is recommended to configure any saved searches or correlation searches in Enterprise Security to run on an hourly basis at 30 minutes past the hour (cron definition of 30 * * * *).  A lookup table (previously_seen_gcp_storage_access_from_remote_ip.csv) stores the previously seen access requests, and is used by this search to determine any newly seen IP addresses accessing the Storage Buckets.

#### Required field

* _time

* sc_status_

* cs_object_

* c_ip_

* cs_uri_

* cs_method_



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1530 | Data from Cloud Storage Object | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
GCP Storage buckets can be accessed from any IP (if the ACLs are open to allow it), 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 two hours.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect HTML Help Renamed
The following analytic identifies a renamed instance of hh.exe (HTML Help) executing a Compiled HTML Help (CHM). This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Validate it is the legitimate version of hh.exe by reviewing the PE metadata. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218/001/)
- **Last Updated**: 2021-02-11

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 OriginalFileName=HH.exe NOT process_name=hh.exe 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_html_help_renamed_filter`
```
#### Associated Analytic Story

* Suspicious Compiled HTML Activity


#### 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. Tune and filter known instances where renamed hh.exe may be used.

#### Required field

* _time

* EventID

* OriginalFileName

* process_name

* Computer

* User

* parent_process_name

* process_path

* CommandLine



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.001 | Compiled HTML File | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely a renamed instance of hh.exe will be used legitimately, filter as needed.

#### Reference


* https://attack.mitre.org/techniques/T1218/001/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md

* https://lolbas-project.github.io/lolbas/Binaries/Hh/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect HTML Help Spawn Child Process
The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) that spawns a child process. This particular technique will load Windows script code from a compiled help file. CHM files may contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review child process events and investigate further. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218/001/)
- **Last Updated**: 2021-02-11

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=hh.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_html_help_spawn_child_process_filter`
```
#### Associated Analytic Story

* Suspicious Compiled HTML Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.001 | Compiled HTML File | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, some legitimate applications (ex. web browsers) may spawn a child process. Filter as needed.

#### Reference


* https://attack.mitre.org/techniques/T1218/001/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md

* https://lolbas-project.github.io/lolbas/Binaries/Hh/

* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7

* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect HTML Help URL in Command Line
The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file from a remote url. This particular technique will load Windows script code from a compiled help file. CHM files may  contain nearly any file type embedded, but only execute html/htm. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. Review reputation of remote IP and domain. Some instances, it is worth decompiling the .chm file to review its original contents. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218/001/)
- **Last Updated**: 2021-02-11

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process=*http* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_html_help_url_in_command_line_filter`
```
#### Associated Analytic Story

* Suspicious Compiled HTML Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.001 | Compiled HTML File | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, some legitimate applications may retrieve a CHM remotely, filter as needed.

#### Reference


* https://attack.mitre.org/techniques/T1218/001/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md

* https://lolbas-project.github.io/lolbas/Binaries/Hh/

* https://blog.sevagas.com/?Hacking-around-HTA-files

* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7

* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect HTML Help Using InfoTech Storage Handlers
The following analytic identifies hh.exe (HTML Help) execution of a Compiled HTML Help (CHM) file using InfoTech Storage Handlers. This particular technique will load Windows script code from a compiled help file, using InfoTech Storage Handlers. itss.dll will load upon execution. Three InfoTech Storage handlers are supported - ms-its, its, mk:@MSITStore. ITSS may be used to launch a specific html/htm file from within a CHM file. CHM files may contain nearly any file type embedded. Upon a successful execution, the following script engines may be used for execution - JScript, VBScript, VBScript.Encode, JScript.Encode, JScript.Compact. Analyst may identify vbscript.dll or jscript.dll loading into hh.exe upon execution. The "htm" and "html" file extensions were the only extensions observed to be supported for the execution of Shortcut commands or WSH script code. During investigation, identify script content origination. hh.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.001](https://attack.mitre.org/techniques/T1218/001/)
- **Last Updated**: 2021-02-11

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=hh.exe Processes.process IN ("*its:*", "*mk:@MSITStore:*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_html_help_using_infotech_storage_handlers_filter`
```
#### Associated Analytic Story

* Suspicious Compiled HTML Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.001 | Compiled HTML File | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
It is rare to see instances of InfoTech Storage Handlers being used, but it does happen in some legitimate instances. Filter as needed.

#### Reference


* https://attack.mitre.org/techniques/T1218/001/

* https://www.kb.cert.org/vuls/id/851869

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.001/T1218.001.md

* https://lolbas-project.github.io/lolbas/Binaries/Hh/

* https://gist.github.com/mgeeky/cce31c8602a144d8f2172a73d510e0e7

* https://cyberforensicator.com/2019/01/20/silence-dissecting-malicious-chm-files-and-performing-forensic-analysis/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect IPv6 Network Infrastructure Threats
By enabling IPv6 First Hop Security as a Layer 2 Security measure on the organization's network devices, we will be able to detect various attacks such as packet forging in the Infrastructure.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557.002](https://attack.mitre.org/techniques/T1557/002/)
- **Last Updated**: 2020-10-28

<details>
  <summary>details</summary>

#### Search
```
`cisco_networks` facility="SISF" mnemonic IN ("IP_THEFT","MAC_THEFT","MAC_AND_IP_THEFT","PAK_DROP") 
| eval src_interface=src_int_prefix_long+src_int_suffix 
| eval dest_interface=dest_int_prefix_long+dest_int_suffix 
| stats min(_time) AS firstTime max(_time) AS lastTime values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(mnemonic) AS mnemonic values(vendor_explanation) AS vendor_explanation values(src_ip) AS src_ip values(dest_ip) AS dest_ip values(dest_interface) AS dest_interface values(action) AS action count BY host src_interface 
| table host src_interface dest_interface src_mac src_ip dest_ip src_vlan mnemonic vendor_explanation action count 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `detect_ipv6_network_infrastructure_threats_filter`
```
#### Associated Analytic Story

* Router and Infrastructure Security


#### How To Implement
This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with one or more First Hop Security measures such as RA Guard, DHCP Guard and/or device tracking. See References for more information. The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.

#### Required field

* _time

* facility

* mnemonic

* src_int_prefix_long

* src_int_suffix

* dest_int_prefix_long

* dest_int_suffix

* src_mac

* src_vlan

* vendor_explanation

* action



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1200 | Hardware Additions | Initial Access |
| T1498 | Network Denial of Service | Impact |
| T1557.002 | ARP Cache Poisoning | Credential Access, Collection |


#### Kill Chain Phase

* Reconnaissance

* Delivery

* Actions on Objectives


#### Known False Positives
None currently known

#### Reference


* https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2019/pdf/BRKSEC-3200.pdf

* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-ra-guard.html

* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-snooping.html

* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dad-proxy.html

* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-nd-mcast-supp.html

* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-dhcpv6-guard.html

* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ip6-src-guard.html

* https://www.cisco.com/c/en/us/td/docs/ios-xml/ios/ipv6_fhsec/configuration/xe-16-12/ip6f-xe-16-12-book/ipv6-dest-guard.html



#### Test Dataset


_version_: 1
</details>

---

### Detect Kerberoasting
This search detects a potential kerberoasting attack via service principal name requests

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1558.003](https://attack.mitre.org/techniques/T1558/003/)
- **Last Updated**: 2020-10-21

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events() 
| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id"), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" 
| first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] 
| where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID 
| eval start_time=_time, end_time=_time, body=create_map(["event_id", event_id, "EventCode", EventCode, "ServiceName", ServiceName, "TicketOptions", TicketOptions, "TicketEncryptionType", TicketEncryptionType]), entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null))
| select start_time, end_time, entities, body 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2

#### Required field

* service_name

* _time

* event_code

* ticket_encryption_type

* service_id

* ticket_options



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1558.003 | Kerberoasting | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Older systems that support kerberos RC4 by default NetApp may generate false positives

#### Reference


* Initial ESCU implementation by Jose Hernandez and Patrick Bareiss



#### Test Dataset


_version_: 2
</details>

---

### Detect Large Outbound ICMP Packets
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1095](https://attack.mitre.org/techniques/T1095/)
- **Last Updated**: 2018-06-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count earliest(_time) as firstTime latest(_time) as lastTime values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 by All_Traffic.src_ip All_Traffic.dest_ip 
| `drop_dm_object_name("All_Traffic")` 
| search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `detect_large_outbound_icmp_packets_filter`
```
#### Associated Analytic Story

* Command and Control


#### 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

#### Required field

* _time

* All_Traffic.action

* All_Traffic.bytes

* All_Traffic.dest_category

* All_Traffic.protocol

* All_Traffic.transport

* All_Traffic.src_ip

* All_Traffic.dest_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1095 | Non-Application Layer Protocol | Command And Control |


#### Kill Chain Phase

* Command and Control


#### 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 macro `detect_large_outbound_icmp_packets_filter` to adjust the byte threshold or add specific IP addresses to an allow list.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Detect MSHTA Url in Command Line
This analytic identifies when Microsoft HTML Application Host (mshta.exe) utility is used to make remote http connections. Adversaries may use mshta.exe to proxy the download and execution of remote .hta files. The analytic identifies command line arguments of http and https being used. This technique is commonly used by malicious software to bypass preventative controls. 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 "rundll32.exe" and its parent process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218/005/)
- **Last Updated**: 2021-01-20

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process="*http://*" OR Processes.process="*https://*") by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_mshta_url_in_command_line_filter`
```
#### Associated Analytic Story

* Suspicious MSHTA Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.005 | Mshta | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
It is possible legitimate applications may perform this behavior and will need to be filtered.

#### Reference


* https://github.com/redcanaryco/AtomicTestHarnesses

* https://redcanary.com/blog/introducing-atomictestharnesses/

* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Mimikatz Using Loaded Images
This search looks for reading loaded Images unique to credential dumping with Mimikatz. Deprecated because mimikatz libraries changed and very noisy sysmon Event Code.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003/001/)
- **Last Updated**: 2019-12-03

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=7 
| stats values(ImageLoaded) as ImageLoaded values(ProcessId) as ProcessId by Computer, Image 
| search ImageLoaded=*WinSCard.dll ImageLoaded=*cryptdll.dll ImageLoaded=*hid.dll ImageLoaded=*samlib.dll ImageLoaded=*vaultcli.dll 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_mimikatz_using_loaded_images_filter`
```
#### Associated Analytic Story

* Credential Dumping

* Detect Zerologon Attack

* Cloud Federated Credential Abuse

* DarkSide Ransomware


#### 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.

#### Required field

* _time

* EventCode

* ImageLoaded

* ProcessId

* Computer

* Image



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.001 | LSASS Memory | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Other tools can import the same DLLs. These tools should be part of a whitelist. False positives may be present with any process that authenticates or uses credentials, PowerShell included. Filter based on parent process.

#### Reference


* https://cyberwardog.blogspot.com/2017/03/chronicles-of-threat-hunter-hunting-for.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Mimikatz With PowerShell Script Block Logging
The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies common Mimikatz functions that may be identified in the script block, including `mimikatz`. This will catch the most basic use cases for Pass the Ticket, Pass the Hash and `-DumprCreds`. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1003](https://attack.mitre.org/techniques/T1003/)
- **Last Updated**: 2021-06-09

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message IN (*mimikatz*, *-dumpcr*, *sekurlsa::pth*, *kerberos::ptt*, *kerberos::golden*) 
| stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_mimikatz_with_powershell_script_block_logging_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* Message

* OpCode

* ComputerName

* User

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003 | OS Credential Dumping | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives should be limited as the commands being identifies are quite specific to EventCode 4104 and Mimikatz. Filter as needed.

#### Reference


* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell.log


_version_: 1
</details>

---

### Detect New Local Admin account
This search looks for newly created accounts that have been elevated to local administrators.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1136.001](https://attack.mitre.org/techniques/T1136/001/)
- **Last Updated**: 2020-07-08

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4720 OR (EventCode=4732 Group_Name=Administrators) 
| transaction member_id connected=false maxspan=180m 
| rename member_id as user 
| stats count min(_time) as firstTime max(_time) as lastTime by user dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_new_local_admin_account_filter`
```
#### Associated Analytic Story

* DHS Report TA18-074A

* HAFNIUM Group


#### How To Implement
You must be ingesting Windows event logs using the Splunk Windows TA and collecting event code 4720 and 4732

#### Required field

* _time

* EventCode

* Group_Name

* member_id

* dest

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1136.001 | Local Account | Persistence |


#### Kill Chain Phase

* Actions on Objectives

* Command and Control


#### 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

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log


_version_: 2
</details>

---

### Detect New Login Attempts to Routers
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Authentication
- **ATT&CK**: 
- **Last Updated**: 2017-09-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user
| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) 
| where isOutlier=1
| `security_content_ctime(earliest)`
| `security_content_ctime(latest)` 
| `drop_dm_object_name("Authentication")` 
| `detect_new_login_attempts_to_routers_filter`
```
#### Associated Analytic Story

* Router and Infrastructure Security


#### 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.

#### Required field

* _time

* Authentication.dest_category

* Authentication.dest

* Authentication.user




#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Legitimate router connections may appear as new connections

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect New Open GCP Storage Buckets
This search looks for GCP PubSub events where a user has created an open/public GCP Storage bucket.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/)
- **Last Updated**: 2020-08-05

<details>
  <summary>details</summary>

#### Search
```
`google_gcp_pubsub_message` data.resource.type=gcs_bucket data.protoPayload.methodName=storage.setIamPermissions 
| spath output=action path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action 
| spath output=user path=data.protoPayload.authenticationInfo.principalEmail 
| spath output=location path=data.protoPayload.resourceLocation.currentLocations{} 
| spath output=src path=data.protoPayload.requestMetadata.callerIp 
| spath output=bucketName path=data.protoPayload.resourceName 
| spath output=role path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role 
| spath output=member path=data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member 
| search (member=allUsers AND action=ADD) 
| table  _time, bucketName, src, user, location, action, role, member 
| search `detect_new_open_gcp_storage_buckets_filter`
```
#### Associated Analytic Story

* Suspicious GCP Storage Activities


#### How To Implement
This search relies on the Splunk Add-on for Google Cloud Platform, setting up a Cloud Pub/Sub input, along with the relevant GCP PubSub topics and logging sink to capture GCP Storage Bucket events (https://cloud.google.com/logging/docs/routing/overview).

#### Required field

* _time

* data.resource.type

* data.protoPayload.methodName

* data.protoPayload.serviceData.policyDelta.bindingDeltas{}.action

* data.protoPayload.authenticationInfo.principalEmail

* data.protoPayload.resourceLocation.currentLocations{}

* data.protoPayload.requestMetadata.callerIp

* data.protoPayload.resourceName

* data.protoPayload.serviceData.policyDelta.bindingDeltas{}.role

* data.protoPayload.serviceData.policyDelta.bindingDeltas{}.member



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1530 | Data from Cloud Storage Object | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
While this search has no known false positives, it is possible that a GCP admin has legitimately created a public bucket for a specific purpose. That said, GCP strongly advises against granting full control to the "allUsers" group.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect New Open S3 Buckets over AWS CLI
This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket over the aws cli.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/)
- **Last Updated**: 2021-07-19

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventSource="s3.amazonaws.com" (userAgent="[aws-cli*" OR userAgent=aws-cli* ) eventName=PutBucketAcl OR requestParameters.accessControlList.x-amz-grant-read-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-write-acp IN ("*AuthenticatedUsers","*AllUsers") OR requestParameters.accessControlList.x-amz-grant-full-control IN ("*AuthenticatedUsers","*AllUsers") 
| rename requestParameters.bucketName AS bucketName 
| fillnull 
| stats count min(_time) as firstTime max(_time) as lastTime by userIdentity.userName userIdentity.principalId userAgent bucketName requestParameters.accessControlList.x-amz-grant-read requestParameters.accessControlList.x-amz-grant-read-acp requestParameters.accessControlList.x-amz-grant-write requestParameters.accessControlList.x-amz-grant-write-acp requestParameters.accessControlList.x-amz-grant-full-control 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_new_open_s3_buckets_over_aws_cli_filter` 
```
#### Associated Analytic Story

* Suspicious AWS S3 Activities


#### How To Implement


#### Required field

* _time

* eventSource

* eventName

* requestParameters.accessControlList.x-amz-grant-read-acp

* requestParameters.accessControlList.x-amz-grant-write

* requestParameters.accessControlList.x-amz-grant-write-acp

* requestParameters.accessControlList.x-amz-grant-full-control

* requestParameters.bucketName

* userIdentity.userName

* userIdentity.principalId

* userAgent

* bucketName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1530 | Data from Cloud Storage Object | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json


_version_: 2
</details>

---

### Detect New Open S3 buckets
This search looks for AWS CloudTrail events where a user has created an open/public S3 bucket.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/)
- **Last Updated**: 2021-07-19

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventSource=s3.amazonaws.com eventName=PutBucketAcl 
| rex field=_raw "(?<json_field>{.+})" 
| spath input=json_field output=grantees path=requestParameters.AccessControlPolicy.AccessControlList.Grant{} 
| search grantees=* 
| mvexpand grantees 
| spath input=grantees output=uri path=Grantee.URI 
| spath input=grantees output=permission path=Permission 
| search uri IN ("http://acs.amazonaws.com/groups/global/AllUsers","http://acs.amazonaws.com/groups/global/AuthenticatedUsers") 
| search permission IN ("READ","READ_ACP","WRITE","WRITE_ACP","FULL_CONTROL") 
| rename requestParameters.bucketName AS bucketName 
| stats count min(_time) as firstTime max(_time) as lastTime by user_arn userIdentity.principalId userAgent uri permission bucketName 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_new_open_s3_buckets_filter` 
```
#### Associated Analytic Story

* Suspicious AWS S3 Activities


#### How To Implement
You must install the AWS App for Splunk.

#### Required field

* _time

* eventSource

* eventName

* requestParameters.bucketName

* user_arn

* userIdentity.principalId

* userAgent

* uri

* permission



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1530 | Data from Cloud Storage Object | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1530/aws_s3_public_bucket/aws_cloudtrail_events.json


_version_: 3
</details>

---

### Detect Outbound SMB Traffic
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1071.002](https://attack.mitre.org/techniques/T1071/002/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` earliest(_time) as start_time latest(_time) as end_time values(All_Traffic.action) as action values(All_Traffic.app) as app values(All_Traffic.dest_ip) as dest_ip values(All_Traffic.dest_port) as dest_port values(sourcetype) as sourcetype count from datamodel=Network_Traffic where ((All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app="smb") AND NOT (All_Traffic.action="blocked" OR All_Traffic.dest_category="internal" OR All_Traffic.dest_ip=10.0.0.0/8 OR All_Traffic.dest_ip=172.16.0.0/12 OR All_Traffic.dest_ip=192.168.0.0/16 OR All_Traffic.dest_ip=100.64.0.0/10)) by All_Traffic.src_ip 
| `drop_dm_object_name("All_Traffic")` 
| `security_content_ctime(start_time)` 
| `security_content_ctime(end_time)` 
| `detect_outbound_smb_traffic_filter`
```
#### Associated Analytic Story

* Hidden Cobra Malware

* DHS Report TA18-074A

* NOBELIUM Group


#### 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 companys 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

#### Required field

* _time

* All_Traffic.action

* All_Traffic.app

* All_Traffic.dest_ip

* All_Traffic.dest_port

* sourcetype

* All_Traffic.dest_category

* All_Traffic.src_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1071.002 | File Transfer Protocols | Command And Control |


#### Kill Chain Phase

* Actions on Objectives

* Command and Control


#### 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.

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Detect Outlook exe writing a zip file
This search looks for execution of process `outlook.exe` where the process is writing a `.zip` file to the disk.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly`  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_name=outlook.exe OR Processes.process_name=explorer.exe by _time span=5m Processes.parent_process_id Processes.process_id Processes.dest Processes.process_name Processes.parent_process_name Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| rename process_id as malicious_id
| rename parent_process_id as outlook_id
| join malicious_id type=inner[
| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name  FROM datamodel=Endpoint.Filesystem where (Filesystem.file_path=*zip*   OR Filesystem.file_name=*.lnk ) AND (Filesystem.file_path=C:\\Users* OR Filesystem.file_path=*Local\\Temp*) by  _time span=5m Filesystem.process_id Filesystem.file_hash Filesystem.dest  
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| rename process_id as malicious_id
| fields malicious_id outlook_id dest file_path file_name file_hash count file_id] 
| table firstTime lastTime user malicious_id outlook_id process_name parent_process_name file_name  file_path 
| where file_name != "" 
| `detect_outlook_exe_writing_a_zip_file_filter` 
```
#### Associated Analytic Story

* Spearphishing Attachments


#### 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 product, such as Carbon Black, or endpoint data sources, such as Sysmon.

#### Required field

* _time

* Processes.process_name

* Processes.parent_process_id

* Processes.process_id

* Processes.dest

* Processes.parent_process_name

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Installation

* Actions on Objectives


#### Known False Positives
It is not uncommon for outlook to write legitimate zip files to the disk.

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Detect Pass the Hash
This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts using Pass-the-Hash technique.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1550.002](https://attack.mitre.org/techniques/T1550/002/)
- **Last Updated**: 2020-10-21

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval signature_id=map_get(input_event, "signature_id"), authentication_type=map_get(input_event, "authentication_type"), authentication_method=map_get(input_event, "authentication_method"), origin_device_domain=map_get(input_event, "origin_device_domain"), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null)

| where (authentication_type="3" AND authentication_method="NtLmSsp") OR (authentication_type="9" AND authentication_method="seclogo")

| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body=create_map(["event_id", event_id, "authentication_type", authentication_type, "authentication_method", authentication_method]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Lateral Movement


#### How To Implement
The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2

#### Required field

* signature_id

* authentication_type

* _time

* authentication_method

* origin_device_domain

* dest_user_id

* dest_device_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1550.002 | Pass the Hash | Defense Evasion, Lateral Movement |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate.

#### Reference


* Initial ESCU implementation by Bhavin Patel and Patrick Bareiss



#### Test Dataset


_version_: 1
</details>

---

### Detect Path Interception By Creation Of program exe
The detection Detect Path Interception By Creation Of program exe is detecting the abuse of unquoted service paths, which is a popular technique for privilege escalation. 

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1574.009](https://attack.mitre.org/techniques/T1574/009/)
- **Last Updated**: 2020-07-03

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| rex field=process "^.*?\\\\(?<service_process>[^\\\\]*\.(?:exe
|bat
|com
|ps1))" 
| eval process_name = lower(process_name) 
| eval service_process = lower(service_process) 
| where process_name != service_process 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_path_interception_by_creation_of_program_exe_filter`
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1574.009 | Path Interception by Unquoted Path | Persistence, Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
unknown

#### Reference


* https://medium.com/@SumitVerma101/windows-privilege-escalation-part-1-unquoted-service-path-c7a011a8d8ae



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.009/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Detect Port Security Violation
By enabling Port Security on a Cisco switch you can restrict input to an interface by limiting and identifying MAC addresses of the workstations that are allowed to access the port. When you assign secure MAC addresses to a secure port, the port does not forward packets with source addresses outside the group of defined addresses. If you limit the number of secure MAC addresses to one and assign a single secure MAC address, the workstation attached to that port is assured the full bandwidth of the port. If a port is configured as a secure port and the maximum number of secure MAC addresses is reached, when the MAC address of a workstation attempting to access the port is different from any of the identified secure MAC addresses, a security violation occurs.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557.002](https://attack.mitre.org/techniques/T1557/002/)
- **Last Updated**: 2020-10-28

<details>
  <summary>details</summary>

#### Search
```
`cisco_networks` (facility="PM" mnemonic="ERR_DISABLE" disable_cause="psecure-violation") OR (facility="PORT_SECURITY" mnemonic="PSECURE_VIOLATION" OR mnemonic="PSECURE_VIOLATION_VLAN") 
| eval src_interface=src_int_prefix_long+src_int_suffix 
| stats min(_time) AS firstTime max(_time) AS lastTime values(disable_cause) AS disable_cause values(src_mac) AS src_mac values(src_vlan) AS src_vlan values(action) AS action count by host src_interface 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_port_security_violation_filter`
```
#### Associated Analytic Story

* Router and Infrastructure Security


#### How To Implement
This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with Port Security and Error Disable for this to work (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst4500/12-2/25ew/configuration/guide/conf/port_sec.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.

#### Required field

* _time

* facility

* mnemonic

* disable_cause

* src_int_prefix_long

* src_int_suffix

* src_mac

* src_vlan

* action

* host

* src_interface



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1200 | Hardware Additions | Initial Access |
| T1498 | Network Denial of Service | Impact |
| T1557.002 | ARP Cache Poisoning | Credential Access, Collection |


#### Kill Chain Phase

* Reconnaissance

* Delivery

* Exploitation

* Actions on Objectives


#### Known False Positives
This search might be prone to high false positives if you have malfunctioning devices connected to your ethernet ports or if end users periodically connect physical devices to the network.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect Prohibited Applications Spawning cmd exe
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059/003/)
- **Last Updated**: 2020-11-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process_name Processes.process_name Processes.dest Processes.user
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|search [`prohibited_apps_launching_cmd`] 
| `detect_prohibited_applications_spawning_cmd_exe_filter`
```
#### Associated Analytic Story

* Suspicious Command-Line Executions

* Suspicious MSHTA Activity

* Suspicious Zoom Child Processes

* NOBELIUM Group


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.003 | Windows Command Shell | Execution |


#### Kill Chain Phase

* Exploitation


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/powershell_spawn_cmd/windows-sysmon.log


_version_: 5
</details>

---

### Detect Prohibited Applications Spawning cmd exe
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. This is a SPL2 implementation of the rule `Detect Prohibited Applications Spawning cmd.exe` by @bpatel.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/)
- **Last Updated**: 2020-7-13

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval process_name=ucast(map_get(input_event, "process_name"), "string", null), parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null)

| where process_name="cmd.exe" 
| rex field=parent_process "(?<field0>[^\\\\]+)$" 
| where field0="winword.exe" OR field0="excel.exe" OR field0="outlook.exe" OR field0="powerpnt.exe" OR field0="visio.exe" OR field0="mspub.exe" OR field0="acrobat.exe" OR field0="acrord32.exe" OR field0="chrome.exe" OR field0="iexplore.exe" OR field0="opera.exe" OR field0="firefox.exe" OR field0="java.exe" OR field0="powershell.exe"

| eval start_time=timestamp, end_time=timestamp, entities=mvappend(dest_device_id, dest_user_id), body=create_map(["event_id", event_id,  "process_name", process_name, "parent_process_name", parent_process]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Suspicious Command-Line Executions

* Suspicious MSHTA Activity

* Suspicious Zoom Child Processes

* Sunburst Malware


#### How To Implement
You must be ingesting sysmon logs. This search has been modified to process raw sysmon data from attack_range's nxlogs on DSP.

#### Required field

* process_name

* parent_process_name

* _time

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059 | Command and Scripting Interpreter | Execution |


#### Kill Chain Phase

* Exploitation


#### 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.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect PsExec With accepteula Flag
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021/002/)
- **Last Updated**: 2020-11-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*psexec* Processes.process=*accepteula* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_psexec_with_accepteula_flag_filter`
```
#### Associated Analytic Story

* SamSam Ransomware

* DHS Report TA18-074A

* HAFNIUM Group

* DarkSide Ransomware

* Lateral Movement


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.002 | SMB/Windows Admin Shares | Lateral Movement |


#### Kill Chain Phase

* Actions on Objectives


#### 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

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021.002/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Detect RClone Command-Line Usage
This analytic identifies commonly used command-line arguments used by `rclone.exe` to initiate a file transfer. Some arguments were negated as they are specific to the configuration used by adversaries. In particular, an adversary may list the files or directories of the remote file share using `ls` or `lsd`, which is not indicative of malicious behavior. During triage, at this stage of a ransomware event, exfiltration is about to occur or has already. Isolate the endpoint and continue investigating by review file modifications and parallel processes.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1020](https://attack.mitre.org/techniques/T1020/)
- **Last Updated**: 2021-05-13

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*copy*", "*mega*", "*pcloud*", "*ftp*", "*--config*", "*--progress*", "*--no-check-certificate*", "*--ignore-existing*", "*--auto-confirm*", "*--transfers*", "*--multi-thread-streams*")  by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process   Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|  `security_content_ctime(lastTime)` 
| `detect_rclone_command_line_usage_filter`
```
#### Associated Analytic Story

* DarkSide Ransomware

* Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1020 | Automated Exfiltration | Exfiltration |


#### Kill Chain Phase

* Exfiltration


#### Known False Positives
There is potential for false positives as these arguments may be used by other applications. Filter or tune the analytic as needed.

#### Reference


* https://redcanary.com/blog/rclone-mega-extortion/

* https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

* https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log


_version_: 1
</details>

---

### Detect Rare Executables
This search will return a table of rare processes, the names of the systems running them, and the users who initiated each process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2020-03-16

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.dest) as dest values(Processes.user) as user min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name  
| rename Processes.process_name as process 
| rex field=user "(?<user_domain>.*)\\\\(?<user_name>.*)" 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| search [
| tstats count from datamodel=Endpoint.Processes by Processes.process_name 
| rare Processes.process_name limit=30 
| rename Processes.process_name as process
| `filter_rare_process_allow_list`
| table process ] 
| `detect_rare_executables_filter` 
```
#### Associated Analytic Story

* Emotet Malware  DHS Report TA18-201A 

* Unusual Processes

* Cloud Federated Credential Abuse


#### 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_allow_list` searches two lookup files for allowed processes.  These consist of `rare_process_allow_list_default.csv` and `rare_process_allow_list_local.csv`. To add your own processes to the allow list, add them to `rare_process_allow_list_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 allow_list value for that process to false. You can modify the limit parameter and search scheduling to better suit your environment.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.process_name




#### Kill Chain Phase

* Installation

* Command and Control

* Actions on Objectives


#### Known False Positives
Some legitimate processes may be only rarely executed in your environment. As these are identified, update `rare_process_allow_list_local.csv` to filter them out of your search results.

#### Reference


#### Test Dataset


_version_: 5
</details>

---

### Detect Regasm Spawning a Process
The following analytic identifies regasm.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218/009/)
- **Last Updated**: 2021-02-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regasm.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regasm_spawning_a_process_filter`
```
#### Associated Analytic Story

* Suspicious Regsvcs Regasm Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.parent_process_name

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.009 | Regsvcs/Regasm | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

#### Reference


* https://attack.mitre.org/techniques/T1218/009/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/

* https://lolbas-project.github.io/lolbas/Binaries/Regasm/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Regasm with Network Connection
The following analytic identifies regasm.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218/009/)
- **Last Updated**: 2021-02-16

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regasm.exe 
| rename Computer as dest 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regasm_with_network_connection_filter`
```
#### Associated Analytic Story

* Suspicious Regsvcs Regasm Activity


#### 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.

#### Required field

* _time

* EventID

* dest_ip

* process_name

* Computer

* User

* src_ip

* dest_host

* dest_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.009 | Regsvcs/Regasm | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, limited instances of regasm.exe with a network connection may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

#### Reference


* https://attack.mitre.org/techniques/T1218/009/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

* https://lolbas-project.github.io/lolbas/Binaries/Regasm/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Regasm with no Command Line Arguments
The following analytic identifies regasm.exe with no command line arguments. This particular behavior occurs when another process injects into regasm.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218/009/)
- **Last Updated**: 2021-02-12

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (process_name=regasm.exe OR OriginalFileName=RegAsm.exe) 
| regex CommandLine="(regasm\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regasm_with_no_command_line_arguments_filter`
```
#### Associated Analytic Story

* Suspicious Regsvcs Regasm Activity


#### 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.

#### Required field

* _time

* EventID

* process_name

* OriginalFileName

* CommandLine

* dest

* User

* ParentImage

* ParentCommandLine

* process_path

* Computer



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.009 | Regsvcs/Regasm | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, limited instances of regasm.exe or may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

#### Reference


* https://attack.mitre.org/techniques/T1218/009/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

* https://lolbas-project.github.io/lolbas/Binaries/Regasm/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Regsvcs Spawning a Process
The following analytic identifies regsvcs.exe spawning a process. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. Spawning of a child process is rare from either process and should be investigated further. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218/009/)
- **Last Updated**: 2021-02-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=regsvcs.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regsvcs_spawning_a_process_filter`
```
#### Associated Analytic Story

* Suspicious Regsvcs Regasm Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.parent_process_name

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.009 | Regsvcs/Regasm | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, limited instances of regasm.exe or regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

#### Reference


* https://attack.mitre.org/techniques/T1218/009/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Regsvcs with Network Connection
The following analytic identifies Regsvcs.exe with a network connection to a public IP address, exluding private IP space. This particular technique has been used in the wild to bypass application control products. Regasm.exe and Regsvcs.exe are signed by Microsoft. By contacting a remote command and control server, the adversary will have the ability to escalate privileges and complete the objectives. During investigation, identify and retrieve the content being loaded. Review parallel processes for additional suspicious behavior. Gather any other file modifications and review accordingly. Review the reputation of the remote IP or domain and block as needed. regsvcs.exe and regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218/009/)
- **Last Updated**: 2021-02-16

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=3 dest_ip!=10.0.0.0/12 dest_ip!=172.16.0.0/12 dest_ip!=192.168.0.0/16 process_name=regsvcs.exe 
| rename Computer as dest 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, process_name, src_ip, dest_host, dest_ip 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regsvcs_with_network_connection_filter`
```
#### Associated Analytic Story

* Suspicious Regsvcs Regasm Activity


#### 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.

#### Required field

* _time

* EventID

* dest_ip

* process_name

* Computer

* User

* src_ip

* dest_host



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.009 | Regsvcs/Regasm | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

#### Reference


* https://attack.mitre.org/techniques/T1218/009/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Regsvcs with No Command Line Arguments
The following analytic identifies regsvcs.exe with no command line arguments. This particular behavior occurs when another process injects into regsvcs.exe, no command line arguments will be present. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Regasm.exe are natively found in C:\Windows\Microsoft.NET\Framework\v*\regasm|regsvcs.exe and C:\Windows\Microsoft.NET\Framework64\v*\regasm|regsvcs.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1218.009](https://attack.mitre.org/techniques/T1218/009/)
- **Last Updated**: 2021-02-12

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (process_name=regsvcs.exe OR OriginalFileName=RegSvcs.exe) 
| regex CommandLine="(regsvcs\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_regsvcs_with_no_command_line_arguments_filter`
```
#### Associated Analytic Story

* Suspicious Regsvcs Regasm Activity


#### 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.

#### Required field

* _time

* EventID

* process_name

* OriginalFileName

* CommandLine

* dest

* User

* ParentImage

* ParentCommandLine

* OriginalFileName

* process_path

* Computer



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.009 | Regsvcs/Regasm | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, limited instances of regsvcs.exe may cause a false positive. Filter based endpoint usage, command line arguments, or process lineage.

#### Reference


* https://attack.mitre.org/techniques/T1218/009/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.009/T1218.009.md

* https://lolbas-project.github.io/lolbas/Binaries/Regsvcs/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.009/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Regsvr32 Application Control Bypass
Adversaries may abuse Regsvr32.exe to proxy execution of malicious code. Regsvr32.exe is a command-line program used to register and unregister object linking and embedding controls, including dynamic link libraries (DLLs), on Windows systems. Regsvr32.exe is also a Microsoft signed binary.This variation of the technique is often referred to as a "Squiblydoo" attack. \
Upon investigating, look for network connections to remote destinations (internal or external). Be cautious to modify the query to look for "scrobj.dll", the ".dll" is not required to load scrobj. "scrobj.dll" will be loaded by "regsvr32.exe" upon execution. 

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.010](https://attack.mitre.org/techniques/T1218/010/)
- **Last Updated**: 2021-01-28

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=regsvr32.exe OR Processes.process_name!=regsvr32.exe) Processes.process=*scrobj* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_regsvr32_application_control_bypass_filter`
```
#### Associated Analytic Story

* Suspicious Regsvr32 Activity

* Cobalt Strike


#### 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. Tune the query by modifying/removing the !=regsv32.exe.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.010 | Regsvr32 | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Limited false positives related to third party software registering .DLL's.

#### Reference


* https://attack.mitre.org/techniques/T1218/010/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md

* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/

* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Renamed 7-Zip
The following analytic identifies renamed 7-Zip usage using Sysmon. At this stage of an attack, review parallel processes and file modifications for data that is staged or potentially have been exfiltrated. This analytic utilizes the OriginalFileName to capture the renamed process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1560.001](https://attack.mitre.org/techniques/T1560/001/)
- **Last Updated**: 2021-05-19

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (OriginalFileName=7z*.exe AND process_name!=7z*.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_renamed_7_zip_filter`
```
#### Associated Analytic Story

* Collection and Staging


#### 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.

#### Required field

* _time

* dest

* User

* parent_process_name

* process_name

* OriginalFileName

* process_path

* CommandLine

* Product



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1560.001 | Archive via Utility | Collection |


#### Kill Chain Phase

* Exfiltration


#### Known False Positives
Limited false positives, however this analytic will need to be modified for each environment if Sysmon is not used.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1560.001/T1560.001.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log


_version_: 1
</details>

---

### Detect Renamed PSExec
The following analytic identifies renamed instances of `PsExec.exe` being utilized on an endpoint. Most instances, it is highly probable to capture `Psexec.exe` or other SysInternal utility usage with the command-line argument of `-accepteula`. In this instance, we are using `OriginalFileName` from Sysmon to identify `PsExec` usage. During triage, validate this is the legitimate version of `PsExec` by review the PE metadata. In addition, review parallel processes for further suspicious behavior.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1569.002](https://attack.mitre.org/techniques/T1569/002/)
- **Last Updated**: 2021-05-19

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (OriginalFileName=psexec.c process_name!=psexec.exe process_name!=PsExec64.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine Product 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_renamed_psexec_filter`
```
#### Associated Analytic Story

* SamSam Ransomware

* DHS Report TA18-074A

* HAFNIUM Group

* DarkSide Ransomware

* Lateral Movement


#### 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. Tune and filter known instances where renamed rundll32.exe may be used.

#### Required field

* _time

* dest

* User

* parent_process_name

* process_name

* OriginalFileName

* process_path

* CommandLine

* Product



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1569.002 | Service Execution | Execution |


#### Kill Chain Phase

* Exploitation

* Lateral Movement

* Execution


#### Known False Positives
Limited false positives should be present. It is possible some third party applications may use older versions of PsExec, filter as needed.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1569.002/T1569.002.yaml

* https://redcanary.com/blog/threat-hunting-psexec-lateral-movement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Renamed RClone
The following analytic identifies the usage of `rclone.exe`, renamed, being used to exfiltrate data to a remote destination. RClone has been used by multiple ransomware groups to exfiltrate data. In many instances, it will be downloaded from the legitimate site and executed accordingly. During triage, isolate the endpoint and begin to review parallel processes for additional behavior. At this stage, the adversary may have staged data to be exfiltrated.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1020](https://attack.mitre.org/techniques/T1020/)
- **Last Updated**: 2021-05-13

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 OriginalFileName=rclone.exe NOT process_name=rclone.exe 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_renamed_rclone_filter`
```
#### Associated Analytic Story

* DarkSide Ransomware

* Ransomware


#### 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.

#### Required field

* _time

* OriginalFileName

* process_name

* process_path

* CommandLine

* dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1020 | Automated Exfiltration | Exfiltration |


#### Kill Chain Phase

* Exfiltration


#### Known False Positives
False positives should be limited as this analytic identifies renamed instances of `rclone.exe`. Filter as needed if there is a legitimate business use case.

#### Reference


* https://redcanary.com/blog/rclone-mega-extortion/

* https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

* https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1020/windows-sysmon.log


_version_: 1
</details>

---

### Detect Renamed WinRAR
The following analtyic identifies renamed instances of `WinRAR.exe`. In most cases, it is not common for WinRAR to be used renamed, however it is common to be installed by a third party application and executed from a non-standard path. In this instance, we are using `OriginalFileName` from Sysmon to determine if the process is WinRAR. During triage, validate additional metadata from the binary that this is `WinRAR`. Review parallel processes and file modifications.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1560.001](https://attack.mitre.org/techniques/T1560/001/)
- **Last Updated**: 2021-05-19

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (Product=WinRAR OR OriginalFileName=WinRAR.exe) process_name!=rar.exe process_name!=winrar.exe 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine Product 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_renamed_winrar_filter`
```
#### Associated Analytic Story

* Collection and Staging


#### 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. Modify query for specific EDR products as needed.

#### Required field

* _time

* dest

* User

* parent_process_name

* process_name

* OriginalFileName

* process_path

* CommandLine

* Product



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1560.001 | Archive via Utility | Collection |


#### Kill Chain Phase

* Exploitation

* Exfiltration


#### Known False Positives
Unknown. It is possible third party applications use renamed instances of WinRAR.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1560.001/T1560.001.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1560.001/archive_utility/windows-sysmon.log


_version_: 1
</details>

---

### Detect Rogue DHCP Server
By enabling DHCP Snooping as a Layer 2 Security measure on the organization's network devices, we will be able to detect unauthorized DHCP servers handing out DHCP leases to devices on the network (Man in the Middle attack).

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1557](https://attack.mitre.org/techniques/T1557/)
- **Last Updated**: 2020-08-11

<details>
  <summary>details</summary>

#### Search
```
`cisco_networks` facility="DHCP_SNOOPING" mnemonic="DHCP_SNOOPING_UNTRUSTED_PORT" 
| stats min(_time) AS firstTime max(_time) AS lastTime count values(message_type) AS message_type values(src_mac) AS src_mac BY host 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)`
| `detect_rogue_dhcp_server_filter`
```
#### Associated Analytic Story

* Router and Infrastructure Security


#### How To Implement
This search uses a standard SPL query on logs from Cisco Network devices. The network devices must be configured with DHCP Snooping enabled (see https://www.cisco.com/c/en/us/td/docs/switches/lan/catalyst2960x/software/15-0_2_EX/security/configuration_guide/b_sec_152ex_2960-x_cg/b_sec_152ex_2960-x_cg_chapter_01101.html) and log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices.

#### Required field

* _time

* facility

* mnemonic

* message_type

* src_mac

* host



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1200 | Hardware Additions | Initial Access |
| T1498 | Network Denial of Service | Impact |
| T1557 | Man-in-the-Middle | Credential Access, Collection |


#### Kill Chain Phase

* Reconnaissance

* Delivery

* Actions on Objectives


#### Known False Positives
This search might be prone to high false positives if DHCP Snooping has been incorrectly configured or in the unlikely event that the DHCP server has been moved to another network interface.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect Rundll32 Application Control Bypass - advpack
The following analytic identifies rundll32.exe loading advpack.dll and ieadvpack.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-02-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*advpack* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_rundll32_application_control_bypass___advpack_filter`
```
#### Associated Analytic Story

* Suspicious Rundll32 Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, some legitimate applications may use advpack.dll or ieadvpack.dll, triggering a false positive.

#### Reference


* https://attack.mitre.org/techniques/T1218/011/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

* https://lolbas-project.github.io/lolbas/Binaries/Rundll32

* https://lolbas-project.github.io/lolbas/Libraries/Advpack/

* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Rundll32 Application Control Bypass - setupapi
The following analytic identifies rundll32.exe loading setupapi.dll and iesetupapi.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-02-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*setupapi* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_rundll32_application_control_bypass___setupapi_filter`
```
#### Associated Analytic Story

* Suspicious Rundll32 Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, some legitimate applications may use setupapi triggering a false positive.

#### Reference


* https://attack.mitre.org/techniques/T1218/011/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

* https://lolbas-project.github.io/lolbas/Binaries/Rundll32

* https://lolbas-project.github.io/lolbas/Libraries/Setupapi/

* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Rundll32 Application Control Bypass - syssetup
The following analytic identifies rundll32.exe loading syssetup.dll by calling the LaunchINFSection function on the command line. This particular technique will load script code from a file. Upon a successful execution, the following module loads may occur - clr.dll, jscript.dll and scrobj.dll. During investigation, identify script content origination. Generally, a child process will spawn from rundll32.exe, but that may be bypassed based on script code contents. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, review any network connections and obtain the script content executed. It's possible other files are on disk.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-02-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*syssetup* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_rundll32_application_control_bypass___syssetup_filter`
```
#### Associated Analytic Story

* Suspicious Rundll32 Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, some legitimate applications may use syssetup.dll, triggering a false positive.

#### Reference


* https://attack.mitre.org/techniques/T1218/011/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

* https://lolbas-project.github.io/lolbas/Binaries/Rundll32

* https://lolbas-project.github.io/lolbas/Libraries/Syssetup/

* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Rundll32 Inline HTA Execution
The following analytic identifies "rundll32.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. This type of behavior is commonly observed with fileless malware or application whitelisting bypass techniques. 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 "rundll32.exe" and its parent process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218/005/)
- **Last Updated**: 2021-01-20

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_rundll32_inline_hta_execution_filter`
```
#### Associated Analytic Story

* Suspicious MSHTA Activity

* NOBELIUM Group


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process

* Processes.process_name

* Processes.user

* Processes.dest

* Processes.parent_process_name

* Processes.parent_process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.005 | Mshta | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.

#### Reference


* https://github.com/redcanaryco/AtomicTestHarnesses

* https://redcanary.com/blog/introducing-atomictestharnesses/

* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect S3 access from a new IP
This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/)
- **Last Updated**: 2018-06-28

<details>
  <summary>details</summary>

#### Search
```
`aws_s3_accesslogs` http_status=200  [search `aws_s3_accesslogs` http_status=200 
| stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip 
| inputlookup append=t previously_seen_S3_access_from_remote_ip.csv 
| stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip 
| outputlookup previously_seen_S3_access_from_remote_ip.csv 
| eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) 
| where newIP=1 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| table bucket_name remote_ip]
| iplocation remote_ip 
|rename remote_ip as src_ip 
| table _time bucket_name src_ip City Country operation request_uri 
| `detect_s3_access_from_a_new_ip_filter`
```
#### Associated Analytic Story

* Suspicious AWS S3 Activities


#### 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.

#### Required field

* _time

* http_status

* bucket_name

* remote_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1530 | Data from Cloud Storage Object | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### 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

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect SNICat SNI Exfiltration
This search looks for commands that the SNICat tool uses in the TLS SNI field.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1041](https://attack.mitre.org/techniques/T1041/)
- **Last Updated**: 2020-10-21

<details>
  <summary>details</summary>

#### Search
```
`zeek_ssl` 
| rex field=server_name "(?<snicat>(LIST
|LS
|SIZE
|LD
|CB
|CD
|EX
|ALIVE
|EXIT
|WHERE
|finito)-[A-Za-z0-9]{16}\.)" 
| stats count by src_ip dest_ip server_name snicat 
| where count>0 
| table src_ip dest_ip server_name snicat 
| `detect_snicat_sni_exfiltration_filter`
```
#### Associated Analytic Story

* Data Exfiltration


#### How To Implement
You must be ingesting Zeek SSL data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting when any of the predefined SNICat commands are found within the server_name (SNI) field. These commands are LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito.  You can go further once this has been detected, and run other searches to decode the SNI data to prove or disprove if any data exfiltration has taken place.

#### Required field

* _time

* server_name

* src_ip

* dest_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1041 | Exfiltration Over C2 Channel | Exfiltration |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Unknown

#### Reference


* https://www.mnemonic.no/blog/introducing-snicat/

* https://github.com/mnemonic-no/SNIcat

* https://attack.mitre.org/techniques/T1041/



#### Test Dataset


_version_: 1
</details>

---

### Detect SharpHound Command-Line Arguments
The following analytic identifies common command-line arguments used by SharpHound `-collectionMethod` and `invoke-bloodhound`. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1087.002](https://attack.mitre.org/techniques/T1087/002/), [T1087.001](https://attack.mitre.org/techniques/T1087/001/), [T1482](https://attack.mitre.org/techniques/T1482/), [T1069.002](https://attack.mitre.org/techniques/T1069/002/), [T1069.001](https://attack.mitre.org/techniques/T1069/001/)
- **Last Updated**: 2021-06-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_sharphound_command_line_arguments_filter`
```
#### Associated Analytic Story

* Discovery Techniques

* Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1087.002 | Domain Account | Discovery |
| T1087.001 | Local Account | Discovery |
| T1482 | Domain Trust Discovery | Discovery |
| T1069.002 | Domain Groups | Discovery |
| T1069.001 | Local Groups | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
False positives should be limited as the arguments used are specific to SharpHound. Filter as needed or add more command-line arguments as needed.

#### Reference


* https://attack.mitre.org/software/S0521/

* https://thedfirreport.com/?s=bloodhound

* https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors

* https://github.com/BloodHoundAD/SharpHound3

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log


_version_: 1
</details>

---

### Detect SharpHound File Modifications
SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. `-randomizefilenames` and `-encryptzip` are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename `20210601181553_BloodHound.zip`. SharpHound creates multiple temp files following the same pattern `20210601182121_computers.json`, `domains.json`, `gpos.json`, `ous.json` and `users.json`. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the `.ps1` ingestor will be PowerShell.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1087.002](https://attack.mitre.org/techniques/T1087/002/), [T1087.001](https://attack.mitre.org/techniques/T1087/001/), [T1482](https://attack.mitre.org/techniques/T1482/), [T1069.002](https://attack.mitre.org/techniques/T1069/002/), [T1069.001](https://attack.mitre.org/techniques/T1069/001/)
- **Last Updated**: 2021-05-27

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*bloodhound.zip", "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_sharphound_file_modifications_filter`
```
#### Associated Analytic Story

* Discovery Techniques

* Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.

#### Required field

* _time

* file_path

* dest

* file_name

* process_id

* file_create_time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1087.002 | Domain Account | Discovery |
| T1087.001 | Local Account | Discovery |
| T1482 | Domain Trust Discovery | Discovery |
| T1069.002 | Domain Groups | Discovery |
| T1069.001 | Local Groups | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed.

#### Reference


* https://attack.mitre.org/software/S0521/

* https://thedfirreport.com/?s=bloodhound

* https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors

* https://github.com/BloodHoundAD/SharpHound3

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log


_version_: 1
</details>

---

### Detect SharpHound Usage
The following analytic identifies SharpHound binary usage by using the `OriginalFileName` from Sysmon. In addition to renaming the PE, other coverage is available to detect command-line arguments. This particular analytic only looks for the OriginalFileName of `SharpHound.exe`. It is possible older instances of SharpHound.exe have different original filenames. Dependent upon the operator, the code may be re-compiled and the attributes removed or changed to anything else. During triage, review the metadata of the binary in question. Review parallel processes for suspicious behavior. Identify the source of this binary.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1087.002](https://attack.mitre.org/techniques/T1087/002/), [T1087.001](https://attack.mitre.org/techniques/T1087/001/), [T1482](https://attack.mitre.org/techniques/T1482/), [T1069.002](https://attack.mitre.org/techniques/T1069/002/), [T1069.001](https://attack.mitre.org/techniques/T1069/001/)
- **Last Updated**: 2021-05-27

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (OriginalFileName=SharpHound.exe process_name!=sharphound.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine Product 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_sharphound_usage_filter`
```
#### Associated Analytic Story

* Discovery Techniques

* Ransomware


#### 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.

#### Required field

* _time

* dest

* User

* parent_process_name

* process_name

* OriginalFileName

* process_path

* CommandLine

* Product



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1087.002 | Domain Account | Discovery |
| T1087.001 | Local Account | Discovery |
| T1482 | Domain Trust Discovery | Discovery |
| T1069.002 | Domain Groups | Discovery |
| T1069.001 | Local Groups | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
False positives should be limited as this is specific to a file attribute not used by anything else. Filter as needed.

#### Reference


* https://attack.mitre.org/software/S0521/

* https://thedfirreport.com/?s=bloodhound

* https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors

* https://github.com/BloodHoundAD/SharpHound3

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/sharphound/windows-sysmon.log


_version_: 1
</details>

---

### Detect Software Download To Network Device
Adversaries may abuse netbooting to load an unauthorized network device operating system from a Trivial File Transfer Protocol (TFTP) server. TFTP boot (netbooting) is commonly used by network administrators to load configuration-controlled network device images from a centralized management server. Netbooting is one option in the boot sequence and can be used to centralize, manage, and control device images.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1542.005](https://attack.mitre.org/techniques/T1542/005/)
- **Last Updated**: 2020-10-28

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.transport=udp AND All_Traffic.dest_port=69) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=21) OR (All_Traffic.transport=tcp AND All_Traffic.dest_port=22) AND All_Traffic.dest_category!=common_software_repo_destination AND All_Traffic.src_category=network OR All_Traffic.src_category=router OR All_Traffic.src_category=switch by All_Traffic.src All_Traffic.dest All_Traffic.dest_port 
| `drop_dm_object_name("All_Traffic")` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_software_download_to_network_device_filter`
```
#### Associated Analytic Story

* Router and Infrastructure Security


#### How To Implement
This search looks for Network Traffic events to TFTP, FTP or SSH/SCP ports from network devices. Make sure to tag any network devices as network, router or switch in order for this detection to work. If the TFTP traffic doesn't traverse a firewall nor packet inspection, these events will not be logged. This is typically an issue if the TFTP server is on the same subnet as the network device. There is also a chance of the network device loading software using a DHCP assigned IP address (netboot) which is not in the Asset inventory.

#### Required field

* _time

* All_Traffic.transport

* All_Traffic.dest_port

* All_Traffic.dest_category

* All_Traffic.src_category

* All_Traffic.src

* All_Traffic.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1542.005 | TFTP Boot | Defense Evasion, Persistence |


#### Kill Chain Phase

* Delivery


#### Known False Positives
This search will also report any legitimate attempts of software downloads to network devices as well as outbound SSH sessions from network devices.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect Spike in AWS Security Hub Alerts for EC2 Instance
This search looks for a spike in number of of AWS security Hub alerts for an EC2 instance in 4 hours intervals

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2021-01-26

<details>
  <summary>details</summary>

#### Search
```
`aws_securityhub_finding` "Resources{}.Type"=AWSEC2Instance 
| bucket span=4h _time 
| stats count AS alerts values(Title) as Title values(Types{}) as Types values(vendor_account) as vendor_account values(vendor_region) as vendor_region values(severity) as severity by _time dest 
| eventstats avg(alerts) as total_alerts_avg, stdev(alerts) as total_alerts_stdev 
| eval threshold_value = 3 
| eval isOutlier=if(alerts > total_alerts_avg+(total_alerts_stdev * threshold_value), 1, 0) 
| search isOutlier=1 
| table _time dest alerts Title Types vendor_account vendor_region severity isOutlier total_alerts_avg 
| `detect_spike_in_aws_security_hub_alerts_for_ec2_instance_filter`
```
#### Associated Analytic Story

* AWS Security Hub Alerts


#### 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 Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.

#### Required field

* _time

* Resources{}.Type

* Title

* Types{}

* vendor_account

* vendor_region

* severity

* dest




#### Kill Chain Phase


#### Known False Positives
None

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/suspicious_behaviour/security_hub_ec2_spike/security_hub_ec2_spike.json


_version_: 3
</details>

---

### Detect Spike in AWS Security Hub Alerts for User
This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2021-01-26

<details>
  <summary>details</summary>

#### Search
```
`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser 
| rename findings{}.Resources{}.Id as user 
| bucket span=4h _time 
| stats count AS alerts by _time user 
| eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev 
| eval threshold_value = 2 
| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) 
| search isOutlier=1 
| table _time user alerts 
|`detect_spike_in_aws_security_hub_alerts_for_user_filter`
```
#### Associated Analytic Story

* AWS Security Hub Alerts


#### 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 Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.

#### Required field

* _time

* findings{}.Resources{}.Type

* indings{}.Resources{}.Id

* user




#### Kill Chain Phase


#### Known False Positives
None

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Detect Spike in S3 Bucket deletion
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1530](https://attack.mitre.org/techniques/T1530/)
- **Last Updated**: 2018-11-27

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=DeleteBucket [search `cloudtrail` eventName=DeleteBucket 
| spath output=arn path=userIdentity.arn 
| stats count as apiCalls by arn 
| inputlookup s3_deletion_baseline append=t 
| fields - latestCount 
| stats values(*) as * by arn 
| rename apiCalls as latestCount 
| eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 
| eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) 
| eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) 
| table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls 
| outputlookup s3_deletion_baseline 
| eval dataPointThreshold = 15, deviationThreshold = 3 
| eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) 
| where isSpike=1 
| rename arn as userIdentity.arn 
| table userIdentity.arn] 
| spath output=user userIdentity.arn 
| spath output=bucketName path=requestParameters.bucketName 
| stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user 
| `detect_spike_in_s3_bucket_deletion_filter`
```
#### Associated Analytic Story

* Suspicious AWS S3 Activities


#### 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 AWS 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.

#### Required field

* _time

* eventName

* userIdentity.arn



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1530 | Data from Cloud Storage Object | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect Spike in blocked Outbound Traffic from your AWS
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-05-07

<details>
  <summary>details</summary>

#### Search
```
`cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  [search  `cloudwatchlogs_vpcflow` action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16)  
| stats count as numberOfBlockedConnections by src_ip 
| inputlookup baseline_blocked_outbound_connections append=t 
| fields - latestCount 
| stats values(*) as * by src_ip 
| rename numberOfBlockedConnections as latestCount 
| eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 
| eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) 
| eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) 
| table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections 
| outputlookup baseline_blocked_outbound_connections 
| eval dataPointThreshold = 5, deviationThreshold = 3 
| eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) 
| where isSpike=1 
| table src_ip] 
| stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip 
| `detect_spike_in_blocked_outbound_traffic_from_your_aws_filter`
```
#### Associated Analytic Story

* AWS Network ACL Activity

* Suspicious AWS Traffic

* Command and Control


#### 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.

#### Required field

* _time

* action

* src_ip

* dest_ip




#### Kill Chain Phase

* Actions on Objectives

* Command and Control


#### 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.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect Traffic Mirroring
Adversaries may leverage traffic mirroring in order to automate data exfiltration over compromised network infrastructure. Traffic mirroring is a native feature for some network devices and used for network analysis and may be configured to duplicate traffic and forward to one or more destinations for analysis by a network analyzer or other monitoring device.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1200](https://attack.mitre.org/techniques/T1200/), [T1498](https://attack.mitre.org/techniques/T1498/), [T1020.001](https://attack.mitre.org/techniques/T1020/001/)
- **Last Updated**: 2020-10-28

<details>
  <summary>details</summary>

#### Search
```
`cisco_networks` (facility="MIRROR" mnemonic="ETH_SPAN_SESSION_UP") OR (facility="SPAN" mnemonic="SESSION_UP") OR (facility="SPAN" mnemonic="PKTCAP_START") OR (mnemonic="CFGLOG_LOGGEDCMD" command="monitor session*") 
| stats min(_time) AS firstTime max(_time) AS lastTime count BY host facility mnemonic 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `detect_traffic_mirroring_filter`
```
#### Associated Analytic Story

* Router and Infrastructure Security


#### How To Implement
This search uses a standard SPL query on logs from Cisco Network devices. The network devices must log with a severity level of minimum "5 - notification". The search also requires that the Cisco Networks Add-on for Splunk (https://splunkbase.splunk.com/app/1467) is used to parse the logs from the Cisco network devices and that the devices have been configured according to the documentation of the Cisco Networks Add-on. Also note that an attacker may disable logging from the device prior to enabling traffic mirroring.

#### Required field

* _time

* facility

* mnemonic

* host



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1200 | Hardware Additions | Initial Access |
| T1498 | Network Denial of Service | Impact |
| T1020.001 | Traffic Duplication | Exfiltration |


#### Kill Chain Phase

* Delivery

* Actions on Objectives


#### Known False Positives
This search will return false positives for any legitimate traffic captures by network administrators.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect Unauthorized Assets by MAC address
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Sessions
- **ATT&CK**: 
- **Last Updated**: 2017-09-13

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST by All_Sessions.src_ip All_Sessions.dest_mac 
| dedup All_Sessions.dest_mac
| `drop_dm_object_name("Network_Sessions")`
|`drop_dm_object_name("All_Sessions")` 
| search NOT [
| inputlookup asset_lookup_by_str 
|rename mac as dest_mac 
| fields + dest_mac] 
| `detect_unauthorized_assets_by_mac_address_filter`
```
#### Associated Analytic Story

* Asset Tracking


#### 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.

#### Required field

* _time

* All_Sessions.signature

* All_Sessions.src_ip

* All_Sessions.dest_mac




#### Kill Chain Phase

* Reconnaissance

* Delivery

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect Use of cmd exe to Launch Script Interpreters
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

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059/003/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="cmd.exe" (Processes.process_name=cscript.exe OR Processes.process_name =wscript.exe) by Processes.parent_process Processes.process_name Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `detect_use_of_cmd_exe_to_launch_script_interpreters_filter`
```
#### Associated Analytic Story

* Emotet Malware  DHS Report TA18-201A 

* Suspicious Command-Line Executions


#### 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.

#### Required field

* _time

* Processes.process

* Processes.parent_process_name

* Processes.process_name

* Processes.parent_process

* Processes.user

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.003 | Windows Command Shell | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Some legitimate applications may exhibit this behavior.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/cmd_spawns_cscript/windows-sysmon.log


_version_: 4
</details>

---

### Detect WMI Event Subscription Persistence
The following analytic identifies the use of WMI Event Subscription to establish persistence or perform privilege escalation.  WMI can be used to install event filters, providers, consumers, and bindings that execute code when a defined event occurs. WMI subscription execution is proxied by the WMI Provider Host process (WmiPrvSe.exe) and thus may result in elevated SYSTEM privileges. This analytic is restricted by commonly added process execution and a path. If the volume is low enough, remove the values and flag on any new subscriptions.\
All event subscriptions have three components \
1. Filter - WQL Query for the events we want. EventID equals 19 \
1. Consumer - An action to take upon triggering the filter. EventID equals 20 \
1. Binding - Registers a filter to a consumer. EventID equals 21 \
Monitor for the creation of new WMI EventFilter, EventConsumer, and FilterToConsumerBinding. It may be pertinent to review all 3 to identify the flow of execution. In addition, EventCode 4104 may assist with any other PowerShell script usage that registered the subscription.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1546.003](https://attack.mitre.org/techniques/T1546/003/)
- **Last Updated**: 2021-06-16

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=20 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer User Destination 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_wmi_event_subscription_persistence_filter`
```
#### Associated Analytic Story

* Suspicious WMI Use


#### How To Implement
To successfully implement this search, you need to be ingesting logs with that provide WMI Event Subscription from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA and have enabled EventID 19, 20 and 21. Tune and filter known good to limit the volume.

#### Required field

* _time

* Destination

* Computer

* User



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1546.003 | Windows Management Instrumentation Event Subscription | Privilege Escalation, Persistence |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
It is possible some applications will create a consumer and may be required to be filtered. For tuning, add any additional LOLBin's for further depth of coverage.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.003/T1546.003.md

* https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/

* https://github.com/trustedsec/SysmonCommunityGuide/blob/master/WMI-events.md

* https://in.security/an-intro-into-abusing-and-identifying-wmi-event-subscriptions-for-persistence/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect Windows DNS SIGRed via Splunk Stream
This search detects SIGRed via Splunk Stream.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/)
- **Last Updated**: 2020-07-28

<details>
  <summary>details</summary>

#### Search
```
`stream_dns` 
| spath "query_type{}" 
| search "query_type{}" IN (SIG,KEY) 
| spath protocol_stack 
| search protocol_stack="ip:tcp:dns" 
| append [search `stream_tcp` bytes_out>65000] 
| `detect_windows_dns_sigred_via_splunk_stream_filter` 
| stats count by flow_id 
| where count>1 
| fields - count
```
#### Associated Analytic Story

* Windows DNS SIGRed CVE-2020-1350


#### How To Implement
You must be ingesting Splunk Stream DNS and Splunk Stream TCP. We are detecting SIG and KEY records via stream:dns and TCP payload over 65KB in size via stream:tcp.  Replace the macro definitions ('stream:dns' and 'stream:tcp') with configurations for your Splunk environment.

#### Required field

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1203 | Exploitation for Client Execution | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/



#### Test Dataset


_version_: 1
</details>

---

### Detect Windows DNS SIGRed via Zeek
This search detects SIGRed via Zeek DNS and Zeek Conn data.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Resolution
- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/)
- **Last Updated**: 2020-07-28

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where DNS.query_type IN (SIG,KEY) by DNS.flow_id 
| rename DNS.flow_id as flow_id 
| append [
| tstats  `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.bytes_in>65000 by All_Traffic.flow_id 
| rename All_Traffic.flow_id as flow_id] 
| `detect_windows_dns_sigred_via_zeek_filter` 
| stats count by flow_id 
| where count>1 
| fields - count 
```
#### Associated Analytic Story

* Windows DNS SIGRed CVE-2020-1350


#### How To Implement
You must be ingesting Zeek DNS and Zeek Conn data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting SIG and KEY records via bro:dns:json and TCP payload over 65KB in size via bro:conn:json.  The Network Resolution and Network Traffic datamodels are in use for this search.

#### Required field

* _time

* DNS.query_type

* DNS.flow_id

* All_Traffic.bytes_in

* All_Traffic.flow_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1203 | Exploitation for Client Execution | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://research.checkpoint.com/2020/resolving-your-way-into-domain-admin-exploiting-a-17-year-old-bug-in-windows-dns-servers/



#### Test Dataset


_version_: 1
</details>

---

### Detect Zerologon via Zeek
This search detects attempts to run exploits for the Zerologon CVE-2020-1472 vulnerability via Zeek RPC

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/)
- **Last Updated**: 2020-09-15

<details>
  <summary>details</summary>

#### Search
```
`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3) 
| bin span=5m _time 
| stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount by _time,src_ip,dest_ip 
| search opscount=3 authcount>4 passcount>0 
| search `detect_zerologon_via_zeek_filter`
```
#### Associated Analytic Story

* Detect Zerologon Attack


#### How To Implement
You must be ingesting Zeek DCE-RPC data into Splunk. Zeek data should also be getting ingested in JSON format.  We are detecting when all three RPC operations (NetrServerReqChallenge, NetrServerAuthenticate3, NetrServerPasswordSet2) are splunk_security_essentials_app via bro:rpc:json.  These three operations are then correlated on the Zeek UID field.

#### Required field

* _time

* operation



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1190 | Exploit Public-Facing Application | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.secura.com/blog/zero-logon

* https://github.com/SecuraBV/CVE-2020-1472

* https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2020-1472



#### Test Dataset


_version_: 1
</details>

---

### Detect attackers scanning for vulnerable JBoss servers
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Web
- **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/)
- **Last Updated**: 2017-09-23

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest 
| `drop_dm_object_name("Web")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_attackers_scanning_for_vulnerable_jboss_servers_filter`
```
#### Associated Analytic Story

* JBoss Vulnerability

* SamSam Ransomware


#### 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.

#### Required field

* _time

* Web.http_method

* Web.url

* Web.src

* Web.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1082 | System Information Discovery | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect hosts connecting to dynamic domain providers
Malicious actors often abuse legitimate Dynamic DNS services to host malicious payloads or interactive command and control nodes. Attackers will automate domain resolution changes by routing dynamic domains to countless IP addresses to circumvent firewall blocks, block lists as well as frustrate a network defenders analytic and investigative processes. This search will look for DNS queries made from within your infrastructure to suspicious dynamic domains.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Resolution
- **ATT&CK**: [T1189](https://attack.mitre.org/techniques/T1189/)
- **Last Updated**: 2021-01-14

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(DNS.answer) as answer min(_time) as firstTime from datamodel=Network_Resolution by DNS.query host 
| `drop_dm_object_name("DNS")` 
| `security_content_ctime(firstTime)` 
| `dynamic_dns_providers` 
| `detect_hosts_connecting_to_dynamic_domain_providers_filter`
```
#### Associated Analytic Story

* Data Protection

* Prohibited Traffic Allowed or Protocol Mismatch

* DNS Hijacking

* Suspicious DNS Traffic

* Dynamic DNS

* Command and Control


#### How To Implement
First, you'll need to ingest data from your DNS operations. This can be done by ingesting logs from your server or data, collected passively by Splunk Stream or a similar solution. Specifically, data that contains the domain that is being queried and the IP of the host originating the request must be populating the `Network_Resolution` data model. This search also leverages a lookup file, `dynamic_dns_providers_default.csv`, which contains a non-exhaustive list of Dynamic DNS providers. Please consider updating the local lookup periodically by adding new domains to the list of `dynamic_dns_providers_local.csv`.\
This search produces fields (query, answer, isDynDNS) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. These fields contribute additional context to the notable event. To see the additional metadata, add the following fields, if not already present, to Incident Review. Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry):\\n1. **Label:** DNS Query, **Field:** query\
1. \
1. **Label:** DNS Answer, **Field:** answer\
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`

#### Required field

* _time

* DNS.answer

* DNS.query

* host



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1189 | Drive-by Compromise | Initial Access |


#### Kill Chain Phase

* Command and Control

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1189/dyn_dns_site/windows-sysmon.log


_version_: 3
</details>

---

### Detect malicious requests to exploit JBoss servers
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Web
- **ATT&CK**: 
- **Last Updated**: 2017-09-23

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest 
| search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 
| `drop_dm_object_name("Web")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table src, dest_ip, http_method, url, firstTime, lastTime 
| `detect_malicious_requests_to_exploit_jboss_servers_filter`
```
#### Associated Analytic Story

* JBoss Vulnerability

* SamSam Ransomware


#### 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

#### Required field

* _time

* Web.http_method

* Web.url

* Web.url_length

* Web.src

* Web.dest




#### Kill Chain Phase

* Delivery


#### Known False Positives
No known false positives for this detection.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Detect mshta inline hta execution
The following analytic identifies "mshta.exe" execution with inline protocol handlers. "JavaScript", "VBScript", and "About" are the only supported options when invoking HTA content directly on the command-line. 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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218/005/)
- **Last Updated**: 2021-01-20

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=mshta.exe (Processes.process=*vbscript* OR Processes.process=*javascript* OR Processes.process=*about*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `detect_mshta_inline_hta_execution_filter`
```
#### Associated Analytic Story

* Suspicious MSHTA Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.005 | Mshta | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.

#### Reference


* https://github.com/redcanaryco/AtomicTestHarnesses

* https://redcanary.com/blog/introducing-atomictestharnesses/

* https://docs.microsoft.com/en-us/windows/win32/search/-search-3x-wds-extidx-prot-implementing



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log


_version_: 5
</details>

---

### Detect mshta renamed
The following analytic identifies renamed instances of mshta.exe executing. Mshta.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. This analytic utilizes the internal name of the PE to identify if is the legitimate mshta binary. Further analysis should be performed to review the executed content and validation it is the real mshta.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218/005/)
- **Last Updated**: 2021-01-20

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (OriginalFileName=mshta.exe AND process_name!=mshta.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_mshta_renamed_filter`
```
#### Associated Analytic Story

* Suspicious MSHTA Activity


#### 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.

#### Required field

* _time

* EventID

* OriginalFileName

* process_name

* Computer

* User

* parent_process_name

* process_path

* CommandLine



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.005 | Mshta | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, some legitimate applications may use a moved copy of mshta.exe, but never renamed, triggering a false positive.

#### Reference


* https://github.com/redcanaryco/AtomicTestHarnesses

* https://redcanary.com/blog/introducing-atomictestharnesses/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Detect processes used for System Network Configuration Discovery
This search looks for fast execution of processes used for system network configuration discovery on the endpoint.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1016](https://attack.mitre.org/techniques/T1016/)
- **Last Updated**: 2020-11-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where NOT Processes.user IN ("","unknown") by Processes.dest Processes.process_name Processes.user _time 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name(Processes)` 
| search `system_network_configuration_discovery_tools` 
| transaction dest connected=false maxpause=5m 
|where eventcount>=5 
| table firstTime lastTime dest user process_name process parent_process eventcount 
| `detect_processes_used_for_system_network_configuration_discovery_filter`
```
#### Associated Analytic Story

* Unusual Processes


#### 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 product, 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.parent_process

* Processes.original_file_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_path

* Processes.process_path

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1016 | System Network Configuration Discovery | Discovery |


#### Kill Chain Phase

* Installation

* Command and Control

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1016/discovery_commands/windows-sysmon.log


_version_: 2
</details>

---

### Detect shared ec2 snapshot
The following analytic utilizes AWS CloudTrail events to identify when an EC2 snapshot permissions are modified to be shared with a different AWS account. This method is used by adversaries to exfiltrate the EC2 snapshot.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1537](https://attack.mitre.org/techniques/T1537/)
- **Last Updated**: 2021-07-20

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=ModifySnapshotAttribute 
| rename requestParameters.createVolumePermission.add.items{}.userId as requested_account_id 
| search requested_account_id != NULL 
| eval match=if(requested_account_id==aws_account_id,"Match","No Match") 
| table _time user_arn src_ip requestParameters.attributeType requested_account_id aws_account_id match vendor_region user_agent 
| where match = "No Match" 
| `detect_shared_ec2_snapshot_filter` 
```
#### Associated Analytic Story

* Suspicious Cloud Instance Activities

* Data Exfiltration


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs.

#### Required field

* _time

* eventName

* user_arn

* src_ip

* requestParameters.attributeType

* aws_account_id

* vendor_region

* user_agent



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1537 | Transfer Data to Cloud Account | Exfiltration |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
It is possible that an AWS admin has legitimately shared a snapshot with others for  a specific purpose.

#### Reference


* https://labs.nettitude.com/blog/how-to-exfiltrate-aws-ec2-data/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1537/aws_snapshot_exfil/aws_cloudtrail_events.json


_version_: 2
</details>

---

### Detection of tools built by NirSoft
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1072](https://attack.mitre.org/techniques/T1072/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="* /stext *" OR Processes.process="* /scomma *" ) by Processes.parent_process Processes.process_name Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `detection_of_tools_built_by_nirsoft_filter`
```
#### Associated Analytic Story

* Emotet Malware  DHS Report TA18-201A 


#### 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.

#### Required field

* _time

* Processes.process

* Processes.parent_process

* Processes.process_name

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1072 | Software Deployment Tools | Execution, Lateral Movement |


#### Kill Chain Phase

* Installation

* Actions on Objectives


#### Known False Positives
While legitimate, these NirSoft tools are prone to abuse. You should verfiy that the tool was used for a legitimate purpose.

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Disable AMSI Through Registry
this search is to identify modification in registry to disable AMSI windows feature to evade detections. This technique was seen in several ransomware, RAT and even APT to impaire defenses of the compromise machine and to be able to execute payload with minimal alert as much as possible.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-06-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Script\\Settings\\AmsiEnable" Registry.registry_value_name = "DWORD (0x00000000)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disable_amsi_through_registry_filter`
```
#### Associated Analytic Story

* Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network operator may disable this feature of windows but not so common.

#### Reference


* https://blog.f-secure.com/hunting-for-amsi-bypasses/

* https://gist.github.com/rxwx/8955e5abf18dc258fd6b43a3a7f4dbf9



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log


_version_: 1
</details>

---

### Disable ETW Through Registry
this search is to identify modification in registry to disable ETW windows feature to evade detections. This technique was seen in several ransomware, RAT and even APT to impaire defenses of the compromise machine and to be able to execute payload with minimal alert as much as possible.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-06-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\.NETFramework\\ETWEnabled" Registry.registry_value_name = "DWORD (0x00000000)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disable_etw_through_registry_filter`
```
#### Associated Analytic Story

* Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network operator may disable this feature of windows but not so common.

#### Reference


* https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log


_version_: 1
</details>

---

### Disable Logs Using WevtUtil
This search is to detect execution of wevtutil.exe to disable logs. This technique was seen in several ransomware to disable the event logs to evade alerts and detections.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1070.001](https://attack.mitre.org/techniques/T1070/001/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "wevtutil.exe" Processes.process = "*sl*" Processes.process = "*/e:false*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disable_logs_using_wevtutil_filter`
```
#### Associated Analytic Story

* Ransomware


#### 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.

#### Required field

* _time

* Processes.parent_process_name

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.process_id

* Processes.process_guid



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070.001 | Clear Windows Event Logs | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network operator may disable audit event logs for debugging purposes.

#### Reference


* https://www.bleepingcomputer.com/news/security/new-ransom-x-ransomware-used-in-texas-txdot-cyberattack/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log


_version_: 1
</details>

---

### Disable Net User Account
This analytic will identify a suspicious command-line that disables a user account using the `net.exe` utility native to Windows. This technique may used by the adversaries to interrupt availability of such users to do their malicious act.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: Endpoint
- **ATT&CK**: [T1489](https://attack.mitre.org/techniques/T1489/)
- **Last Updated**: 2021-06-21

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=lower(ucast(map_get(input_event, "process"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line IS NOT NULL AND like(cmd_line, "%/active:no%") AND (process_name="net1.exe" OR process_name="net.exe") 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name, "process_path", process_path]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* XMRig

* Ransomware


#### 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. Tune and filter known instances where renamed net.exe/net1.exe may be used.

#### Required field

* _time

* dest_device_id

* process_name

* parent_process_name

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1489 | Service Stop | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network operator may use this approach to quickly disable an account but not a common practice.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/ssa_data1/net_user_dis.log


_version_: 1
</details>

---

### Disable Registry Tool
This search identifies modification of registry to disable the regedit or registry tools of the windows operating system. Since registry tool is a swiss knife in analyzing registry, malware such as RAT or trojan Spy disable this application to prevent the removal of their registry entry such as persistence, file less components and defense evasion.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disable_registry_tool_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin may disable this application for non technical user.

#### Reference


* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Disable Show Hidden Files
The following analytic is to identify a modification in the Windows registry to prevent users from seeing all the files with hidden attributes. This event or techniques are known on some worm and trojan spy malware that will drop hidden files on the infected machine.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1564.001](https://attack.mitre.org/techniques/T1564/001/), [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\Hidden" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\HideFileExt" Registry.registry_value_name = "DWORD (0x00000001)") OR (Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced\\ShowSuperHidden" Registry.registry_value_name = "DWORD (0x00000000)") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disable_show_hidden_files_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_nam



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1564.001 | Hidden Files and Directories | Defense Evasion |
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.sophos.com/en-us/threat-center/threat-analyses/viruses-and-spyware/W32~Tiotua-P/detailed-analysis.aspx



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Disable Windows App Hotkeys
This analytic detects a suspicious registry modification to disable Windows hotkey (shortcut keys) for native Windows applications. This technique is commonly used to disable certain or several Windows applications like `taskmgr.exe` and `cmd.exe`. This technique is used to impair the analyst in analyzing and removing the attacker implant in compromised systems.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-05-05

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\Windows NT\\CurrentVersion\\Image File Execution Options\\*" AND Registry.registry_value_name = "HotKey Disabled" AND Registry.registry_key_name = "Debugger" by Registry.dest Registry.user Registry.registry_value_name 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `disable_windows_app_hotkeys_filter`
```
#### Associated Analytic Story

* XMRig


#### 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 product, such as CarbonBlack 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.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.registry_value_name

* Registry.dest Registry.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log


_version_: 1
</details>

---

### Disable Windows Behavior Monitoring
This search is to identifies a modification in registry to disable the windows denfender real time behavior monitoring. This event or technique is commonly seen in RAT, bot, or Trojan to disable AV to evade detections.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableBehaviorMonitoring" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableOnAccessProtection" OR Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableScanOnRealtimeEnable" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows Defender\\Real-Time Protection\\DisableRealtimeMonitoring" OR Registry.registry_path= "*\\Real-Time Protection\\DisableIntrusionPreventionSystem" OR Registry.registry_path= "*\\Real-Time Protection\\DisableIOAVProtection" OR Registry.registry_path= "*\\Real-Time Protection\\DisableScriptScanning" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disable_windows_behavior_monitoring_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics

* Ransomware

* Revil Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin or user may choose to disable this windows features.

#### Reference


* https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Disable Windows SmartScreen Protection
The following search identifies a modification of registry to disable the smartscreen protection of windows machine. This is windows feature provide an early warning system against website that might engage in phishing attack or malware distribution. This modification are seen in RAT malware to cover their tracks upon downloading other of its component or other payload.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\SmartScreenEnabled"  Registry.registry_value_name = "Off" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disable_windows_smartscreen_protection_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_nam



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin or user may choose to disable this windows features.

#### Reference


* https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Disabling CMD Application
this search is to identify modification in registry to disable cmd prompt application. This technique is commonly seen in RAT, Trojan or WORM to prevent triaging or deleting there samples through cmd application which is one of the tool of analyst to traverse on directory and files.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Policies\\Microsoft\\Windows\\System\\DisableCMD" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disabling_cmd_application_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin may disable this application for non technical user.

#### Reference


* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Disabling ControlPanel
this search is to identify registry modification to disable control panel window. This technique is commonly seen in malware to prevent their artifacts , persistence removed on the infected machine.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoControlPanel" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_controlpanel_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin may disable this application for non technical user.

#### Reference


* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Disabling Firewall with Netsh
This search is to identifies suspicious firewall disabling using netsh application. this technique is commonly seen in malware that tries to communicate or download its component or other payload to its C2 server.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=netsh.exe Processes.process= "*firewall*" (Processes.process= "*off*" OR  Processes.process= "*disable*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_firewall_with_netsh_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin may disable firewall during testing or fixing network problem.

#### Reference


* https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.htm



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Disabling FolderOptions Windows Feature
This search is to identify registry modification to disable folder options feature of windows to show hidden files, file extension and etc. This technique used by malware in combination if disabling show hidden files feature to hide their files and also to hide the file extension to lure the user base on file icons or fake file extensions.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoFolderOptions" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_folderoptions_windows_feature_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin may disable this application for non technical user.

#### Reference


* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Disabling Net User Account
This analytic will identify a suspicious command-line that disables a user account using the `net.exe` utility native to Windows. This technique may used by the adversaries to interrupt availability of such users to do their malicious act.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1531](https://attack.mitre.org/techniques/T1531/)
- **Last Updated**: 2021-05-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="net.exe" OR Processes.process_name="net1.exe" AND Processes.process="*user*" AND Processes.process="*/active:no*" by  Processes.process_name Processes.dest Processes.user Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_net_user_account_filter`
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed net.exe/net1.exe may be used.

#### Required field

* _time

* Processes.process_name

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_id

* Processes.parent_process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1531 | Account Access Removal | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Disabling NoRun Windows App
This search is to identify modification of registry to disable run application in window start menu. this application is known to be a helpful shortcut to windows OS user to run known application and also to execute some reg or batch script. This technique is used malware to make cleaning of its infection more harder by preventing known application run easily through run shortcut.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\NoRun" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_norun_windows_app_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin may disable this application for non technical user.

#### Reference


* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry

* https://blog.malwarebytes.com/detections/pum-optional-norun/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Disabling Remote User Account Control
The search looks for modifications to registry keys that control the enforcement of Windows User Account Control (UAC).

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548/002/)
- **Last Updated**: 2020-11-18

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\EnableLUA* Registry.registry_value_name="DWORD (0x00000000)" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action 
| `drop_dm_object_name(Registry)` 
| `disabling_remote_user_account_control_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics

* Suspicious Windows Registry Activities


#### 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 product, 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.

#### Required field

* _time

* Registry.registry_path

* Registry.registry_value_name

* Registry.dest

* Registry.registry_key_name

* Registry.user

* Registry.action



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548.002 | Bypass User Account Control | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Disabling SystemRestore In Registry
The following search identifies the modification of registry related in disabling the system restore of a machine. This event or behavior are seen in some RAT malware to make the restore of the infected machine  difficult and keep their infection on the box.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableSR" OR Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows NT\\CurrentVersion\\SystemRestore\\DisableConfig" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `disabling_systemrestore_in_registry_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
in some cases admin can disable systemrestore on a machine.

#### Reference


* https://tccontre.blogspot.com/2020/01/remcos-rat-evading-windows-defender-av.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Disabling Task Manager
This search is to identifies modification of registry to disable the task manager of windows operating system. this event or technique are commonly seen in malware such as RAT, Trojan, TrojanSpy or worm to prevent the user to terminate their process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-31

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableTaskMgr" Registry.registry_value_name = "DWORD (0x00000001)" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `disabling_task_manager_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin may disable this application for non technical user.

#### Reference


* https://any.run/report/ea4ea08407d4ee72e009103a3b77e5a09412b722fdef67315ea63f22011152af/a866d7b1-c236-4f26-a391-5ae32213dfc4#registry

* https://blog.talosintelligence.com/2020/05/threat-roundup-0424-0501.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/win_app_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Discover DNS records
The search takes corporate and common cloud provider domains configured under `cim_corporate_email_domains.csv`, `cim_corporate_web_domains.csv`, and `cloud_domains.csv` finds their responses across the last 30 days from data in the `Network_Resolution ` datamodel, then stores the output under the `discovered_dns_records.csv` lookup

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Resolution
- **ATT&CK**: 
- **Last Updated**: 2019-02-14

<details>
  <summary>details</summary>

#### Search
```

| inputlookup cim_corporate_email_domains.csv 
| inputlookup append=T cim_corporate_web_domains.csv 
| inputlookup append=T cim_cloud_domains.csv 
| eval domain = trim(replace(domain, "\*", "")) 
| join domain [
|tstats `security_content_summariesonly` count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query 
| rename DNS.query as query 
| where query!="unknown" 
| rex field=query "(?<domain>\w+\.\w+?)(?:$
|/)"] 
| makemv delim=" " answer 
|  makemv delim=" " type 
| sort -count 
| table count,domain,type,query,answer 
| outputlookup createinapp=true discovered_dns_records
```
#### Associated Analytic Story

* DNS Hijacking


#### How To Implement
To successfully implement this search, you must be ingesting DNS logs, and populating the Network_Resolution data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation

#### Required field

* _time

* DNS.record_type

* DNS.answer

* DNS.query




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Download Files Using Telegram
The following analytic will identify a suspicious download by the Telegram application on a Windows system. This behavior was identified on a honeypot where the adversary gained access, installed Telegram and followed through with downloading different network scanners (port, bruteforcer, masscan) to the system and later used to mapped the whole network and further move laterally.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1105](https://attack.mitre.org/techniques/T1105/)
- **Last Updated**: 2021-05-06

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode= 15 process_name = "telegram.exe" TargetFilename = "*:Zone.Identifier" 
|stats count min(_time) as firstTime max(_time) as lastTime by Computer EventCode Image process_id TargetFilename Hash 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `download_files_using_telegram_filter`
```
#### Associated Analytic Story

* XMRig


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name and TargetFilename from your endpoints or Events that monitor filestream events which is happened when process download something. (EventCode 15) If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* Computer

* EventCode

* Image

* process_id

* TargetFilename

* Hash



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1105 | Ingress Tool Transfer | Command And Control |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
normal download of file in telegram app. (if it was a common app in network)

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/minergate/windows-sysmon.log


_version_: 1
</details>

---

### Drop IcedID License dat
This search is to detect dropping a suspicious file named as "license.dat" in %appdata%. This behavior seen in latest IcedID malware that contain the actual core bot that will be injected in other process to do banking stealing.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1204.002](https://attack.mitre.org/techniques/T1204/002/)
- **Last Updated**: 2021-07-30

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode= 11  TargetFilename = "*\\license.dat" AND (TargetFilename="*\\appdata\\*" OR TargetFilename="*\\programdata\\*") 
|stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id  process_name Computer 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_icedid_license_dat_filter`
```
#### Associated Analytic Story

* Icedid


#### 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. Tune and filter known instances where renamed rundll32.exe may be used.

#### Required field

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1204.002 | Malicious File | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.cisecurity.org/white-papers/security-primer-icedid/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Dump LSASS via comsvcs DLL
Detect the usage of comsvcs.dll for dumping the lsass process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003/001/)
- **Last Updated**: 2020-02-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dump_lsass_via_comsvcs_dll_filter`
```
#### Associated Analytic Story

* Credential Dumping

* Suspicious Rundll32 Activity

* HAFNIUM Group


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.001 | LSASS Memory | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://modexp.wordpress.com/2019/08/30/minidumpwritedump-via-com-services-dll/

* https://twitter.com/SBousseaden/status/1167417096374050817



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Dump LSASS via procdump
Detect procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. This query does not monitor for the internal name (OriginalFileName=procdump) of the PE or look for procdump64.exe. Modify the query as needed.\
During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003/001/)
- **Last Updated**: 2021-02-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=procdump.exe OR Processes.process_name=procdump64.exe (Processes.process=*-ma* OR Processes.process=*-mm*) Processes.process=*lsass* by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dump_lsass_via_procdump_filter`
```
#### Associated Analytic Story

* Credential Dumping

* HAFNIUM Group


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.001 | LSASS Memory | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://attack.mitre.org/techniques/T1003/001/

* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Dump LSASS via procdump Rename
Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.\
During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1003.001](https://attack.mitre.org/techniques/T1003/001/)
- **Last Updated**: 2021-02-01

<details>
  <summary>details</summary>

#### Search
```
`sysmon` OriginalFileName=procdump  process_name!=procdump*.exe  EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* 
| rename Computer as dest 
|  stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dump_lsass_via_procdump_rename_filter`
```
#### Associated Analytic Story

* Credential Dumping

* HAFNIUM Group


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* OriginalFileName

* process_name

* EventID

* CommandLine

* Computer

* parent_process_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.001 | LSASS Memory | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://attack.mitre.org/techniques/T1003/001/

* https://docs.microsoft.com/en-us/sysinternals/downloads/procdump

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.001/T1003.001.md#atomic-test-2---dump-lsassexe-memory-using-procdump



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Email Attachments With Lots Of Spaces
Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the file names.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Email
- **ATT&CK**: 
- **Last Updated**: 2017-09-19

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Email")` 
| eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) 
| search space_ratio >= 0.1 
|  rex field=recipient_address "(?<recipient_user>.*)@" 
| `email_attachments_with_lots_of_spaces_filter`
```
#### Associated Analytic Story

* Emotet Malware  DHS Report TA18-201A 

* Suspicious Emails


#### 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.

#### Required field

* _time

* All_Email.recipient

* All_Email.file_name

* All_Email.src_user

* All_Email.file_name

* All_Email.message_id




#### Kill Chain Phase

* Delivery


#### Known False Positives
None at this time

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Email files written outside of the Outlook directory
The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1114.001](https://attack.mitre.org/techniques/T1114/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.pst OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*"  Filesystem.file_path!="C:\\Users\\*\\AppData\\Local\\Microsoft\\Outlook*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest 
| `drop_dm_object_name("Filesystem")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `email_files_written_outside_of_the_outlook_directory_filter` 
```
#### Associated Analytic Story

* Collection and Staging


#### 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 product, 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.

#### Required field

* _time

* Filesystem.file_path

* Filesystem.file_name

* Filesystem.action

* Filesystem.process_id

* Filesystem.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1114.001 | Local Email Collection | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Email servers sending high volume traffic to hosts
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114/002/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d 
| `drop_dm_object_name("All_Traffic")` 
| eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out 
| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip 
| eval minimum_data_samples = 4, deviation_threshold = 3 
| where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") 
| eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) 
| table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average 
| `email_servers_sending_high_volume_traffic_to_hosts_filter`
```
#### Associated Analytic Story

* Collection and Staging

* HAFNIUM Group


#### 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.

#### Required field

* _time

* All_Traffic.bytes_out

* All_Traffic.src_category

* All_Traffic.dest_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1114.002 | Remote Email Collection | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Enable RDP In Other Port Number
This search is to detect a modification to registry to enable rdp to a machine with different port number. This technique was seen in some atttacker tries to do lateral movement and remote access to a compromised machine to gain control of it.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1021](https://attack.mitre.org/techniques/T1021/)
- **Last Updated**: 2021-05-19

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*HKLM\\SYSTEM\\CurrentControlSet\\Control\\Terminal Server\\WinStations\\RDP-Tcp*" Registry.registry_key_name = "PortNumber" by Registry.dest Registry.user Registry.registry_value_name 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `enable_rdp_in_other_port_number_filter`
```
#### Associated Analytic Story

* Prohibited Traffic Allowed or Protocol Mismatch


#### 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. Tune and filter known instances where renamed rundll32.exe may be used.

#### Required field

* _time

* Registry.registry_path

* Registry.dest

* Registry.user

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021 | Remote Services | Lateral Movement |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.mvps.net/docs/how-to-secure-remote-desktop-rdp/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log


_version_: 1
</details>

---

### Enumerate Users Local Group Using Telegram
This analytic will detect a suspicious Telegram process enumerating all network users in a local group. This technique was seen in a Monero infected honeypot to mapped all the users on the compromised system. EventCode 4798 is generated when a process enumerates a user's security-enabled local groups on a computer or device.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1087](https://attack.mitre.org/techniques/T1087/)
- **Last Updated**: 2021-05-06

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4798  Process_Name = "*\\telegram.exe" 
| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName EventCode Process_Name  Process_ID Account_Name Account_Domain Logon_ID Security_ID Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `enumerate_users_local_group_using_telegram_filter`
```
#### Associated Analytic Story

* XMRig


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the Task Schedule (Exa. Security Log EventCode 4798) endpoints. Tune and filter known instances of process like logonUI used in your environment.

#### Required field

* _time

* ComputerName

* EventCode

* Process_Name

* Process_ID

* Account_Name

* Account_Domain

* Logon_ID

* Security_ID

* Message



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1087 | Account Discovery | Discovery |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4798



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/minergate/windows-security.log


_version_: 1
</details>

---

### Eventvwr UAC Bypass
The following search identifies Eventvwr bypass by identifying the registry modification into a specific path that eventvwr.msc looks to (but is not valid) upon execution. A successful attack will include a suspicious command to be executed upon eventvwr.msc loading. Upon triage, review the parallel processes that have executed. Identify any additional registry modifications on the endpoint that may look suspicious. Remediate as necessary.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548/002/)
- **Last Updated**: 2021-03-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where  Registry.registry_path="*mscfile\\shell\\open\\command\\*"  by Registry.user, Registry.dest , Registry.registry_value_name
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `eventvwr_uac_bypass_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics

* Icedid


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.user

* Registry.dest

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548.002 | Bypass User Account Control | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation

* Privilege Escalation


#### Known False Positives
Some false positives may be present and will need to be filtered.

#### Reference


* https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md

* https://attack.mitre.org/techniques/T1548/002

* https://enigma0x3.net/2016/08/15/fileless-uac-bypass-using-eventvwr-exe-and-registry-hijacking/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Excel Spawning PowerShell
The following detection identifies Microsoft Excel spawning PowerShell. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003/002/)
- **Last Updated**: 2021-04-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe")  by Processes.parent_process Processes.process_name Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `excel_spawning_powershell_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest

* user

* parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.002 | Security Account Manager | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives should be limited, but if any are present, filter as needed.

#### Reference


* https://redcanary.com/threat-detection-report/techniques/powershell/

* https://attack.mitre.org/techniques/T1566/001/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log


_version_: 1
</details>

---

### Excel Spawning Windows Script Host
The following detection identifies Microsoft Excel spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Excel.exe. Excel.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64`. `cscript.exe` or `wscript.exe` spawning from Excel.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003/002/)
- **Last Updated**: 2021-04-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="excel.exe" Processes.process_name IN ("cscript.exe", "wscript.exe")  by Processes.parent_process Processes.process_name Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `excel_spawning_windows_script_host_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest

* user

* parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.002 | Security Account Manager | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives should be limited, but if any are present, filter as needed. In some instances, `cscript.exe` is used for legitimate business practices.

#### Reference


* https://app.any.run/tasks/8ecfbc29-03d0-421c-a5bf-3905d29192a2/

* https://attack.mitre.org/techniques/T1566/001/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log


_version_: 1
</details>

---

### Excessive Attempt To Disable Services
This analytic will identify suspicious series of command-line to disable several services. This technique is seen where the adversary attempts to disable security app services or other malware services to complete the objective on the compromised system.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1489](https://attack.mitre.org/techniques/T1489/)
- **Last Updated**: 2021-05-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where   Processes.process_name = "sc.exe" AND Processes.process="*config*" OR Processes.process="*Disabled*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m 
| where count >=5 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_attempt_to_disable_services_filter`
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed sc.exe may be used.

#### Required field

* _time

* Processes.process

* Processes.process_id

* Processes.process_name

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1489 | Service Stop | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Excessive DNS Failures
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Resolution
- **ATT&CK**: [T1071.004](https://attack.mitre.org/techniques/T1071/004/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values("DNS.query") as queries from datamodel=Network_Resolution where nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*" by "DNS.src","DNS.query"
| `drop_dm_object_name("DNS")`
| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain
| where isnull(domain)
| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank
| where isnull(rank)
| stats sum(count) as count mode(queries) as queries by src
| `get_asset(src)`
| where count>50 
| `excessive_dns_failures_filter`
```
#### Associated Analytic Story

* Suspicious DNS Traffic

* Command and Control


#### How To Implement
To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.

#### Required field

* _time

* DNS.query

* DNS.reply_code

* DNS.src



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1071.004 | DNS | Command And Control |


#### Kill Chain Phase

* Command and Control


#### 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.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Excessive Service Stop Attempt
This analytic identifies suspicious series of attempt to kill multiple services on a system using either `net.exe` or `sc.exe`. This technique is use by adversaries to terminate security services or other related services to continue there objective and evade detections.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1489](https://attack.mitre.org/techniques/T1489/)
- **Last Updated**: 2021-05-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "net.exe" OR  Processes.process_name = "sc.exe" OR  Processes.process_name = "net1.exe" AND Processes.process="*stop*" OR Processes.process="*delete*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m 
| where count >=5 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_service_stop_attempt_filter`
```
#### Associated Analytic Story

* XMRig

* Ransomware


#### 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. Tune and filter known instances where renamed sc.exe may be used.

#### Required field

* _time

* Processes.process

* Processes.process_id

* Processes.process_name

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1489 | Service Stop | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Excessive Usage Of Cacls App
The following analytic identifies excessive usage of `cacls.exe`, `xcacls.exe` or `icacls.exe` application to change file or folder permission. This behavior is commonly seen where the adversary attempts to impair some users from deleting or accessing its malware components or artifact from the compromised system.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1222](https://attack.mitre.org/techniques/T1222/)
- **Last Updated**: 2021-05-07

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id  values(Processes.process_name) as process_name count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "XCACLS.exe" by Processes.parent_process_name Processes.parent_process Processes.dest Processes.user _time span=1m 
| where count >=10 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_cacls_app_filter`
```
#### Associated Analytic Story

* XMRig


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process

* Processes.process_id

* Processes.process_name

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1222 | File and Directory Permissions Modification | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Administrators or administrative scripts may use this application. Filter as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Excessive Usage Of Net App
This analytic identifies excessive usage of `net.exe` or `net1.exe` within a bucket of time (1 minute). This behavior was seen in a Monero incident where the adversary attempts to create many users, delete and disable users as part of its malicious behavior.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1531](https://attack.mitre.org/techniques/T1531/)
- **Last Updated**: 2021-05-06

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "net.exe" OR Processes.process_name = "net1.exe" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user _time span=1m 
| where count >=10 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_net_app_filter`
```
#### Associated Analytic Story

* XMRig

* Ransomware


#### 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. Tune and filter known instances where renamed net.exe may be used.

#### Required field

* _time

* Processes.process

* Processes.process_id

* Processes.process_name

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1531 | Account Access Removal | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown. Filter as needed. Modify the time span as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Excessive Usage Of SC Service Utility
This search is to detect a suspicious excessive usage of sc.exe in a host machine. This technique was seen in several ransomware , xmrig and other malware to create, modify, delete or disable a service may related to security application or to gain privilege escalation.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1569.002](https://attack.mitre.org/techniques/T1569/002/)
- **Last Updated**: 2021-06-24

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode = 1 process_name = "sc.exe" 
|  bucket _time span=15m 
| stats values(process) as process count as numScExe by Computer, _time 
|  eventstats avg(numScExe) as avgScExe, stdev(numScExe) as stdScExe, count as numSlots by Computer 
|  eval upperThreshold=(avgScExe + stdScExe *3) 
|  eval isOutlier=if(avgScExe > 5 and avgScExe >= upperThreshold, 1, 0) 
|  search isOutlier=1 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_sc_service_utility_filter`
```
#### Associated Analytic Story

* Ransomware


#### 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. Tune and filter known instances where renamed taskkill.exe may be used.

#### Required field

* _time

* EventCode

* process_name

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1569.002 | Service Execution | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
excessive execution of sc.exe is quite suspicious since it can modify or execute app in high privilege permission.

#### Reference


* https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log


_version_: 1
</details>

---

### Excessive Usage Of Taskkill
This analytic identifies excessive usage of `taskkill.exe` application. This application is commonly used by adversaries to evade detections by killing security product processes or even other processes to evade detection.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-05-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "taskkill.exe"  by Processes.parent_process_name Processes.process_name Processes.dest Processes.user _time span=1m 
| where count >=10 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_taskkill_filter`
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed taskkill.exe may be used.

#### Required field

* _time

* Processes.parent_process_name

* Processes.process_name

* Processes.dest

* Processes.user

* Processes.process

* Processes.process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Unknown. Filter as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Excessive Usage of NSLOOKUP App
This search is to detect potential DNS exfiltration using nslookup application. This technique are seen in couple of malware and APT group to exfiltrated collected data in a infected machine or infected network. This detection is looking for unique use of nslookup where it tries to use specific record type (TXT, A, AAAA) that are commonly used by attacker and also the retry parameter which is designed to query C2 DNS multiple tries.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1048](https://attack.mitre.org/techniques/T1048/)
- **Last Updated**: 2021-04-21

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode = 1 process_name = "nslookup.exe" 
|  bucket _time span=15m 
| stats count as numNsLookup by Computer, _time 
|  eventstats avg(numNsLookup) as avgNsLookup, stdev(numNsLookup) as stdNsLookup, count as numSlots by Computer 
|  eval upperThreshold=(avgNsLookup + stdNsLookup *3) 
|  eval isOutlier=if(avgNsLookup > 20 and avgNsLookup >= upperThreshold, 1, 0) 
|  search isOutlier=1 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_usage_of_nslookup_app_filter`
```
#### Associated Analytic Story

* Suspicious DNS Traffic

* Dynamic DNS

* Command and Control

* Data Exfiltration


#### 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. Tune and filter known instances of nslookup.exe may be used.

#### Required field

* _time

* Computer

* process_name

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1048 | Exfiltration Over Alternative Protocol | Exfiltration |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.fireeye.com/blog/threat-research/2017/03/fin7_spear_phishing.html

* https://www.varonis.com/blog/dns-tunneling/

* https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/nslookup_exfil/windows-sysmon.log


_version_: 1
</details>

---

### Excessive number of distinct processes created in Windows Temp folder
This analytic will identify suspicious series of process executions.  We have observed that post exploit framework tools like Koadic and Meterpreter will launch an excessive number of processes with distinct file paths from Windows\Temp to execute actions on objective.  This behavior is extremely anomalous compared to typical application behaviors that use Windows\Temp.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/)
- **Last Updated**: 2021-06-03

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process distinct_count(Processes.process) as distinct_process_count  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*\\Windows\\Temp\\*" by Processes.dest Processes.user  _time span=20m 
| where distinct_process_count > 37 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_number_of_distinct_processes_created_in_windows_temp_folder_filter`
```
#### Associated Analytic Story

* meterpreter


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the full process path in the process field of CIM's Process data model. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed sc.exe may be used.

#### Required field

* _time

* Processes.process

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059 | Command and Scripting Interpreter | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Many benign applications will create processes from executables in Windows\Temp, although unlikely to exceed the given threshold.  Filter as needed.

#### Reference


* https://www.offensive-security.com/metasploit-unleashed/about-meterpreter/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/meterpreter/windows_temp_processes/logExcessiveWindowsTemp.log


_version_: 1
</details>

---

### Excessive number of service control start as disabled
This detection targets behaviors observed when threat actors have used sc.exe to modify services. We observed malware in a honey pot spawning numerous sc.exe processes in a short period of time, presumably to impair defenses, possibly to block others from compromising the same machine.  This detection will alert when we see both an excessive number of sc.exe processes launched with specific commandline arguments to disable the start of certain services.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-06-25

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` distinct_count(Processes.process) as distinct_cmdlines values(Processes.process_id) as process_ids min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "sc.exe" AND Processes.process="*start= disabled*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.parent_process_id, _time span=30m 
| where distinct_cmdlines >= 8 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_number_of_service_control_start_as_disabled_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Legitimate programs and administrators will execute sc.exe with the start disabled flag.  It is possible, but unlikely from the telemetry of normal Windows operation we observed, that sc.exe will be called more than seven times in a short period of time.

#### Reference


* https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/sc-create

* https://attack.mitre.org/techniques/T1562/001/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/sc_service_start_disabled/windows-sysmon.log


_version_: 1
</details>

---

### Excessive number of taskhost processes
This detection targets behaviors observed in post exploit kits like Meterpreter and Koadic that are run in memory.  We have observed that these tools must invoke an excessive number of taskhost.exe and taskhostex.exe processes to complete various actions (discovery, lateral movement, etc.).  It is extremely uncommon in the course of normal operations to see so many distinct taskhost and taskhostex processes running concurrently in a short time frame.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1033](https://attack.mitre.org/techniques/T1033/)
- **Last Updated**: 2021-06-07

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process_id) as process_ids  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE Processes.process_name = "taskhost.exe" OR Processes.process_name = "taskhostex.exe" BY Processes.dest Processes.process_name _time span=1h 
| `drop_dm_object_name(Processes)` 
| eval pid_count=mvcount(process_ids) 
| eval taskhost_count_=if(process_name == "taskhost.exe", pid_count, 0) 
| eval taskhostex_count_=if(process_name == "taskhostex.exe", pid_count, 0) 
| stats sum(taskhost_count_) as taskhost_count, sum(taskhostex_count_) as taskhostex_count by _time, dest, firstTime, lastTime 
| where taskhost_count > 10 and taskhostex_count > 10 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `excessive_number_of_taskhost_processes_filter`
```
#### Associated Analytic Story

* Meterpreter


#### How To Implement
To successfully implement this search you need to be ingesting events related to processes on the endpoints that include the name of the process and process id into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_id

* Processes.process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1033 | System Owner/User Discovery | Discovery |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Administrators, administrative actions or certain applications may run many instances of taskhost and taskhostex concurrently.  Filter as needed.

#### Reference


* https://attack.mitre.org/software/S0250/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059/meterpreter/taskhost_processes/logExcessiveTaskHost.log


_version_: 1
</details>

---

### Executables Or Script Creation In Suspicious Path
This analytic will identify suspicious executable or scripts (known file extensions) in list of suspicious file path in Windows. This technique is used by adversaries to evade detection. The suspicious file path are known paths used in the wild and are not common to have executable or scripts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/)
- **Last Updated**: 2021-05-06

<details>
  <summary>details</summary>

#### Search
```

|tstats `security_content_summariesonly` values(Filesystem.file_path) as file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = *.exe OR Filesystem.file_name = *.dll OR Filesystem.file_name = *.sys OR Filesystem.file_name = *.com OR Filesystem.file_name = *.vbs OR Filesystem.file_name = *.vbe OR Filesystem.file_name = *.js OR Filesystem.file_name = *.ps1 OR Filesystem.file_name = *.bat OR Filesystem.file_name = *.cmd OR Filesystem.file_name = *.pif) AND ( Filesystem.file_path = *\\windows\\fonts\\* OR Filesystem.file_path = *\\windows\\temp\\* OR Filesystem.file_path = *\\users\\public\\* OR Filesystem.file_path = *\\windows\\debug\\* OR Filesystem.file_path = *\\Users\\Administrator\\Music\\* OR Filesystem.file_path = *\\Windows\\servicing\\* OR Filesystem.file_path = *\\Users\\Default\\* OR Filesystem.file_path = *Recycle.bin* OR Filesystem.file_path = *\\Windows\\Media\\* OR Filesystem.file_path = *\\Windows\\repair\\* OR Filesystem.file_path = *\\AppData\\Local\\Temp*) by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `executables_or_script_creation_in_suspicious_path_filter`
```
#### Associated Analytic Story

* XMRig


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.

#### Required field

* _time

* Filesystem.file_path

* Filesystem.file_create_time

* Filesystem.process_id

* Filesystem.file_name

* Filesystem.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1036 | Masquerading | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Administrators may allow creation of script or exe in the paths specified. Filter as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Execute Javascript With Jscript COM CLSID
This analytic will identify suspicious process of cscript.exe where it tries to execute javascript using jscript.encode CLSID (COM OBJ). This technique was seen in ransomware (reddot ransomware) where it execute javascript with this com object with combination of amsi disabling technique.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.005](https://attack.mitre.org/techniques/T1059/005/)
- **Last Updated**: 2021-06-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cscript.exe" Processes.process="*-e:{F414C262-6AC0-11CF-B6D1-00AA00BBBB58}*" by Processes.parent_process_name Processes.process_name Processes.process Processes.parent_process Processes.process_id Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `execute_javascript_with_jscript_com_clsid_filter`
```
#### Associated Analytic Story

* Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.

#### Required field

* _time

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.parent_process

* Processes.process_id

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.005 | Visual Basic | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log


_version_: 1
</details>

---

### Execution of File with Multiple Extensions
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036/003/)
- **Last Updated**: 2020-11-18

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name(Processes)` 
| `execution_of_file_with_multiple_extensions_filter`
```
#### Associated Analytic Story

* Windows File Extension and Association Abuse

* Masquerading - Rename System Utilities


#### 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.

#### Required field

* _time

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1036.003 | Rename System Utilities | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Extract SAM from Registry
The following analytic identifies the use of `reg.exe` exporting Windows Registry hives containing credentials. Adversaries may use this technique to export registry hives for offline credential access attacks. Typically found executed from a untrusted process or script. Upon execution, a file will be written to disk.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003/002/)
- **Last Updated**: 2021-05-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=reg.exe (Processes.process=*save* OR Processes.process=*export*) AND (Processes.process=*sam* OR Processes.process=*system* OR Processes.process=*security*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `extract_sam_from_registry_filter`
```
#### Associated Analytic Story

* DarkSide Ransomware

* Credential Dumping


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.002 | Security Account Manager | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
It is possible some agent based products will generate false positives. Filter as needed.

#### Reference


* https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.002/T1003.002.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.002/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### File with Samsam Extension
The search looks for file writes with extensions consistent with a SamSam ransomware attack.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2018-12-14

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)`
| rex field=file_name "(?<file_extension>\.[^\.]+)$" 
| search file_extension=.stubbin OR file_extension=.berkshire OR file_extension=.satoshi OR file_extension=.sophos OR file_extension=.keyxml 
| `file_with_samsam_extension_filter`
```
#### Associated Analytic Story

* SamSam Ransomware


#### 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.

#### Required field

* _time

* Filesystem.user

* Filesystem.dest

* Filesystem.file_path

* Filesystem.file_name




#### Kill Chain Phase

* Installation


#### Known False Positives
Because these extensions are not typically used in normal operations, you should investigate all results.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/samsam_extension/windows-sysmon.log


_version_: 1
</details>

---

### First Time Seen Child Process of Zoom
This search looks for child processes spawned by zoom.exe or zoom.us that has not previously been seen.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/)
- **Last Updated**: 2020-05-20

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` min(_time) as firstTime values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_id) as parent_process_id values(Processes.process_name) as process_name values(Processes.process) as process from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_id Processes.dest 
| `drop_dm_object_name(Processes)` 
| lookup zoom_first_time_child_process dest as dest process_name as process_name OUTPUT firstTimeSeen 
| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_window`") 
| `security_content_ctime(firstTime)` 
| table firstTime dest, process_id, process_name, parent_process_id, parent_process_name 
|`first_time_seen_child_process_of_zoom_filter`
```
#### Associated Analytic Story

* Suspicious Zoom Child Processes


#### How To Implement
You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You should run the baseline search `Previously Seen Zoom Child Processes - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Zoom Child Processes - Update` to keep this table up to date and to age out old child processes. Please update the `previously_seen_zoom_child_processes_window` macro to adjust the time window.

#### Required field

* _time

* Processes.parent_process_name

* Processes.parent_process_id

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.process_id

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
A new child process of zoom isn't malicious by that fact alone. Further investigation of the actions of the child process is needed to verify any malicious behavior is taken.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1068/zoom_child_process/windows-sysmon.log


_version_: 1
</details>

---

### First Time Seen Running Windows Service
This search looks for the first and last time a Windows service is seen running in your environment. This table is then cached.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1569.002](https://attack.mitre.org/techniques/T1569/002/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_system` EventCode=7036 
| rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" 
| where state="running" 
| lookup previously_seen_running_windows_services service as service OUTPUT firstTimeSeen 
| where isnull(firstTimeSeen) OR firstTimeSeen > relative_time(now(), `previously_seen_windows_services_window`) 
| table _time dest service 
| `first_time_seen_running_windows_service_filter`
```
#### Associated Analytic Story

* Windows Service Abuse

* Orangeworm Attack Group

* NOBELIUM Group


#### How To Implement
While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows system event logs in order for this search to execute successfully. You should run the baseline search `Previously Seen Running Windows Services - Initial` to build the initial table of child processes and hostnames for this search to work. You should also schedule at the same interval as this search the second baseline search `Previously Seen Running Windows Services - Update` to keep this table up to date and to age out old Windows Services. Please update the `previously_seen_windows_services_window` macro to adjust the time window. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.

#### Required field

* _time

* EventCode

* Message

* dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1569.002 | Service Execution | Execution |


#### Kill Chain Phase

* Installation

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset


_version_: 4
</details>

---

### First time seen command line argument
This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. This is an implementation on SPL2 of the rule `First time seen command line argument` by @bpatel.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/), [T1117](https://attack.mitre.org/techniques/T1117/), [T1202](https://attack.mitre.org/techniques/T1202/)
- **Last Updated**: 2021-2-1

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), cmd_line_norm=lower(cmd_line), cmd_line_norm=replace(cmd_line_norm, /[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}/, "GUID"), cmd_line_norm=replace(cmd_line_norm, /(?<=\s)+\\[^:]*(?=\\.*\.\w{3}(\s
|$)+)/, "\\PATH"), /* replaces " \\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\w:\\[^:]*(?=\\.*\.\w{3}(\s
|$)+)/, "\\PATH"), /* replaces "C:\\Something\\Something\\command.ext" => "PATH\\command.ext" */ cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where process_name="cmd.exe" AND match_regex(ucast(cmd_line, "string", ""), /.* \/[cC] .*/)=true 
| select process_name, cmd_line, cmd_line_norm, timestamp, dest_device_id, dest_user_id 
| first_time_event input_columns=["cmd_line_norm"] 
| where first_time_cmd_line_norm 
| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Unusual Processes


#### How To Implement
You must be populating the endpoint data model for SSA and specifically the process_name and the process fields

#### Required field

* process_name

* _time

* dest_device_id

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059 | Command and Scripting Interpreter | Execution |
| T1117 | Regsvr32 |  |
| T1202 | Indirect Command Execution | Defense Evasion |


#### Kill Chain Phase

* Command and Control

* Actions on Objectives


#### 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

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### FodHelper UAC Bypass
Fodhelper.exe has a known UAC bypass as it attempts to look for specific registry keys upon execution, that do not exist. Therefore, an attacker can write its malicious commands in these registry keys to be executed by fodhelper.exe with the highest privilege. \
1. `HKCU:\Software\Classes\ms-settings\shell\open\command`\
1. `HKCU:\Software\Classes\ms-settings\shell\open\command\DelegateExecute`\
1. `HKCU:\Software\Classes\ms-settings\shell\open\command\(default)`\
Upon triage, fodhelper.exe will have a child process and read access will occur on the registry keys. Isolate the endpoint and review parallel processes for additional behavior.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1112](https://attack.mitre.org/techniques/T1112/), [T1548.002](https://attack.mitre.org/techniques/T1548/002/)
- **Last Updated**: 2021-03-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=fodhelper.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `fodhelper_uac_bypass_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics

* Icedid


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.parent_process_name

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1112 | Modify Registry | Defense Evasion |
| T1548.002 | Bypass User Account Control | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation

* Privilege Escalation


#### Known False Positives
Limited to no false positives are expected.

#### Reference


* https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md

* https://github.com/gushmazuko/WinBypass/blob/master/FodhelperBypass.ps1

* https://attack.mitre.org/techniques/T1548/002



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### GCP Detect gcploit framework
This search provides detection of GCPloit exploitation framework. This framework can be used to escalate privileges and move laterally from compromised high privilege accounts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2020-10-08

<details>
  <summary>details</summary>

#### Search
```
`google_gcp_pubsub_message` data.protoPayload.request.function.timeout=539s 
| table src src_user data.resource.labels.project_id data.protoPayload.request.function.serviceAccountEmail data.protoPayload.authorizationInfo{}.permission data.protoPayload.request.location http_user_agent 
| `gcp_detect_gcploit_framework_filter`
```
#### Associated Analytic Story

* GCP Cross Account Activity


#### How To Implement
You must install splunk GCP add-on. This search works with gcp:pubsub:message logs

#### Required field

* _time

* data.protoPayload.request.function.timeout

* src

* src_user

* data.resource.labels.project_id

* data.protoPayload.request.function.serviceAccountEmail

* data.protoPayload.authorizationInfo{}.permission

* data.protoPayload.request.location

* http_user_agent



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Lateral Movement


#### Known False Positives
Payload.request.function.timeout value can possibly be match with other functions or requests however the source user and target request account may indicate an attempt to move laterally accross acounts or projects

#### Reference


* https://github.com/dxa4481/gcploit

* https://www.youtube.com/watch?v=Ml09R38jpok



#### Test Dataset


_version_: 1
</details>

---

### GCP Kubernetes activity by src ip
This search provides investigation data about requests via user agent, authentication request URI, resource path and cluster name data against Kubernetes cluster from a specific IP address

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-04-13

<details>
  <summary>details</summary>

#### Search
```
`google_gcp_pubsub_message` 
| rename data.protoPayload.requestMetadata.callerIp as src_ip 
| search src_ip =$src_ip$ 
| stats count min(_time) as firstTime max(_time) as lastTime values(data.protoPayload.methodName) as method_names values(data.protoPayload.resourceName) as resource_name values(data.protoPayload.requestMetadata.callerSuppliedUserAgent) as http_user_agent values(data.protoPayload.authenticationInfo.principalEmail) as user values(data.protoPayload.status.message) by src_ip data.resource.labels.cluster_name data.resource.type
```
#### Associated Analytic Story

* Kubernetes Scanning Activity


#### 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.

#### Required field

* _time

* data.protoPayload.requestMetadata.callerIp

* data.protoPayload.methodName

* data.protoPayload.resourceName

* data.protoPayload.requestMetadata.callerSuppliedUserAgent

* data.protoPayload.authenticationInfo.principalEmail

* data.protoPayload.status.message

* data.resource.labels.cluster_name

* data.resource.type




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### GCP Kubernetes cluster pod scan detection
This search provides information of unauthenticated requests via user agent, and authentication data against Kubernetes cluster's pods

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1526](https://attack.mitre.org/techniques/T1526/)
- **Last Updated**: 2020-07-17

<details>
  <summary>details</summary>

#### Search
```
`google_gcp_pubsub_message` category=kube-audit 
|spath input=properties.log 
|search responseStatus.code=401 
|table sourceIPs{} userAgent verb requestURI responseStatus.reason properties.pod 
| `gcp_kubernetes_cluster_pod_scan_detection_filter`
```
#### Associated Analytic Story

* Kubernetes Scanning Activity


#### 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.

#### Required field

* _time

* category

* responseStatus.code

* sourceIPs{}

* userAgent

* verb

* requestURI

* responseStatus.reason

* properties.pod



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1526 | Cloud Service Discovery | Discovery |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
Not all unauthenticated requests are malicious, but frequency, User Agent, source IPs and pods  will provide context.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### GPUpdate with no Command Line Arguments with Network
The following analytic identifies gpupdate.exe with no command line arguments and with a network connection. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-04-19

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=gpupdate.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| regex process="(gpupdate\.exe.{0,4}$)" 
| join  process_id [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
| `drop_dm_object_name(Ports)` 
| rename  dest as connection_to_CNC] 
| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port 
| `gpupdate_with_no_command_line_arguments_with_network_filter`
```
#### Associated Analytic Story

* Cobalt Strike


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* EventID

* process_name

* process_id

* parent_process_name

* dest_port

* process_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives may be present in small environments. Tuning may be required based on parent process.

#### Reference


* https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile

* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log


_version_: 1
</details>

---

### Get All AWS Activity From City
This search retrieves all the activity from a specific city and will create a table containing the time, city, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-03-19

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` 
| iplocation sourceIPAddress 
| search City=$City$ 
| spath output=user path=userIdentity.arn 
| spath output=awsUserName path=userIdentity.userName 
| spath output=userType path=userIdentity.type 
| rename sourceIPAddress as src_ip 
| table _time, City, user, userName, userType, src_ip, awsRegion, eventName, errorCode
```
#### Associated Analytic Story

* AWS Suspicious Provisioning Activities


#### 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.

#### Required field

* _time

* sourceIPAddress

* userIdentity.arn

* userIdentity.userName

* userIdentity.type

* awsRegion

* eventName

* errorCode




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get All AWS Activity From Country
This search retrieves all the activity from a specific country and will create a table containing the time, country, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-03-19

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` 
| iplocation sourceIPAddress 
| search Country=$Country$ 
| spath output=user path=userIdentity.arn 
| spath output=awsUserName path=userIdentity.userName 
| spath output=userType path=userIdentity.type 
| rename sourceIPAddress as src_ip 
| table _time, Country, user, userName, userType, src_ip, awsRegion, eventName, errorCode
```
#### Associated Analytic Story

* AWS Suspicious Provisioning Activities


#### 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.

#### Required field

* _time

* sourceIPAddress

* userIdentity.arn

* userIdentity.userName

* userIdentity.type

* awsRegion

* eventName

* errorCode




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get All AWS Activity From IP Address
This search retrieves all the activity from a specific IP address and will create a table containing the time, ARN, username, the type of user, the IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-03-19

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` 
| iplocation sourceIPAddress 
| search src_ip=$src_ip$ 
| spath output=user path=userIdentity.arn 
| spath output=awsUserName path=userIdentity.userName 
| spath output=userType path=userIdentity.type 
| rename sourceIPAddress as src_ip 
| table _time, user, userName, userType, src_ip, awsRegion, eventName, errorCode
```
#### Associated Analytic Story

* AWS Network ACL Activity

* AWS Suspicious Provisioning Activities

* Command and Control

* Suspicious AWS S3 Activities

* Suspicious AWS Traffic

* Suspicious Cloud Instance Activities


#### 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.

#### Required field

* _time

* sourceIPAddress

* userIdentity.arn

* userIdentity.userName

* userIdentity.type

* awsRegion

* eventName

* errorCode




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get All AWS Activity From Region
This search retrieves all the activity from a specific geographic region and will create a table containing the time, geographic region, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful.

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-03-19

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` 
| iplocation sourceIPAddress 
| search Region=$Region$ 
| spath output=user path=userIdentity.arn 
| spath output=awsUserName path=userIdentity.userName 
| spath output=userType path=userIdentity.type 
| rename sourceIPAddress as src_ip 
| table _time, Region, user, userName, userType, src_ip, awsRegion, eventName, errorCode
```
#### Associated Analytic Story

* AWS Suspicious Provisioning Activities


#### 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.

#### Required field

* _time

* sourceIPAddress

* userIdentity.arn

* userIdentity.userName

* userIdentity.type

* awsRegion

* eventName

* errorCode




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get Backup Logs For Endpoint
This search will tell you the backup status from your netbackup_logs of a specific endpoint for the last week.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-09-14

<details>
  <summary>details</summary>

#### Search
```
`netbackup` COMPUTERNAME=$dest$ 
| rename COMPUTERNAME as dest, MESSAGE as signature 
| table _time, dest, signature
```
#### Associated Analytic Story

* Ransomware

* SamSam Ransomware


#### How To Implement
You must be ingesting your backup logs.

#### Required field

* _time

* COMPUTERNAME

* MESSAGE




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get Certificate logs for a domain
This search queries the Certificates datamodel and give you all the information for a specific domain. Please note that the certificates issued by "Let's Encrypt" are widely used by attackers.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2019-04-29

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Certificates.All_Certificates where All_Certificates.SSL.ssl_subject_common_name=*$domain$  by All_Certificates.dest All_Certificates.src All_Certificates.SSL.ssl_issuer_common_name All_Certificates.SSL.ssl_subject_common_name All_Certificates.SSL.ssl_hash 
| `drop_dm_object_name(All_Certificates)` 
| `drop_dm_object_name(SSL)` 
| rename ssl_subject_common_name as domain 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
```
#### Associated Analytic Story

* Common Phishing Frameworks


#### How To Implement
You must be ingesting your certificates or SSL logs from your network traffic into your Certificates datamodel. Please note the wildcard(*) before domain in the search syntax, we use to match for all domain and subdomain combinations

#### Required field

* _time

* All_Certificates.SSL.ssl_subject_common_name

* All_Certificates.dest

* All_Certificates.src

* All_Certificates.SSL.ssl_issuer_common_name

* All_Certificates.SSL.ssl_hash




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Get DNS Server History for a host
While investigating any detections it is important to understand which and how many DNS servers a host has connected to in the past. This search uses data that is tagged as DNS and gives you a count and list of DNS servers that a particular host has connected to the previous 24 hours.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-11-09

<details>
  <summary>details</summary>

#### Search
```

| search tag=dns src_ip=$src_ip$ dest_port=53 
| streamstats time_window=1d count values(dest_ip) as dcip by src_ip 
| table date_mday src_ip dcip count 
| sort -count
```
#### Associated Analytic Story

* AWS Network ACL Activity

* Command and Control

* DNS Hijacking

* Data Protection

* Dynamic DNS

* Hidden Cobra Malware

* Host Redirection

* Prohibited Traffic Allowed or Protocol Mismatch

* Suspicious AWS Traffic

* Suspicious DNS Traffic


#### How To Implement
To successfully implement this search, you must be ingesting your DNS traffic

#### Required field

* _time

* src_ip

* dest_port

* dest_ip




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get DNS traffic ratio
This search calculates the ratio of DNS traffic originating and coming from a host to a list of DNS servers over the last 24 hours. A high value of this ratio could be very useful to quickly understand if a src_ip (host) is sending a high volume of data out via port 53, could be an indicator of data exfiltration via DNS.  

- **Product**: Splunk Phantom
- **Datamodel**: Network_Traffic
- **ATT&CK**: 
- **Last Updated**: 2017-11-09

<details>
  <summary>details</summary>

#### Search
```

| tstats allow_old_summaries=true sum(All_Traffic.bytes_out) as "bytes_out" sum(All_Traffic.bytes_in) as "bytes_in" from datamodel=Network_Traffic where nodename=All_Traffic All_Traffic.dest_port=53 by All_Traffic.src All_Traffic.dest
| `drop_dm_object_name(All_Traffic)` 
| rename src as src_ip 
| rename dest as dest_ip 
| search src_ip=$src_ip$ 
| search dest_ip = $dest_ip 
| eval ratio = (bytes_out/bytes_in) 
| table ratio
```
#### Associated Analytic Story

* AWS Network ACL Activity

* Command and Control

* Data Protection

* Dynamic DNS

* Hidden Cobra Malware

* Suspicious AWS Traffic

* Suspicious DNS Traffic


#### How To Implement
You must be ingesting your network traffic

#### Required field

* _time

* All_Traffic.bytes_out

* All_Traffic.bytes_in

* All_Traffic.dest_port

* All_Traffic.src

* All_Traffic.dest




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get EC2 Instance Details by instanceId
This search queries AWS description logs and returns all the information about a specific instance via the instanceId field

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-02-12

<details>
  <summary>details</summary>

#### Search
```
`aws_description` 
| dedup id sortby -_time 
|rename id as instanceId
|  search instanceId=$instanceId$ 
| spath output=tags path=tags 
| eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) 
| table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags 
| rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC
```
#### Associated Analytic Story

* AWS Cryptomining

* Cloud Cryptomining

* Suspicious AWS EC2 Activities

* Unusual AWS EC2 Modifications

* AWS Security Hub Alerts


#### How To Implement
In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs.

#### Required field

* _time

* id

* ip_address

* tags

* aws_account_id

* placement

* instance_type

* key_name

* launch_time

* state

* vpc_id

* subnet_id




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get EC2 Launch Details
This search returns some of the launch details for a EC2 instance.

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-03-12

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` dest=$dest$ 
|rename userIdentity.arn as arn, responseElements.instancesSet.items{}.instanceId as dest, responseElements.instancesSet.items{}.privateIpAddress as privateIpAddress, responseElements.instancesSet.items{}.imageId as amiID, responseElements.instancesSet.items{}.architecture as architecture, responseElements.instancesSet.items{}.keyName as keyName 
| table arn, awsRegion, dest, architecture, privateIpAddress, amiID, keyName
```
#### Associated Analytic Story

* AWS Cryptomining

* Cloud Cryptomining

* Suspicious AWS EC2 Activities

* AWS Security Hub Alerts


#### How To Implement
In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs.

#### Required field

* _time

* dest

* userIdentity.arn

* responseElements.instancesSet.items{}.instanceId

* responseElements.instancesSet.items{}.privateIpAddress

* responseElements.instancesSet.items{}.imageId

* responseElements.instancesSet.items{}.architecture

* responseElements.instancesSet.items{}.keyName




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get Email Info
This search returns all the information Splunk might have collected a specific email message over the last 2 hours.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-11-09

<details>
  <summary>details</summary>

#### Search
```

| from datamodel Email.All_Email 
| search message_id=$message_id$
```
#### Associated Analytic Story

* Brand Monitoring

* Suspicious Emails


#### How To Implement
To successfully implement this search you must be ingesting your email logs or capturing unencrypted network traffic which contains email communications.

#### Required field

* _time

* message




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get Emails From Specific Sender
This search returns all the emails from a specific sender over the last 24 and next hours.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-11-09

<details>
  <summary>details</summary>

#### Search
```

| from datamodel Email.All_Email 
| search src_user=$src_user$
```
#### Associated Analytic Story

* Brand Monitoring

* Suspicious Emails

* Web Fraud Detection


#### How To Implement
To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model.

#### Required field

* _time

* src_user




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get First Occurrence and Last Occurrence of a MAC Address
This search allows you to gather more context around a notable which has detected a new device connecting to your network. Use this search to determine the first and last occurrences of the suspicious device attempting to connect with your network.

- **Product**: Splunk Phantom
- **Datamodel**: Network_Sessions
- **ATT&CK**: 
- **Last Updated**: 2017-09-13

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST All_Sessions.src_mac= $src_mac$ by All_Sessions.src_ip All_Sessions.user 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)`
```
#### Associated Analytic Story

* Asset Tracking


#### How To Implement
To successfully implement this search, you must be ingesting the logs from your DHCP server.

#### Required field

* _time

* All_Sessions.DHCP

* All_Sessions.signature

* All_Sessions.src_mac

* All_Sessions.src_ip

* All_Sessions.user




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get History Of Email Sources
This search returns a list of all email sources seen in the 48 hours prior to the notable event to 24 hours after, and the number of emails from each source.

- **Product**: Splunk Phantom
- **Datamodel**: Email
- **ATT&CK**: 
- **Last Updated**: 2019-02-21

<details>
  <summary>details</summary>

#### Search
```

|tstats `security_content_summariesonly` values(All_Email.dest) as dest values(All_Email.recipient) as recepient  min(_time) as firstTime max(_time) as lastTime count from datamodel=Email.All_Email by All_Email.src 
|`drop_dm_object_name(All_Email)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search src=$src$
```
#### Associated Analytic Story

* Emotet Malware  DHS Report TA18-201A 

* Hidden Cobra Malware

* Lateral Movement

* Malicious PowerShell

* Orangeworm Attack Group

* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

* Ransomware

* SamSam Ransomware


#### How To Implement
To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model.

#### Required field

* _time

* All_Email.dest

* All_Email.recipient

* All_Email.src




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get Logon Rights Modifications For Endpoint
This search allows you to retrieve any modifications to logon rights associated with a specific host.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-09-12

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` (signature_id=4718 OR signature_id=4717) dest=$dest$ 
| rename user as "Account Modified" 
| table _time, dest, "Account Modified", Access_Right, signature
```
#### Associated Analytic Story

* Account Monitoring and Controls


#### How To Implement
To successfully implement this search you must be ingesting your Windows event logs

#### Required field

* _time

* signature_id

* dest

* user




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Get Logon Rights Modifications For User
This search allows you to retrieve any modifications to logon rights for a specific user account.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2019-02-27

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` (signature_id=4718 OR signature_id=4717) user=$user$ 
| rename user as "Account Modified" 
| table _time, dest, "Account Modified", Access_Right, signature
```
#### Associated Analytic Story

* Account Monitoring and Controls


#### How To Implement
To successfully implement this search you must be ingesting your Windows event logs

#### Required field

* _time

* signature_id

* dest

* user




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Get Notable History
This search queries the notable index and returns all the Notable Events for the particular destination host, giving the analyst an overview of the incidents that may have occurred with the host under investigation.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-09-20

<details>
  <summary>details</summary>

#### Search
```

| search `notable` 
| search dest=$dest$ 
| table _time, dest, rule_name, owner, priority, severity, status_description
```
#### Associated Analytic Story

* AWS Cross Account Activity

* AWS Cryptomining

* AWS Network ACL Activity

* AWS User Monitoring

* Account Monitoring and Controls

* Apache Struts Vulnerability

* Asset Tracking

* Brand Monitoring

* Cloud Cryptomining

* ColdRoot MacOS RAT

* Collection and Staging

* Command and Control

* DHS Report TA18-074A

* DNS Amplification Attacks

* Data Protection

* Disabling Security Tools

* Dynamic DNS

* Emotet Malware  DHS Report TA18-201A 

* Hidden Cobra Malware

* Host Redirection

* JBoss Vulnerability

* Kubernetes Scanning Activity

* Lateral Movement

* Malicious PowerShell

* Monitor Backup Solution

* Monitor for Unauthorized Software

* Monitor for Updates

* Netsh Abuse

* Orangeworm Attack Group

* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

* Prohibited Traffic Allowed or Protocol Mismatch

* Ransomware

* Router and Infrastructure Security

* SQL Injection

* SamSam Ransomware

* Spectre And Meltdown Vulnerabilities

* Splunk Enterprise Vulnerability

* Splunk Enterprise Vulnerability CVE-2018-11409

* Suspicious AWS EC2 Activities

* Suspicious AWS S3 Activities

* Suspicious AWS Traffic

* Suspicious Cloud Authentication Activities

* Suspicious Command-Line Executions

* Suspicious DNS Traffic

* Suspicious Emails

* Suspicious MSHTA Activity

* Suspicious WMI Use

* Suspicious Windows Registry Activities

* Unusual AWS EC2 Modifications

* Unusual Processes

* Use of Cleartext Protocols

* Web Fraud Detection

* Windows Defense Evasion Tactics

* Windows File Extension and Association Abuse

* Windows Log Manipulation

* Windows Persistence Techniques

* Windows Privilege Escalation

* Windows Service Abuse

* Data Exfiltration

* F5 TMUI RCE CVE-2020-5902

* Detect Zerologon Attack

* GCP Cross Account Activity

* Kubernetes Sensitive Object Access Activity

* Kubernetes Sensitive Role Activity

* Ransomware Cloud

* Ryuk Ransomware

* Suspicious Cloud Provisioning Activities

* Suspicious GCP Storage Activities

* Windows DNS SIGRed CVE-2020-1350


#### How To Implement
If you are using Enterprise Security you are likely already creating notable events with your correlation rules. No additional configuration is necessary.

#### Required field

* _time




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Get Outbound Emails to Hidden Cobra Threat Actors
This search returns the information of the users that sent emails to the accounts controlled by the Hidden Cobra Threat Actors: specifically to `misswang8107@gmail.com`, and from `redhat@gmail.com`.

- **Product**: Splunk Phantom
- **Datamodel**: Email
- **ATT&CK**: 
- **Last Updated**: 2018-06-14

<details>
  <summary>details</summary>

#### Search
```

| from datamodel Email.All_Email 
| search recipient=misswang8107@gmail.com OR src_user=redhat@gmail.com 
| stats count earliest(_time) as firstTime, latest(_time) as lastTime values(dest) values(src) by src_user recipient 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
```
#### Associated Analytic Story

* Hidden Cobra Malware


#### How To Implement
To successfully implement this search you must ingest your email logs or capture unencrypted email communications within network traffic, and populate the Email data model.

#### Required field

* _time

* recipient

* src_user

* dest

* sec




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get Parent Process Info
This search queries the Endpoint data model to give you details about the parent process of a process running on a host which is under investigation. Enter the values of the process name in question and the dest

- **Product**: Splunk Phantom
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2019-02-28

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.parent_process_name Processes.process_name Processes.dest 
| `drop_dm_object_name("Processes")` 
| search  parent_process_name= $parent_process_name$ 
|search dest = $dest$ 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
```
#### Associated Analytic Story

* Collection and Staging

* Command and Control

* DHS Report TA18-074A

* Disabling Security Tools

* Emotet Malware  DHS Report TA18-201A 

* Hidden Cobra Malware

* Lateral Movement

* Malicious PowerShell

* Monitor for Unauthorized Software

* Netsh Abuse

* Orangeworm Attack Group

* Phishing Payloads

* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

* Prohibited Traffic Allowed or Protocol Mismatch

* Ransomware

* SamSam Ransomware

* Suspicious Command-Line Executions

* Suspicious DNS Traffic

* Suspicious MSHTA Activity

* Suspicious WMI Use

* Suspicious Windows Registry Activities

* Unusual Processes

* Windows Defense Evasion Tactics

* Windows File Extension and Association Abuse

* Windows Log Manipulation

* Windows Persistence Techniques

* Windows Privilege Escalation

* Windows Service Abuse


#### 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.

#### Required field

* _time

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.dest




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Get Process File Activity
This search returns the file activity for a specific process on a specific endpoint

- **Product**: Splunk Phantom
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2019-11-06

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Filesystem.file_name) as file_name values(Filesystem.dest) as dest, values(Filesystem.process_name) as process_name from datamodel=Endpoint.Filesystem by Filesystem.dest Filesystem.process_name Filesystem.file_path, Filesystem.action, _time 
| `drop_dm_object_name(Filesystem)`  
| search dest=$dest$  
| search process_name=$process_name$ 
| table _time, process_name, dest, action, file_name, file_path
```
#### Associated Analytic Story

* DHS Report TA18-074A

* Suspicious Zoom Child Processes


#### How To Implement
To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model.

#### Required field

* _time

* Filesystem.file_name

* Filesystem.dest

* Filesystem.process_name

* Filesystem.file_path

* Filesystem.action




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Get Process Info
This search queries the Endpoint data model to give you details about the process running on a host which is under investigation. To gather the process info, enter the values for the process name in question and the destination IP address.

- **Product**: Splunk Phantom
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2019-04-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.parent_process_name Processes.process_name Processes.dest 
| `drop_dm_object_name("Processes")` 
| search  process_name= $process_name$ 
| search dest = $dest$ 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
```
#### Associated Analytic Story

* AWS Network ACL Activity

* Collection and Staging

* Command and Control

* DHS Report TA18-074A

* Data Protection

* Disabling Security Tools

* Emotet Malware  DHS Report TA18-201A 

* Hidden Cobra Malware

* Lateral Movement

* Malicious PowerShell

* Monitor for Unauthorized Software

* Netsh Abuse

* Orangeworm Attack Group

* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

* Prohibited Traffic Allowed or Protocol Mismatch

* Ransomware

* SamSam Ransomware

* Suspicious AWS Traffic

* Suspicious Command-Line Executions

* Suspicious DNS Traffic

* Suspicious MSHTA Activity

* Suspicious WMI Use

* Suspicious Windows Registry Activities

* Unusual Processes

* Windows Defense Evasion Tactics

* Windows File Extension and Association Abuse

* Windows Log Manipulation

* Windows Persistence Techniques

* Windows Privilege Escalation

* Windows Service Abuse


#### How To Implement
To successfully implement this search you must be ingesting endpoint data and populating the Endpoint data model.

#### Required field

* _time

* Processes.user

* Processes.parent_process_name

* Processes.process_name

* Processes.dest




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Get Process Information For Port Activity
This search will return information about the process associated with observed network traffic to a specific destination port from a specific host.

- **Product**: Splunk Phantom
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2019-04-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.process_name Processes.user Processes.dest Processes.process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search dest=$dest$ 
| join dest type=inner [
| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports by Ports.process_id Ports.src Ports.dest_port 
| `drop_dm_object_name(Ports)` 
| search dest_port=$dest_port$ 
| rename src as dest]
```
#### Associated Analytic Story

* AWS Network ACL Activity

* Command and Control

* DHS Report TA18-074A

* Emotet Malware  DHS Report TA18-201A 

* Hidden Cobra Malware

* Lateral Movement

* Prohibited Traffic Allowed or Protocol Mismatch

* Ransomware

* SamSam Ransomware

* Suspicious AWS Traffic

* Use of Cleartext Protocols


#### How To Implement
To successfully implement this search you must be ingesting endpoint data that associates processes with network events and populate the Endpoint Datamodel

#### Required field

* _time

* Processes.user

* Processes.process_id

* Processes.process_name

* Processes.dest

* Ports.process_id

* Ports.src

* Ports.dest_port




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Get Process Responsible For The DNS Traffic
While investigating, an analyst will want to know what process and parent_process is responsible for generating suspicious DNS traffic. Use the following search and enter the value of `dest` in the search to get specific details on the process responsible for creating the DNS traffic.

- **Product**: Splunk Phantom
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2019-04-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.parent_process Processes.process_name Processes.user Processes.dest Processes.process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search dest = $dest$ 
| join dest type=inner [
| tstats `security_content_summariesonly` count from datamodel=Endpoint.Ports where Ports.dest_port=53 by Ports.process_id Ports.src 
| `drop_dm_object_name(Ports)` 
| rename src as dest]
```
#### Associated Analytic Story

* AWS Network ACL Activity

* Brand Monitoring

* Command and Control

* Data Protection

* Dynamic DNS

* Hidden Cobra Malware

* Suspicious AWS Traffic

* Suspicious DNS Traffic


#### How To Implement
You must be ingesting endpoint data that associates processes with network events into the Endpoint datamodel. This can come from endpoint protection products such as carbon black, or endpoint data sources such as Sysmon.

#### Required field

* _time

* Processes.user

* Processes.process_id

* Processes.process_name

* Processes.dest

* Processes.parent_process

* Ports.process_id

* Ports.src

* Ports.dest_port




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Get Sysmon WMI Activity for Host
This search queries Sysmon WMI events for the host of interest.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-10-23

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode>18 EventCode<22 
| rename host as dest 
| search dest=$dest$
| table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter
```
#### Associated Analytic Story

* Ransomware

* Suspicious WMI Use


#### How To Implement
To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate events for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields.

#### Required field

* _time

* EventCode

* user

* Name

* Operation

* EventType

* Type

* Query

* Consumer

* Filter




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Get Web Session Information via session id
This search helps an analyst investigate a notable event to find out more about a specific web session. The search looks for a specific web session ID in the HTTP web traffic and outputs the URL and user agents, grouped by source IP address and HTTP status code.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-10-08

<details>
  <summary>details</summary>

#### Search
```
`stream_http` session_id  = $session_id$ 
| stats values(url) values(http_user_agent) by src_ip status
```
#### Associated Analytic Story

* Web Fraud Detection


#### How To Implement
This search leverages data extracted from Stream:HTTP. You must configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment server.

#### Required field

* _time

* session_id

* http_user_agent

* src_ip

* status




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Grant Permission Using Cacls Utility
This analytic identifies potential adversaries that modify the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft, ransomware and coinminer scripts to evade detections and restrict access to their component files.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: Endpoint
- **ATT&CK**: [T1222](https://attack.mitre.org/techniques/T1222/)
- **Last Updated**: 2021-06-14

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line IS NOT NULL AND match_regex(cmd_line, /(?i)grant/)=true AND (process_name="cacls.exe" OR process_name="xcacls.exe" OR process_name="icacls.exe") 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name, "process_path", process_path]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed icacls.exe may be used.

#### Required field

* _time

* dest_device_id

* process_name

* parent_process_name

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1222 | File and Directory Permissions Modification | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network administrator may use this windows utility but this is not a common practice.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/ssa_cacls/all_icalc.log


_version_: 1
</details>

---

### Hide User Account From Sign-In Screen
This analytic identifies a suspicious registry modification to hide a user account on the Windows Login screen. This technique was seen in some tradecraft where the adversary will create a hidden user account with Admin privileges in login screen to avoid noticing by the user that they already compromise and to persist on that said machine.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-05-05

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\Windows NT\\CurrentVersion\\Winlogon\\SpecialAccounts\\Userlist*" AND Registry.registry_value_name = "DWORD (0x00000000)" by Registry.dest Registry.user Registry.registry_value_name 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `hide_user_account_from_sign_in_screen_filter`
```
#### Associated Analytic Story

* XMRig


#### 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 product, such as CarbonBlack 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.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.registry_value_name

* Registry.dest Registry.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Unknown. Filter as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/hotkey_disabled_hidden_user/windows-sysmon.log


_version_: 1
</details>

---

### Hiding Files And Directories With Attrib exe
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1222.001](https://attack.mitre.org/techniques/T1222/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=attrib.exe (Processes.process=*+h*) by Processes.parent_process Processes.process_name Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)`
| `hiding_files_and_directories_with_attrib_exe_filter` 
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics

* Windows Persistence Techniques


#### 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.

#### Required field

* _time

* Processes.process

* Processes.process_name

* Processes.parent_process

* Processes.user

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1222.001 | Windows File and Directory Permissions Modification | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Some applications and users may legitimately use attrib.exe to interact with the files. 

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### High File Deletion Frequency
This search looks for high frequency of file deletion relative to process name and process id. These events usually happen when the ransomware tries to encrypt the files with the ransomware file extensions and sysmon treat the original files to be deleted as soon it was replace as encrypted data.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1485](https://attack.mitre.org/techniques/T1485/)
- **Last Updated**: 2021-03-16

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=23 TargetFilename IN ("*\.cmd", "*\.ini","*\.gif", "*\.jpg", "*\.jpeg", "*\.db", "*\.ps1", "*\.doc*", "*\.xls*", "*\.ppt*", "*\.bmp","*\.zip", "*\.rar", "*\.7z", "*\.chm", "*\.png", "*\.log", "*\.vbs", "*\.js") 
| stats values(TargetFilename) as deleted_files min(_time) as firstTime max(_time) as lastTime count by Computer user EventCode Image ProcessID 
|where count >=100 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `high_file_deletion_frequency_filter`
```
#### Associated Analytic Story

* Clop Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the deleted target file name, process name and process id  from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* EventCode

* TargetFilename

* Computer

* user

* Image

* ProcessID

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1485 | Data Destruction | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
user may delete bunch of pictures or files in a folder.

#### Reference


* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log


_version_: 1
</details>

---

### High Number of Login Failures from a single source
This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1110.001](https://attack.mitre.org/techniques/T1110/001/)
- **Last Updated**: 2020-12-16

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` Operation=UserLoginFailed  record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory 
| stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app 
| search accounts_locked >= 5
| `high_number_of_login_failures_from_a_single_source_filter`
```
#### Associated Analytic Story

* Office 365 Detections


#### How To Implement


#### Required field

* _time

* Operation

* record_type

* app

* user

* LogonError

* authentication_method

* signature

* UserAgent

* src_ip

* record_type



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1110.001 | Password Guessing | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
unknown

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### High Process Termination Frequency
This analytics are designed to indentify a high frequency of process termination on a machine which is a common behavior of ransomware malware before encrypting files. This technique is designed to avoid an exception error while accessing (docs, images, database and etc..) in the infected machine for encryption.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/)
- **Last Updated**: 2021-03-16

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=5 
|bin _time span=3s 
|stats values(Image) as proc_terminated min(_time) as firstTime max(_time) as lastTime  count by Computer EventCode ProcessID 
| where count >= 15 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `high_process_termination_frequency_filter`
```
#### Associated Analytic Story

* Clop Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the Image (process full path of terminated process) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* EventCode

* Image

* Computer

* _time

* ProcessID



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1486 | Data Encrypted for Impact | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
admin or user tool that can terminate multiple process.

#### Reference


* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log


_version_: 1
</details>

---

### Hosts receiving high volume of network traffic from email server
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114/002/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` sum(All_Traffic.bytes_in) as bytes_in from datamodel=Network_Traffic where All_Traffic.dest_category=email_server by All_Traffic.src_ip _time span=1d 
| `drop_dm_object_name("All_Traffic")` 
| eventstats avg(bytes_in) as avg_bytes_in stdev(bytes_in) as stdev_bytes_in 
| eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_avg_bytes_in stdev(eval(if(_time < relative_time(now(), "@d"), bytes_in, null))) as per_source_stdev_bytes_in by src_ip 
| eval minimum_data_samples = 4, deviation_threshold = 3 
| where num_data_samples >= minimum_data_samples AND bytes_in > (avg_bytes_in + (deviation_threshold * stdev_bytes_in)) AND bytes_in > (per_source_avg_bytes_in + (deviation_threshold * per_source_stdev_bytes_in)) AND _time >= relative_time(now(), "@d") 
| eval num_standard_deviations_away_from_server_average = round(abs(bytes_in - avg_bytes_in) / stdev_bytes_in, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_in - per_source_avg_bytes_in) / per_source_stdev_bytes_in, 2) 
| table src_ip, _time, bytes_in, avg_bytes_in, per_source_avg_bytes_in, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average 
| `hosts_receiving_high_volume_of_network_traffic_from_email_server_filter`
```
#### Associated Analytic Story

* Collection and Staging


#### 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.

#### Required field

* _time

* All_Traffic.bytes_in

* All_Traffic.dest_category

* All_Traffic.src_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1114.002 | Remote Email Collection | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### ICACLS Grant Command
This analytic identifies potential adversaries that modify the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft and coinminer scripts to evade detections and restrict access to their component files.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1222](https://attack.mitre.org/techniques/T1222/)
- **Last Updated**: 2021-05-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "icacls.exe" OR Processes.process_name = "cacls.exe" OR Processes.process_name = "xcacls.exe" AND Processes.process = "*/grant*" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `icacls_grant_command_filter`
```
#### Associated Analytic Story

* XMRig

* Ransomware


#### 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. Tune and filter known instances where renamed icacls.exe may be used.

#### Required field

* _time

* Processes.parent_process_name

* Processes.process_name

* Processes.dest

* Processes.user

* Processes.process_id

* Processes.process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1222 | File and Directory Permissions Modification | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Unknown. Filter as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Icacls Deny Command
This analytic identifies a potential adversary that changes the security permission of a specific file or directory. This technique is commonly seen in APT tradecraft or coinminer scripts. This behavior is meant to evade detection and prevent access to their component files.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1222](https://attack.mitre.org/techniques/T1222/)
- **Last Updated**: 2021-04-29

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "icacls.exe" OR Processes.process_name = "cacls.exe" OR Processes.process_name = "xcacls.exe" AND Processes.process = "*/deny*" by Processes.parent_process_name Processes.process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `icacls_deny_command_filter`
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed icacls.exe may be used.

#### Required field

* _time

* Processes.parent_process_name

* Processes.process_name

* Processes.dest

* Processes.user

* Processes.process_id

* Processes.process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1222 | File and Directory Permissions Modification | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Unknown. It is possible some administrative scripts use ICacls. Filter as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### IcedID Exfiltrated Archived File Creation
This search is to detect a suspicious file creation namely passff.tar and cookie.tar. This files are possible archived of stolen browser information like history and cookies in a compromised machine with IcedID.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1560.001](https://attack.mitre.org/techniques/T1560/001/)
- **Last Updated**: 2021-07-30

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode= 11  (TargetFilename = "*\\passff.tar" OR TargetFilename = "*\\cookie.tar") 
|stats count min(_time) as firstTime max(_time) as lastTime by TargetFilename EventCode process_id  process_name Computer 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `icedid_exfiltrated_archived_file_creation_filter`
```
#### Associated Analytic Story

* Icedid


#### 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.

#### Required field

* _time

* TargetFilename

* EventCode

* process_id

* process_name

* Computer



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1560.001 | Archive via Utility | Collection |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.cisecurity.org/white-papers/security-primer-icedid/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Identify Systems Creating Remote Desktop Traffic
This search counts the numbers of times the system has generated remote desktop traffic.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: 
- **Last Updated**: 2017-09-15

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src 
| `drop_dm_object_name("All_Traffic")` 
| sort - count
```
#### Associated Analytic Story


#### How To Implement
To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model.

#### Required field

* _time

* All_Traffic.dest_port

* All_Traffic.src




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Identify Systems Receiving Remote Desktop Traffic
This search counts the numbers of times the system has created remote desktop traffic

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: 
- **Last Updated**: 2017-09-15

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest 
| `drop_dm_object_name("All_Traffic")` 
| sort - count
```
#### Associated Analytic Story


#### How To Implement
To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it.

#### Required field

* _time

* All_Traffic.dest_port

* All_Traffic.dest




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Identify Systems Using Remote Desktop
This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2019-04-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process_name="*mstsc.exe*" by Processes.dest Processes.process_name 
| `drop_dm_object_name(Processes)` 
| sort - count
```
#### Associated Analytic Story


#### How To Implement
To successfully implement this search you must be ingesting endpoint data that records process activity.

#### Required field

* _time

* Processes.process_name

* Processes.dest




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Illegal Access To User Content via PowerSploit modules
This detection identifies access to PowerSploit modules that enable illegaly access user content, such as key logging, audio recording, screenshots, tapping into http and RDP sessions, etc.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1021](https://attack.mitre.org/techniques/T1021/), [T1113](https://attack.mitre.org/techniques/T1113/), [T1123](https://attack.mitre.org/techniques/T1123/), [T1563](https://attack.mitre.org/techniques/T1563/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-HttpStatus/)=true OR match_regex(cmd_line, /(?i)Get-Keystrokes/)=true OR match_regex(cmd_line, /(?i)Get-MicrophoneAudio/)=true OR match_regex(cmd_line, /(?i)Get-NetRDPSession/)=true OR match_regex(cmd_line, /(?i)Get-TimedScreenshot/)=true OR match_regex(cmd_line, /(?i)Get-WebConfig/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021 | Remote Services | Lateral Movement |
| T1113 | Screen Capture | Collection |
| T1123 | Audio Capture | Collection |
| T1563 | Remote Service Session Hijacking | Lateral Movement |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1021/illegal_access_to_content/logAllPowerSploitModulesWithOldNames.log


_version_: 1
</details>

---

### Illegal Account Creation via PowerSploit modules
This detection identifies access to PowerSploit modules that create accounts illegaly.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1585](https://attack.mitre.org/techniques/T1585/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)New-DomainUser/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1585 | Establish Accounts | Resource Development |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1585/illegal_account_creation/logAllPowerSploitModulesWithOldNames.log


_version_: 1
</details>

---

### Illegal Deletion of Logs via Mimikatz modules
This detection identifies access to PowerSploit modules that delete event logs.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1070](https://attack.mitre.org/techniques/T1070/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)event::drop/)=true OR match_regex(cmd_line, /(?i)event::clear/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Log Manipulation


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070 | Indicator Removal on Host | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/illegal_log_deletion/logAllMimikatzModules.log


_version_: 1
</details>

---

### Illegal Enabling or Disabling of Accounts via DSInternals modules
This detection identifies use of DSInternals modules that enable or disable accounts illegaly.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Disable-ADDBAccount/)=true OR match_regex(cmd_line, /(?i)Enable-ADDBAccount/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1098 | Account Manipulation | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/MichaelGrafnetter/DSInternals



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllDSInternalsModules.log


_version_: 1
</details>

---

### Illegal Management of Active Directory Elements and Policies via DSInternals modules
This detection identifies use of DSInternals modules for illegal management of Active Directoty elements and policies.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1098](https://attack.mitre.org/techniques/T1098/), [T1207](https://attack.mitre.org/techniques/T1207/), [T1484](https://attack.mitre.org/techniques/T1484/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Remove-ADDBObject/)=true OR match_regex(cmd_line, /(?i)Set-ADDBDomainController/)=true OR match_regex(cmd_line, /(?i)Set-ADDBPrimaryGroup/)=true OR match_regex(cmd_line, /(?i)Set-LsaPolicyInformation/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1098 | Account Manipulation | Persistence |
| T1207 | Rogue Domain Controller | Defense Evasion |
| T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/MichaelGrafnetter/DSInternals



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/logAllDSInternalsModules.log


_version_: 1
</details>

---

### Illegal Management of Computers and Active Directory Elements via PowerSploit modules
This detection identifies access to PowerSploit modules that enable illegal management of computers and Active Directory elements.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1098](https://attack.mitre.org/techniques/T1098/), [T1207](https://attack.mitre.org/techniques/T1207/), [T1484](https://attack.mitre.org/techniques/T1484/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainObject/)=true OR match_regex(cmd_line, /(?i)Set-ADObject/)=true OR match_regex(cmd_line, /(?i)Set-DomainObjectOwner/)=true OR match_regex(cmd_line, /(?i)Set-MasterBootRecord/)=true )


| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1098 | Account Manipulation | Persistence |
| T1207 | Rogue Domain Controller | Defense Evasion |
| T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1484/logAllPowerSploitModulesWithOldNames.log


_version_: 1
</details>

---

### Illegal Privilege Elevation and Persistence via PowerSploit modules
This detection identifies access to PowerSploit modules that illegaly elevate general privileges or ensure persistence, e.g., enable manipulation of registry, task scheduling, persistent WMI, access to OS objects under desired identities.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1053](https://attack.mitre.org/techniques/T1053/), [T1134](https://attack.mitre.org/techniques/T1134/), [T1548](https://attack.mitre.org/techniques/T1548/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Add-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Enable-Privilege/)=true OR match_regex(cmd_line, /(?i)New-ElevatedPersistenceOption/)=true OR match_regex(cmd_line, /(?i)New-UserPersistenceOption/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Malicious PowerShell

* Windows Persistence Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation |
| T1134 | Access Token Manipulation | Defense Evasion, Privilege Escalation |
| T1548 | Abuse Elevation Control Mechanism | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/logAllPowerSploitModulesWithOldNames.log


_version_: 1
</details>

---

### Illegal Privilege Elevation via Mimikatz modules
This detection identifies use of Mimikatz modules for illegal privilege elevation.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1134](https://attack.mitre.org/techniques/T1134/), [T1548](https://attack.mitre.org/techniques/T1548/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)privilege::debug/)=true OR match_regex(cmd_line, /(?i)token::elevate/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Privilege Escalation


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1134 | Access Token Manipulation | Defense Evasion, Privilege Escalation |
| T1548 | Abuse Elevation Control Mechanism | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/logAllMimikatzModules.log


_version_: 1
</details>

---

### Illegal Service and Process Control via Mimikatz modules
This detection identifies use of Mimikatz modules for illegal control over services and processes, including the authentication service.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1106](https://attack.mitre.org/techniques/T1106/), [T1569](https://attack.mitre.org/techniques/T1569/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::start/)=true OR match_regex(cmd_line, /(?i)service::\+/)=true OR match_regex(cmd_line, /(?i)service::\-/)=true OR match_regex(cmd_line, /(?i)service::start/)=true OR match_regex(cmd_line, /(?i)service::stop/)=true OR match_regex(cmd_line, /(?i)service::suspend/)=true OR match_regex(cmd_line, /(?i)misc::memssp/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Service Abuse


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |
| T1106 | Native API | Execution |
| T1569 | System Services | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllMimikatzModules.log


_version_: 1
</details>

---

### Illegal Service and Process Control via PowerSploit modules
This detection identifies access to PowerSploit modules that enable illegal control of services and processes, such as installing or spoofing of malicious services, injecting malicious code in DLLs and EXEs, invoking shell code and WMI commands, modifying access to service objects, etc.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1106](https://attack.mitre.org/techniques/T1106/), [T1569](https://attack.mitre.org/techniques/T1569/)
- **Last Updated**: 2020-11-09

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Install-SSP/)=true OR match_regex(cmd_line, /(?i)Set-CriticalProcess/)=true OR match_regex(cmd_line, /(?i)Install-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Restore-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Write-ServiceBinary/)=true OR match_regex(cmd_line, /(?i)Set-ServiceBinaryPath/)=true OR match_regex(cmd_line, /(?i)Invoke-ReflectivePEInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-DllInjection/)=true OR match_regex(cmd_line, /(?i)Invoke-ServiceAbuse/)=true OR match_regex(cmd_line, /(?i)Invoke-Shellcode/)=true OR match_regex(cmd_line, /(?i)Invoke-WScriptUACBypass/)=true OR match_regex(cmd_line, /(?i)Invoke-WmiCommand/)=true OR match_regex(cmd_line, /(?i)Write-HijackDll/)=true OR match_regex(cmd_line, /(?i)Add-ServiceDacl/)=true )


| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Service Abuse

* Malicious PowerShell


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |
| T1106 | Native API | Execution |
| T1569 | System Services | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003/credential_extraction/logAllPowerSploitModulesWithOldNames.log


_version_: 1
</details>

---

### Investigate AWS User Activities by user field
This search lists all the logged CloudTrail activities by a specific user and will create a table containing the source of the user, the region of the activity, the name and type of the event, the action taken, and the user's identity information.

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-03-12

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` user=$user$ 
| table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType 
```
#### Associated Analytic Story

* AWS User Monitoring

* Suspicious Cloud Authentication Activities


#### 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.

#### Required field

* _time

* user

* userIdentity.type

* userIdentity.userName

* userIdentity.arn

* aws_account_id

* src

* awsRegion

* eventName

* eventType




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate AWS activities via region name
This search lists all the user activities logged by CloudTrail for a specific region in question and will create a table of the values of parameters requested, the type of the event and the response from the AWS API by each user

- **Product**: Splunk Phantom, Splunk Security Analytics for AWS
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-02-09

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` vendor_region=$vendor_region$
| rename requestParameters.instancesSet.items{}.instanceId as instanceId 
| stats values(eventName) by user instanceId vendor_region
```
#### Associated Analytic Story

* AWS Cryptomining

* Cloud Cryptomining

* Suspicious AWS EC2 Activities

* Suspicious AWS S3 Activities


#### 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.

#### Required field

* _time

* vendor_region

* requestParameters.instancesSet.items{}.instanceId

* eventName

* user




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate Failed Logins for Multiple Destinations
This search returns failed logins to multiple destinations by user.

- **Product**: Splunk Phantom
- **Datamodel**: Authentication
- **ATT&CK**: 
- **Last Updated**: 2019-12-10

<details>
  <summary>details</summary>

#### Search
```

| tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login dc(Authentication.dest) AS distinct_count_dest values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app  from datamodel=Authentication where Authentication.action=failure by Authentication.user 
| where distinct_count_dest > 1 
| `security_content_ctime(first_login)` 
| `security_content_ctime(last_login)` 
| `drop_dm_object_name("Authentication")` 
| search user=$user$
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model.

#### Required field

* _time

* Authentication.dest

* Authentication.app

* Authentication.action

* Authentication.user




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate Network Traffic From src ip
This search allows you to find all the network traffic from a specific IP address.

- **Product**: Splunk Phantom
- **Datamodel**: Network_Traffic
- **ATT&CK**: 
- **Last Updated**: 2018-06-15

<details>
  <summary>details</summary>

#### Search
```

| from datamodel Network_Traffic.All_Traffic 
| search src_ip=$src_ip$
```
#### Associated Analytic Story

* ColdRoot MacOS RAT

* Splunk Enterprise Vulnerability CVE-2018-11409


#### How To Implement
To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model.

#### Required field

* _time

* src_ip




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate Okta Activity by IP Address
This search returns all okta events from a specific IP address.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-04-02

<details>
  <summary>details</summary>

#### Search
```
`okta` src_ip={src_ip} 
| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city 
| table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason
```
#### Associated Analytic Story

* Suspicious Okta Activity


#### How To Implement
You must be ingesting Okta logs

#### Required field

* _time

* app

* client.geographicalContext.country

* client.geographicalContext.state

* client.geographicalContext.city

* user

* displayMessage

* src_ip

* result

* outcome.reason




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate Okta Activity by app
This search returns all okta events associated with a specific app

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-04-02

<details>
  <summary>details</summary>

#### Search
```
`okta` app=$app$ 
| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city 
| table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason
```
#### Associated Analytic Story

* Suspicious Okta Activity


#### How To Implement
You must be ingesting Okta logs

#### Required field

* _time

* app

* client.geographicalContext.country

* client.geographicalContext.state

* client.geographicalContext.city

* user

* displayMessage

* src_ip

* result

* outcome.reason




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate Pass the Hash Attempts
This search hunts for dumped NTLM hashes used for pass the hash.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2019-12-10

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4624 Logon_Type=9 AuthenticationPackageName=Negotiate 
| stats count earliest(_time) as first_login latest(_time) as last_login by src_user dest 
| `security_content_ctime(first_login)` 
| `security_content_ctime(last_login)` 
| search dest=$dest$
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
To successfully implement this search you need be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.

#### Required field

* _time

* EventCode

* Logon_Type

* AuthenticationPackageName

* src_user

* dest




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate Pass the Ticket Attempts
This search hunts for dumped kerberos ticket from LSASS memory.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2019-12-10

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4768 OR EventCode=4769 
| rex field=user "(?<new_user>[^\@]+)" 
| stats count BY new_user, dest, EventCode 
| stats max(count) AS max_count sum(count) AS sum_count BY new_user, dest
| search dest=$dest$ 
| where sum_count/max_count!=2 
| rename new_user AS user 
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
To successfully implement this search you need to be ingesting windows security logs. This search uses an input macro named `wineventlog_security`. We strongly recommend that you specify your environment-specific configurations (index, source, sourcetype, etc.) for Windows Security logs. Replace the macro definition with configurations for your Splunk environment. The search also uses a post-filter macro designed to filter out known false positives.

#### Required field

* _time

* EventCode

* user

* dest




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate Previous Unseen User
This search returns previous unseen user, which didn't log in for 30 days.

- **Product**: Splunk Phantom
- **Datamodel**: Authentication
- **ATT&CK**: 
- **Last Updated**: 2019-12-10

<details>
  <summary>details</summary>

#### Search
```

| tstats count `security_content_summariesonly` earliest(_time) as first_login latest(_time) as last_login values(Authentication.dest) AS Authentication.dest values(Authentication.app) AS Authentication.app values(Authentication.action) AS Authentication.action from datamodel=Authentication where Authentication.action=success by _time, Authentication.user 
| bucket _time span=30d 
| stats count min(first_login) as first_login max(last_login) as last_login values(Authentication.dest) AS Authentication.dest by Authentication.user 
| where count=1 
| where first_login >= relative_time(now(), "-30d") 
| `security_content_ctime(first_login)` 
| `security_content_ctime(last_login)` 
| `drop_dm_object_name("Authentication")` 
| search dest=$dest$
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model.

#### Required field

* _time

* Authentication.dest

* Authentication.app

* Authentication.action

* Authentication.user




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate Successful Remote Desktop Authentications
This search returns the source, destination, and user for all successful remote-desktop authentications. A successful authentication after a brute-force attack on a destination machine is suspicious behavior. 

- **Product**: Splunk Phantom
- **Datamodel**: Authentication
- **ATT&CK**: 
- **Last Updated**: 2018-12-14

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature_id=4624 Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app Authentication.user Authentication.signature Authentication.src_nt_domain 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name("Authentication")` 
| search dest=$dest$ 
| table firstTime lastTime src src_nt_domain dest user app count 
| sort count
```
#### Associated Analytic Story

* Hidden Cobra Malware

* Lateral Movement

* SamSam Ransomware


#### How To Implement
You must be populating the Authentication data model with security events from your Windows event logs.

#### Required field

* _time

* Authentication.signature_id

* Authentication.app

* Authentication.src

* Authentication.dest

* Authentication.user

* Authentication.signature

* Authentication.src_nt_domain




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate Suspicious Strings in HTTP Header
This search helps an analyst investigate a notable event related to a potential Apache Struts exploitation. To investigate, we will want to isolate and analyze the "payload" or the commands that were passed to the vulnerable hosts by creating a few regular expressions to carve out the commands focusing on common keywords from the payload, such as cmd.exe, /bin/bash and whois. The search returns these suspicious strings found in the HTTP logs of the system of interest.

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-10-20

<details>
  <summary>details</summary>

#### Search
```
`stream_http` 
| search src_ip=$src_ip$ 
| search dest_ip=$dest_ip$ 
| eval cs_content_type_length = len(cs_content_type) 
| search cs_content_type_length > 100 
| rex field="cs_content_type" (?<suspicious_strings>cmd.exe) 
| eval suspicious_strings_found=if(match(cs_content_type, "application"), "True", "False")  
| rename suspicious_strings_found AS "Suspicious Content-Type Found" 
| fields "Suspicious Content-Type Found", dest_ip, src_ip, suspicious_strings, cs_content_type, cs_content_type_length, url
```
#### Associated Analytic Story

* Apache Struts Vulnerability


#### 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.

#### Required field

* _time

* src_ip

* dest_ip

* cs_content_type

* url




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate User Activities In Okta
This search returns all okta events by a specific user

- **Product**: Splunk Phantom
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-04-02

<details>
  <summary>details</summary>

#### Search
```
`okta` user=$user$ 
| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city 
| table _time, user, displayMessage, app, src_ip, state, city, result, outcome.reason
```
#### Associated Analytic Story

* Suspicious Okta Activity


#### How To Implement
You must be ingesting Okta logs

#### Required field

* _time

* client.geographicalContext.country

* client.geographicalContext.state

* client.geographicalContext.city

* user

* displayMessage

* src_ip

* result

* outcome.reason




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Investigate Web POSTs From src
This investigative search retrieves POST requests from a specified source IP or hostname. Identifying the POST requests, as well as their associated destination URLs and user agent(s), may help you scope and characterize the suspicious traffic. 

- **Product**: Splunk Phantom
- **Datamodel**: Web
- **ATT&CK**: 
- **Last Updated**: 2018-12-06

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method 
| `drop_dm_object_name("Web")`
| search http_method, "POST" 
| search src=$src$
```
#### Associated Analytic Story

* Apache Struts Vulnerability


#### How To Implement
To successfully implement this search, you must be ingesting your web-traffic logs and populating the web data model.

#### Required field

* _time

* Web.url

* Web.src

* Web.http_user_agent

* Web.http_method




#### Kill Chain Phase


#### Known False Positives


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Kerberoasting spn request with RC4 encryption
This search detects a potential kerberoasting attack via service principal name requests

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1558.003](https://attack.mitre.org/techniques/T1558/003/)
- **Last Updated**: 2020-10-16

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4769 Ticket_Options=0x40810000 Ticket_Encryption_Type=0x17 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, service, service_id, Ticket_Encryption_Type, Ticket_Options 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `kerberoasting_spn_request_with_rc4_encryption_filter`
```
#### Associated Analytic Story

* Lateral Movement


#### How To Implement
You must be ingesting endpoint data that tracks process activity, and include the windows security event logs that contain kerberos

#### Required field

* _time

* EventCode

* Ticket_Options

* Ticket_Encryption_Type

* dest

* service

* service_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1558.003 | Kerberoasting | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Older systems that support kerberos RC4 by default NetApp may generate false positives

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1208/T1208.md

* https://www.trimarcsecurity.com/post/trimarcresearch-detecting-kerberoasting-activity



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1558.003/atomic_red_team/windows-security.log


_version_: 3
</details>

---

### Known Services Killed by Ransomware
This search detects a suspicioous termination of known services killed by ransomware before encrypting files in a compromised machine. This technique is commonly seen in most of ransomware now a days to avoid exception error while accessing the targetted files it wants to encrypts because of the open handle of those services to the targetted file.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/)
- **Last Updated**: 2021-06-04

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_system` EventCode=7036 Message IN ("*Volume Shadow Copy*","*VSS*", "*backup*", "*sophos*", "*sql*", "*memtas*", "*mepocs*", "*veeam*", "*svc$*") Message="*service entered the stopped state*" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message dest Type 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `known_services_killed_by_ransomware_filter`
```
#### Associated Analytic Story

* Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the 7036 EventCode ScManager in System audit Logs from your endpoints.

#### Required field

* _time

* EventCode

* Message

* dest

* Type



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1490 | Inhibit System Recovery | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Admin activities or installing related updates may do a sudden stop to list of services we monitor.

#### Reference


* https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/

* https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf3/windows-system.log


_version_: 1
</details>

---

### Kubernetes AWS detect suspicious kubectl calls
This search provides information on anonymous Kubectl calls with IP, verb namespace and object access context

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-06-23

<details>
  <summary>details</summary>

#### Search
```
`aws_cloudwatchlogs_eks` userAgent=kubectl* sourceIPs{}!=127.0.0.1 sourceIPs{}!=::1 src_user=system:anonymous  
| table  src_ip src_user verb userAgent requestURI  
| stats  count by src_ip src_user verb userAgent requestURI 
|`kubernetes_aws_detect_suspicious_kubectl_calls_filter`
```
#### Associated Analytic Story

* Kubernetes Sensitive Object Access Activity


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs.

#### Required field

* _time

* userAgent

* sourceIPs{}

* src_user

* src_ip

* verb

* requestURI




#### Kill Chain Phase

* Lateral Movement


#### Known False Positives
Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Large Volume of DNS ANY Queries
The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Resolution
- **ATT&CK**: [T1498.002](https://attack.mitre.org/techniques/T1498/002/)
- **Last Updated**: 2017-09-20

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" 
| `drop_dm_object_name("DNS")` 
| where count>200 
| `large_volume_of_dns_any_queries_filter`
```
#### Associated Analytic Story

* DNS Amplification Attacks


#### How To Implement
To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model.

#### Required field

* _time

* DNS.message_type

* DNS.record_type

* DNS.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1498.002 | Reflection Amplification | Impact |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### MacOS - Re-opened Applications
This search looks for processes referencing the plist files that determine which applications are re-opened when a user reboots their machine.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2020-02-07

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*com.apple.loginwindow*" by Processes.user Processes.process_name Processes.parent_process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `macos___re_opened_applications_filter`
```
#### Associated Analytic Story


#### 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.

#### Required field

* _time

* Processes.process

* Processes.parent_process

* Processes.user

* Processes.process_name

* Processes.parent_process_name

* Processes.dest




#### Kill Chain Phase

* Installation

* Command and Control


#### 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 added to an allow list.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Mailsniper Invoke functions
This search is to detect known mailsniper.ps1 functions executed in a machine. This technique was seen in some attacker to harvest some sensitive e-mail in a compromised exchange server.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1114.001](https://attack.mitre.org/techniques/T1114/001/)
- **Last Updated**: 2021-05-19

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message IN ("*Invoke-GlobalO365MailSearch*", "*Invoke-GlobalMailSearch*", "*Invoke-SelfSearch*", "*Invoke-PasswordSprayOWA*", "*Invoke-PasswordSprayEWS*","*Invoke-DomainHarvestOWA*", "*Invoke-UsernameHarvestOWA*","*Invoke-OpenInboxFinder*","*Invoke-InjectGEventAPI*","*Invoke-InjectGEvent*","*Invoke-SearchGmail*", "*Invoke-MonitorCredSniper*", "*Invoke-AddGmailRule*","*Invoke-PasswordSprayEAS*","*Invoke-UsernameHarvestEAS*") 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `mailsniper_invoke_functions_filter`
```
#### Associated Analytic Story

* Data Exfiltration


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.

#### Required field

* _time

* EventCode

* Message

* ComputerName

* User



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1114.001 | Local Email Collection | Collection |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.blackhillsinfosec.com/introducing-mailsniper-a-tool-for-searching-every-users-email-for-sensitive-data/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-powershell.log


_version_: 1
</details>

---

### Malicious PowerShell Process - Connect To Internet With Hidden Window
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. Deprecated becaue hidden is not needed when download file with System.Net.WebClient.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2020-11-20

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*-WindowStyle* Processes.process=*hidden* Processes.process="*New-Object*" by Processes.user Processes.process_name Processes.parent_process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `malicious_powershell_process___connect_to_internet_with_hidden_window_filter`
```
#### Associated Analytic Story

* Malicious PowerShell

* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

* HAFNIUM Group


#### 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.

#### Required field

* _time

* Processes.process

* Processes.process_name

* Processes.user

* Processes.parent_process_name

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Command and Control

* Actions on Objectives


#### Known False Positives
Legitimate process can have this combination of command-line options, but it's not common.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/hidden_powershell/windows-sysmon.log


_version_: 5
</details>

---

### Malicious PowerShell Process - Encoded Command
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1027](https://attack.mitre.org/techniques/T1027/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = powershell.exe (Processes.process=*-EncodedCommand* OR Processes.process=*-enc*) by Processes.user Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `malicious_powershell_process___encoded_command_filter`
```
#### Associated Analytic Story

* Malicious PowerShell

* NOBELIUM Group


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.user

* Processes.parent_process_name

* Processes.dest

* Processes.process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1027 | Obfuscated Files or Information | Defense Evasion |


#### Kill Chain Phase

* Command and Control

* Actions on Objectives


#### Known False Positives
System administrators may use this option, but it's not common.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1027/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Malicious PowerShell Process - Execution Policy Bypass
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process_id) as process_id, values(Processes.parent_process_id) as parent_process_id values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe (Processes.process="* -ex*" OR Processes.process="* bypass *") by Processes.process_id, Processes.user, Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `malicious_powershell_process___execution_policy_bypass_filter`
```
#### Associated Analytic Story

* DHS Report TA18-074A

* HAFNIUM Group


#### 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.

#### Required field

* _time

* Processes.process_id

* Processes.parent_process_id

* Processes.process

* Processes.process_name

* Processes.user

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Command and Control

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/encoded_powershell/windows-sysmon.log


_version_: 4
</details>

---

### Malicious PowerShell Process With Obfuscation Techniques
This search looks for PowerShell processes launched with arguments that have characters indicative of obfuscation on the command-line.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2021-01-19

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| eval num_obfuscation = (mvcount(split(process,"`"))-1) + (mvcount(split(process, "^"))-1) + (mvcount(split(process, "'"))-1) 
| `malicious_powershell_process_with_obfuscation_techniques_filter` 
| search num_obfuscation > 10 
```
#### Associated Analytic Story

* Malicious PowerShell


#### 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.

#### Required field

* _time

* Processes.process

* Processes.parent_process

* Processes.process_name

* Processes.user

* Processes.parent_process_name

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Command and Control

* Actions on Objectives


#### Known False Positives
These characters might be legitimately on the command-line, but it is not common.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/obfuscated_powershell/windows-sysmon.log


_version_: 4
</details>

---

### Malicious Powershell Executed As A Service
This detection is to identify the abuse the Windows SC.exe to execute malicious commands or payloads via PowerShell.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1569.002](https://attack.mitre.org/techniques/T1569/002/)
- **Last Updated**: 2021-04-07

<details>
  <summary>details</summary>

#### Search
```
 `wineventlog_system` EventCode=7045 
| eval l_Service_File_Name=lower(Service_File_Name) 
| regex l_Service_File_Name="powershell[.\s]
|powershell_ise[.\s]
|pwsh[.\s]
|psexec[.\s]" 
| regex l_Service_File_Name="-nop[rofile\s]+
|-w[indowstyle]*\s+hid[den]*
|-noe[xit\s]+
|-enc[odedcommand\s]+" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Service_File_Name Service_Name Service_Start_Type Service_Type Service_Account user 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `malicious_powershell_executed_as_a_service_filter`
```
#### Associated Analytic Story

* Malicious Powershell


#### How To Implement
To successfully implement this search, you need to be ingesting Windows System logs with the Service name, Service File Name Service Start type, and Service Type from your endpoints.

#### Required field

* EventCode

* Service_File_Name

* Service_Type

* _time

* Service_Name

* Service_Start_Type

* Service_Account

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1569.002 | Service Execution | Execution |


#### Kill Chain Phase

* Privilege Escalation


#### Known False Positives
Creating a hidden powershell service is rare and could key off of those instances.

#### Reference


* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/dosfuscation-report.pdf

* http://az4n6.blogspot.com/2017/

* https://www.danielbohannon.com/blog-1/2017/3/12/powershell-execution-argument-obfuscation-how-it-can-make-detection-easier



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1569.002/atomic_red_team/windows-system.log


_version_: 1
</details>

---

### Modification Of Wallpaper
This analytic identifies suspicious modification of registry to deface or change the wallpaper of a compromised machines as part of its payload. This technique was commonly seen in ransomware like REVIL where it create a bitmap file contain a note that the machine was compromised and make it as a wallpaper.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1491](https://attack.mitre.org/techniques/T1491/)
- **Last Updated**: 2021-06-02

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode =13  (TargetObject= "*\\Control Panel\\Desktop\\Wallpaper" AND Image != "*\\explorer.exe") OR (TargetObject= "*\\Control Panel\\Desktop\\Wallpaper" AND Details = "*\\temp\\*") 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Image TargetObject Details  Computer process_guid process_id user_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `modification_of_wallpaper_filter`
```
#### Associated Analytic Story

* Ransomware

* Revil Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the Image, TargetObject registry key, registry Details from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* EventCode

* Image

* TargetObject

* Details

* Computer

* process_guid

* process_id

* user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1491 | Defacement | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
3rd party tool may used to changed the wallpaper of the machine

#### Reference


* https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/

* https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log


_version_: 1
</details>

---

### Modify ACL permission To Files Or Folder
This analytic identifies suspicious modification of ACL permission to a files or folder to make it available to everyone. This technique may be used by the adversary to evade ACLs or protected files access. This changes is commonly configured by the file or directory owner with appropriate permission. This behavior is a good indicator if this command seen on a machine utilized by an account with no permission to do so.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1222](https://attack.mitre.org/techniques/T1222/)
- **Last Updated**: 2021-05-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "cacls.exe" OR Processes.process_name = "icacls.exe" OR Processes.process_name = "xcacls.exe" AND (Processes.process = "*/G everyone:*"  OR Processes.process = "*/G SYSTEM:*") by Processes.parent_process_name Processes.process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `modify_acl_permission_to_files_or_folder_filter`
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed cacls.exe may be used.

#### Required field

* _time

* Processes.parent_process_name

* Processes.process_name

* Processes.dest

* Processes.user

* Processes.process

* Processes.process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1222 | File and Directory Permissions Modification | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
administrators may use this command. Filter as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Modify ACLs Permission Of Files Or Folders
This analytic identifies suspicious modification of ACL permission to a files or folder to make it available to everyone or to a specific user. This technique may be used by the adversary to evade ACLs or protected files access. This changes is commonly configured by the file or directory owner with appropriate permission. This behavior is a good indicator if this command seen on a machine utilized by an account with no permission to do so.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: Endpoint
- **ATT&CK**: [T1222](https://attack.mitre.org/techniques/T1222/)
- **Last Updated**: 2021-06-15

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line IS NOT NULL AND like(cmd_line, "%/G%") AND (match_regex(cmd_line, /(?i)everyone:/)=true OR match_regex(cmd_line, /(?i)SYSTEM:/)=true) AND (process_name="cacls.exe" OR process_name="xcacls.exe" OR process_name="icacls.exe") 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name, "process_path", process_path]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed cacls.exe may be used.

#### Required field

* _time

* dest_device_id

* process_name

* parent_process_name

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1222 | File and Directory Permissions Modification | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network administrator may use this windows utility. filter is needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1222.001/ssa_cacls/all_icalc.log


_version_: 1
</details>

---

### Monitor Email For Brand Abuse
This search looks for emails claiming to be sent from a domain similar to one that you want to have monitored for abuse.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Email
- **ATT&CK**: 
- **Last Updated**: 2018-01-05

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(All_Email.recipient) as recipients, min(_time) as firstTime, max(_time) as lastTime from datamodel=Email by All_Email.src_user, All_Email.message_id 
| `drop_dm_object_name("All_Email")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| eval temp=split(src_user, "@") 
| eval email_domain=mvindex(temp, 1) 
| lookup update=true brandMonitoring_lookup domain as email_domain OUTPUT domain_abuse 
| search domain_abuse=true 
| table message_id, src_user, email_domain, recipients, firstTime, lastTime 
| `monitor_email_for_brand_abuse_filter`
```
#### Associated Analytic Story

* Brand Monitoring

* Suspicious Emails


#### 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.

#### Required field

* _time

* All_Email.recipient

* All_Email.src_user

* All_Email.message_id




#### Kill Chain Phase

* Delivery


#### Known False Positives
None at this time

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Monitor Registry Keys for Print Monitors
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1547.010](https://attack.mitre.org/techniques/T1547/010/)
- **Last Updated**: 2020-11-23

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_name Registry.action 
| `drop_dm_object_name(Registry)` 
| `monitor_registry_keys_for_print_monitors_filter`
```
#### Associated Analytic Story

* Suspicious Windows Registry Activities

* Windows Persistence Techniques


#### 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 product, 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.

#### Required field

* _time

* Registry.action

* Registry.registry_path

* Registry.dest

* Registry.registry_key_name

* Registry.user

* Registry.registry_value_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1547.010 | Port Monitors | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
You will encounter noise from legitimate print-monitor registry entries.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.010/atomic_red_team/windows-sysmon.log


_version_: 2
</details>

---

### Monitor Successful Backups
This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-09-12

<details>
  <summary>details</summary>

#### Search
```
`netbackup` "Disk/Partition backup completed successfully." 
| bucket _time span=1d 
| stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE
```
#### Associated Analytic Story

* Monitor Backup Solution


#### How To Implement
To successfully implement this search you must be ingesting your backup logs.

#### Required field

* _time




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Monitor Unsuccessful Backups
This search is intended to give you a feel for how often backup failures happen in your environments.  Fluctuations in these numbers will allow you to determine when you should investigate.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-09-12

<details>
  <summary>details</summary>

#### Search
```
`netbackup` "An error occurred, failed to backup." 
| bucket _time span=1d 
| stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE
```
#### Associated Analytic Story

* Monitor Backup Solution


#### How To Implement
To successfully implement this search you must be ingesting your backup logs.

#### Required field

* _time




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Monitor Web Traffic For Brand Abuse
This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Web
- **ATT&CK**: 
- **Last Updated**: 2017-09-23

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src 
| `drop_dm_object_name("Web")` 
| `security_content_ctime(firstTime)` 
| `brand_abuse_web` 
| `monitor_web_traffic_for_brand_abuse_filter`
```
#### Associated Analytic Story

* Brand Monitoring


#### 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.

#### Required field

* _time

* Web.url

* Web.src




#### Kill Chain Phase

* Delivery


#### Known False Positives
None at this time

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### More than usual number of LOLBAS applications in short time period
Attacker activity may compromise executing several LOLBAS applications in conjunction to accomplish their objectives. We are looking for more than usual LOLBAS applications over a window of time, by building profiles per machine.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/), [T1053](https://attack.mitre.org/techniques/T1053/)
- **Last Updated**: 2020-08-25

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events() 
| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| where process_name=="regsvcs.exe" OR process_name=="ftp.exe" OR process_name=="dfsvc.exe" OR process_name=="rasautou.exe" OR process_name=="schtasks.exe" OR process_name=="xwizard.exe" OR process_name=="findstr.exe" OR process_name=="esentutl.exe" OR process_name=="cscript.exe" OR process_name=="reg.exe" OR process_name=="csc.exe" OR process_name=="atbroker.exe" OR process_name=="print.exe" OR process_name=="pcwrun.exe" OR process_name=="vbc.exe" OR process_name=="rpcping.exe" OR process_name=="wsreset.exe" OR process_name=="ilasm.exe" OR process_name=="certutil.exe" OR process_name=="replace.exe" OR process_name=="mshta.exe" OR process_name=="bitsadmin.exe" OR process_name=="wscript.exe" OR process_name=="ieexec.exe" OR process_name=="cmd.exe" OR process_name=="microsoft.workflow.compiler.exe" OR process_name=="runscripthelper.exe" OR process_name=="makecab.exe" OR process_name=="forfiles.exe" OR process_name=="desktopimgdownldr.exe" OR process_name=="control.exe" OR process_name=="msbuild.exe" OR process_name=="register-cimprovider.exe" OR process_name=="tttracer.exe" OR process_name=="ie4uinit.exe" OR process_name=="sc.exe" OR process_name=="bash.exe" OR process_name=="hh.exe" OR process_name=="cmstp.exe" OR process_name=="mmc.exe" OR process_name=="jsc.exe" OR process_name=="scriptrunner.exe" OR process_name=="odbcconf.exe" OR process_name=="extexport.exe" OR process_name=="msdt.exe" OR process_name=="diskshadow.exe" OR process_name=="extrac32.exe" OR process_name=="eventvwr.exe" OR process_name=="mavinject.exe" OR process_name=="regasm.exe" OR process_name=="gpscript.exe" OR process_name=="rundll32.exe" OR process_name=="regsvr32.exe" OR process_name=="regedit.exe" OR process_name=="msiexec.exe" OR process_name=="gfxdownloadwrapper.exe" OR process_name=="presentationhost.exe" OR process_name=="regini.exe" OR process_name=="wmic.exe" OR process_name=="runonce.exe" OR process_name=="syncappvpublishingserver.exe" OR process_name=="verclsid.exe" OR process_name=="psr.exe" OR process_name=="infdefaultinstall.exe" OR process_name=="explorer.exe" OR process_name=="expand.exe" OR process_name=="installutil.exe" OR process_name=="netsh.exe" OR process_name=="wab.exe" OR process_name=="dnscmd.exe" OR process_name=="at.exe" OR process_name=="pcalua.exe" OR process_name=="cmdkey.exe" OR process_name=="msconfig.exe" 
| stats count(process_name) as lolbas_counter by device,span(timestamp, 300s) 
| eval lolbas_counter=lolbas_counter*1.0 
| rename window_end as timestamp 
| adaptive_threshold algorithm="quantile" value="lolbas_counter" entity="device" window=2419200000L 
| where label AND quantile>0.99 
| eval start_time = window_start, end_time = timestamp, entities = mvappend(device), body=create_map(["lolbas_counter", lolbas_counter]) 
| into write_null();
```
#### Associated Analytic Story

* Unusual Processes


#### How To Implement
Collect endpoint data such as sysmon or 4688 events.

#### Required field

* dest_device_id

* _time

* process_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059 | Command and Scripting Interpreter | Execution |
| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Some administrative tasks may involve multiple use of LOLBAS applications in a short period of time. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline.


#### Reference


* https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries



#### Test Dataset


_version_: 2
</details>

---

### Mshta spawning Rundll32 OR Regsvr32 Process
This search is to detect a suspicious mshta.exe process that spawn rundll32 or regsvr32 child process. This technique was seen in several malware nowadays like trickbot to load its initial .dll stage loader to execute and download the the actual trickbot payload.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218/005/)
- **Last Updated**: 2021-07-19

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "mshta.exe"  (Processes.process_name=rundll32.exe OR Processes.process_name=regsvr32.exe) by Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `mshta_spawning_rundll32_or_regsvr32_process_filter`
```
#### Associated Analytic Story

* Trickbot

* Icedid


#### 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. Tune and filter known instances where renamed mshta.exe may be used.

#### Required field

* _time

* parent_process

* process_name

* process

* process_id

* process_guid



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.005 | Mshta | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
limitted. this anomaly behavior is not commonly seen in clean host.

#### Reference


* https://twitter.com/cyb3rops/status/1416050325870587910?s=21



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/spear_phish/windows-sysmon.log


_version_: 1
</details>

---

### Msmpeng Application DLL Side Loading
This search is to detect a suspicious creation of msmpeng.exe or mpsvc.dll in non default windows defender folder. This technique was seen couple days ago with revil ransomware in Kaseya Supply chain. The approach is to drop an old version of msmpeng.exe to load the actual payload name as mspvc.dll which will load the revil ransomware to the compromise machine

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1574.002](https://attack.mitre.org/techniques/T1574/002/)
- **Last Updated**: 2021-07-05

<details>
  <summary>details</summary>

#### Search
```

|tstats `security_content_summariesonly` values(Filesystem.file_path) as file_path count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name = "msmpeng.exe" OR Filesystem.file_name = "mpsvc.dll")  AND Filesystem.file_path != "*\\Program Files\\windows defender\\*" by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `msmpeng_application_dll_side_loading_filter`
```
#### Associated Analytic Story

* Ransomware

* Revil Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.

#### Required field

* _time

* Filesystem.file_create_time

* Filesystem.process_id

* Filesystem.file_name

* Filesystem.user

* Filesystem.file_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1574.002 | DLL Side-Loading | Persistence, Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
quite minimal false positive expected.

#### Reference


* https://community.sophos.com/b/security-blog/posts/active-ransomware-attack-on-kaseya-customers



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets//malware/revil/msmpeng_side/windows-sysmon.log


_version_: 1
</details>

---

### Multiple Archive Files Http Post Traffic
This search is designed to detect high frequency of archive files data exfiltration through HTTP POST method protocol. This are one of the common techniques used by APT or trojan spy after doing the data collection like screenshot, recording, sensitive data to the infected machines. The attacker may execute archiving command to the collected data, save it a temp folder with a hidden attribute then send it to its C2 through HTTP POST. Sometimes adversaries will rename the archive files or encode/encrypt to cover their tracks. This detection can detect a renamed archive files transfer to HTTP POST since it checks the request body header. Unfortunately this detection cannot support archive that was encrypted or encoded before doing the exfiltration.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048/003/)
- **Last Updated**: 2021-04-21

<details>
  <summary>details</summary>

#### Search
```
`stream_http` http_method=POST 
|eval archive_hdr1=substr(form_data,1,2) 
| eval archive_hdr2 = substr(form_data,1,4) 
|stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by http_method http_user_agent uri_path url bytes_in bytes_out archive_hdr1 archive_hdr2 
|where count >20 AND (archive_hdr1 = "7z" OR archive_hdr1 = "PK" OR archive_hdr2="Rar!") 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `multiple_archive_files_http_post_traffic_filter`
```
#### Associated Analytic Story

* Command and Control

* Data Exfiltration


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled in stream http configuration.

#### Required field

* _time

* http_method

* http_user_agent

* uri_path

* url

* bytes_in

* bytes_out

* archive_hdr1

* archive_hdr2

* form_data



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration |


#### Kill Chain Phase

* Exfiltration


#### Known False Positives
Normal archive transfer via HTTP protocol may trip this detection.

#### Reference


* https://attack.mitre.org/techniques/T1560/001/

* https://www.fireeye.com/blog/threat-research/2019/01/apt39-iranian-cyber-espionage-group-focused-on-personal-information.html

* https://www.microsoft.com/security/blog/2021/01/20/deep-dive-into-the-solorigate-second-stage-activation-from-sunburst-to-teardrop-and-raindrop/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/archive_http_post/stream_http_events.log


_version_: 1
</details>

---

### Multiple Disabled Users Failing To Authenticate From Host Using Kerberos
The following analytic identifies one source endpoint failing to authenticate with multiple disabled domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack against disabled users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code `0x12` stands for `clients credentials have been revoked` (account disabled, expired or locked out).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1110.003](https://attack.mitre.org/techniques/T1110/003/)
- **Last Updated**: 2021-04-14

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4768 Account_Name!="*$" Result_Code=0x12 
| bucket span=2m _time 
| stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_disabled_users_failing_to_authenticate_from_host_using_kerberos_filter` 
```
#### Associated Analytic Story

* Active Directory Password Spraying


#### How To Implement
To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.

#### Required field

* _time

* EventCode

* Result_Code

* Account_Name

* Client_Address



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1110.003 | Password Spraying | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
A host failing to authenticate with multiple disabled domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems missconfigured systems.

#### Reference


* https://attack.mitre.org/techniques/T1110/003/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_disabled_users_kerberos/windows-security.log


_version_: 1
</details>

---

### Multiple Invalid Users Failing To Authenticate From Host Using Kerberos
The following analytic identifies one source endpoint failing to authenticate with multiple invalid domain users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4768 is generated every time the Key Distribution Center issues a Kerberos Ticket Granting Ticket (TGT). Failure code 0x6 stands for `client not found in Kerberos database` (the attempted user is not a valid domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1110.003](https://attack.mitre.org/techniques/T1110/003/)
- **Last Updated**: 2021-04-14

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4768 Result_Code=0x6 Account_Name!="*$" 
| bucket span=2m _time 
| stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_invalid_users_failing_to_authenticate_from_host_using_kerberos_filter` 
```
#### Associated Analytic Story

* Active Directory Password Spraying


#### How To Implement
To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.

#### Required field

* _time

* EventCode

* Result_Code

* Account_Name

* Client_Address



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1110.003 | Password Spraying | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, multi-user systems and missconfigured systems.

#### Reference


* https://attack.mitre.org/techniques/T1110/003/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_kerberos/windows-security.log


_version_: 1
</details>

---

### Multiple Invalid Users Failing To Authenticate From Host Using NTLM
The following analytic identifies one source endpoint failing to authenticate with multiple invalid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. As attackers progress in a breach, mistakes will be made. In certain scenarios, adversaries may execute a password spraying attack using an invalid list of users. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC0000064 stands for `The username you typed does not exist` (the attempted user is a legitimate domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1110.003](https://attack.mitre.org/techniques/T1110/003/)
- **Last Updated**: 2021-04-15

<details>
  <summary>details</summary>

#### Search
```
 `wineventlog_security` EventCode=4776 Logon_Account!="*$" 0xC0000064 action=failure 
| bucket span=2m _time 
| stats dc(Logon_Account) AS unique_accounts values(Logon_Account) as tried_accounts by _time, Source_Workstation 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Workstation 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_invalid_users_failing_to_authenticate_from_host_using_ntlm_filter`
```
#### Associated Analytic Story

* Active Directory Password Spraying


#### How To Implement
To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation' within `Account Logon` needs to be enabled.

#### Required field

* _time

* EventCode

* action

* Logon_Account

* Source_Workstation



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1110.003 | Password Spraying | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
A host failing to authenticate with multiple invalid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.

#### Reference


* https://attack.mitre.org/techniques/T1110/003/

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_invalid_users_ntlm/windows-security.log


_version_: 1
</details>

---

### Multiple Okta Users With Invalid Credentials From The Same IP
This search detects Okta login failures due to bad credentials for multiple users originating from the same ip address.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```
`okta` outcome.reason=INVALID_CREDENTIALS 
| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city 
| stats min(_time) as firstTime max(_time) as lastTime dc(user) as distinct_users values(user) as users by src_ip, displayMessage, outcome.reason, country, state, city  
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
|  search distinct_users > 5
| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` 
```
#### Associated Analytic Story

* Suspicious Okta Activity


#### How To Implement
This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.

#### Required field

* _time

* outcome.reason

* client.geographicalContext.country

* client.geographicalContext.state

* client.geographicalContext.city

* user

* src_ip

* displayMessage



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.001 | Default Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### 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 `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter` to raise the threshold or except specific IP adresses from triggering this search.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Multiple Users Attempting To Authenticate Using Explicit Credentials
The following analytic identifies a source user failing to authenticate with multiple users using explicit credentials on a host. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4648 is generated when a process attempts an account logon by explicitly specifying that accounts credentials. This event generates on domain controllers, member servers, and workstations.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source account, attempted user accounts and the endpoint were the behavior was identified.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1110.003](https://attack.mitre.org/techniques/T1110/003/)
- **Last Updated**: 2021-04-13

<details>
  <summary>details</summary>

#### Search
```
 `wineventlog_security` EventCode=4648 
| bucket span=2m _time 
| eval Source_Account = mvindex(Account_Name, 0) 
| eval Destination_Account = mvindex(Account_Name, 1) 
| search Source_Account != "*$" Source_Account !="-" Destination_Account !="*$" 
| stats dc(Destination_Account) AS unique_accounts values(Destination_Account) as tried_account by _time, ComputerName, Source_Account 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by ComputerName 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_users_attempting_to_authenticate_using_explicit_credentials_filter` 
```
#### Associated Analytic Story

* Active Directory Password Spraying


#### How To Implement
To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.

#### Required field

* _time

* EventCode

* Security_ID

* Account_Name

* ComputerName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1110.003 | Password Spraying | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
A source user failing attempting to authenticate multiple users on a host is not a common behavior for regular systems. Some applications, however, may exhibit this behavior in which case sets of users hosts can be added to an allow list. Possible false positive scenarios include systems where several users connect to like Mail servers, identity providers, remote desktop services, Citrix, etc.

#### Reference


* https://attack.mitre.org/techniques/T1110/003/

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4648

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_explicit_credential_spray/windows-security.log


_version_: 1
</details>

---

### Multiple Users Failing To Authenticate From Host Using Kerberos
The following analytic identifies one source endpoint failing to authenticate with multiple valid users using the Kerberos protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using Kerberos to obtain initial access or elevate privileges. Event 4771 is generated when the Key Distribution Center fails to issue a Kerberos Ticket Granting Ticket (TGT). Failure code 0x18 stands for `wrong password provided` (the attempted user is a legitimate domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source ip and attempted user accounts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1110.003](https://attack.mitre.org/techniques/T1110/003/)
- **Last Updated**: 2021-04-08

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4771 Failure_Code=0x18 Account_Name!="*$" 
| bucket span=2m _time 
| stats dc(Account_Name) AS unique_accounts values(Account_Name) as tried_accounts by _time, Client_Address 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Client_Address 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_users_failing_to_authenticate_from_host_using_kerberos_filter`
```
#### Associated Analytic Story

* Active Directory Password Spraying


#### How To Implement
To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting `Audit Kerberos Authentication Service` within `Account Logon` needs to be enabled.

#### Required field

* _time

* EventCode

* Result_Code

* Account_Name

* Client_Address



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1110.003 | Password Spraying | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, missconfigured systems and multi-user systems like Citrix farms.

#### Reference


* https://attack.mitre.org/techniques/T1110/003/

* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-r2-and-2012/dn319109(v=ws.11)

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4771



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_kerberos/windows-security.log


_version_: 1
</details>

---

### Multiple Users Failing To Authenticate From Host Using NTLM
The following analytic identifies one source endpoint failing to authenticate with multiple valid users using the NTLM protocol. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment using NTLM to obtain initial access or elevate privileges. Event 4776 is generated on the computer that is authoritative for the provided credentials. For domain accounts, the domain controller is authoritative. For local accounts, the local computer is authoritative. Error code 0xC000006A means: misspelled or bad password (the attempted user is a legitimate domain user).\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will only trigger on domain controllers, not on member servers or workstations.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source workstation name and attempted user accounts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1110.003](https://attack.mitre.org/techniques/T1110/003/)
- **Last Updated**: 2021-04-13

<details>
  <summary>details</summary>

#### Search
```
 `wineventlog_security` EventCode=4776 Logon_Account!="*$" 0xC000006A action=failure 
| bucket span=2m _time 
| stats dc(Logon_Account) AS unique_accounts values(Logon_Account) as tried_accounts by _time, Source_Workstation 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Workstation 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_users_failing_to_authenticate_from_host_using_ntlm_filter`
```
#### Associated Analytic Story

* Active Directory Password Spraying


#### How To Implement
To successfully implement this search, you need to be ingesting Domain Controller events. The Advanced Security Audit policy setting `Audit Credential Validation` within `Account Logon` needs to be enabled.

#### Required field

* _time

* EventCode

* action

* Logon_Account

* Source_Workstation



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1110.003 | Password Spraying | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
A host failing to authenticate with multiple valid domain users is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems. If this detection triggers on a host other than a Domain Controller, the behavior could represent a password spraying attack against the host's local accounts.

#### Reference


* https://attack.mitre.org/techniques/T1110/003/

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/audit-credential-validation

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4776



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_valid_users_ntlm/windows-security.log


_version_: 1
</details>

---

### Multiple Users Failing To Authenticate From Process
The following analytic identifies a source process name failing to authenticate with multiple users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4625 generates on domain controllers, member servers, and workstations when an account fails to logon. Logon Type 2 describes an iteractive logon attempt.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will trigger on the potenfially malicious host, perhaps controlled via a trojan or operated by an insider threat, from where a password spraying attack is being executed. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1110.003](https://attack.mitre.org/techniques/T1110/003/)
- **Last Updated**: 2021-04-13

<details>
  <summary>details</summary>

#### Search
```
 `wineventlog_security` EventCode=4625 Logon_Type=2 Caller_Process_Name!="-" 
| bucket span=2m _time 
| eval Source_Account = mvindex(Account_Name, 0) 
| eval Destination_Account = mvindex(Account_Name, 1) 
| stats dc(Destination_Account) AS unique_accounts values(Account_Name) as tried_accounts by _time, Caller_Process_Name, Source_Account, ComputerName 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Caller_Process_Name, Source_Account, ComputerName 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_users_failing_to_authenticate_from_process_filter` 
```
#### Associated Analytic Story

* Active Directory Password Spraying


#### How To Implement
To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers aas well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.

#### Required field

* _time

* EventCode

* Logon_Type

* Caller_Process_Name

* Security_ID

* Account_Name

* ComputerName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1110.003 | Password Spraying | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
A process failing to authenticate with multiple users is not a common behavior for legitimate user sessions. Possible false positive scenarios include but are not limited to vulnerability scanners and missconfigured systems.

#### Reference


* https://attack.mitre.org/techniques/T1110/003/

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625

* https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_multiple_users_from_process/windows-security.log


_version_: 1
</details>

---

### Multiple Users Remotely Failing To Authenticate From Host
The following analytic identifies a source host failing to authenticate against a remote host with multiple users. This behavior could represent an adversary performing a Password Spraying attack against an Active Directory environment to obtain initial access or elevate privileges. Event 4625 documents each and every failed attempt to logon to the local computer. This event generates on domain controllers, member servers, and workstations. Logon Type 3 describes an remote authentication attempt.\
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of users. To customize this analytic, users can try different combinations of the `bucket` span time and the calculation of the `upperBound` field. This logic can be used for real time security monitoring as well as threat hunting exercises.\
This detection will trigger on the host that is the target of the password spraying attack. This could be a domain controller as well as a member server or workstation.\
The analytics returned fields allow analysts to investigate the event further by providing fields like source process name, source account and attempted user accounts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1110.003](https://attack.mitre.org/techniques/T1110/003/)
- **Last Updated**: 2021-04-13

<details>
  <summary>details</summary>

#### Search
```
 `wineventlog_security` EventCode=4625 Logon_Type=3 Source_Network_Address!="-" 
| bucket span=2m _time 
| eval Destination_Account = mvindex(Account_Name, 1) 
| stats dc(Destination_Account) AS unique_accounts values(Destination_Account) as tried_accounts by _time, Source_Network_Address, ComputerName 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by Source_Network_Address, ComputerName 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1, 0) 
| search isOutlier=1 
| `multiple_users_remotely_failing_to_authenticate_from_host_filter` 
```
#### Associated Analytic Story

* Active Directory Password Spraying


#### How To Implement
To successfully implement this search, you need to be ingesting Windows Event Logs from domain controllers as as well as member servers and workstations. The Advanced Security Audit policy setting `Audit Logon` within `Logon/Logoff` needs to be enabled.

#### Required field

* _time

* EventCode

* Logon_Type

* Security_ID

* Account_Name

* ComputerName

* Source_Network_Address



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1110.003 | Password Spraying | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
A host failing to authenticate with multiple valid users against a remote host is not a common behavior for legitimate systems. Possible false positive scenarios include but are not limited to vulnerability scanners, remote administration tools, missconfigyred systems, etc.

#### Reference


* https://attack.mitre.org/techniques/T1110/003/

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625

* https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4625

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/basic-audit-logon-events



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110.003/purplesharp_remote_spray/windows-security.log


_version_: 1
</details>

---

### NET Profiler UAC bypass
This search is to detect modification of registry to bypass UAC windows feature. This technique is to add a payload dll path on .NET COR file path that will be loaded by mmc.exe as soon it was executed. This detection rely on monitoring the registry key and values in the detection area. It may happened that windows update some dll related to mmc.exe and add dll path in this registry. In this case filtering is needed.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548/002/)
- **Last Updated**: 2021-07-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\COR_PROFILER_PATH" Registry.registry_value_name = "*.dll" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `net_profiler_uac_bypass_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_path

* Registry.registry_key_name

* Registry.registry_value_name

* Registry.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548.002 | Bypass User Account Control | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
limited false positive. It may trigger by some windows update that will modify this registry.

#### Reference


* https://offsec.almond.consulting/UAC-bypass-dotnet.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon2.log


_version_: 1
</details>

---

### NLTest Domain Trust Discovery
This search looks for the execution of `nltest.exe` with command-line arguments utilized to query for Domain Trust information. Two arguments `/domain trusts`, returns a list of trusted domains, and `/all_trusts`, returns all trusted domains. Red Teams and adversaries alike use NLTest.exe to enumerate the current domain to assist with further understanding where to pivot next.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1482](https://attack.mitre.org/techniques/T1482/)
- **Last Updated**: 2021-01-25

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=nltest.exe OR Processes.process_name!=nltest.exe) (Processes.process=*/domain_trusts* OR Processes.process=*/all_trusts*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `nltest_domain_trust_discovery_filter`
```
#### Associated Analytic Story

* Ryuk Ransomware

* Domain Trust Discovery

* Icedid


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1482 | Domain Trust Discovery | Discovery |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Administrators may use nltest for troubleshooting purposes, otherwise, rarely used.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1482/T1482.md

* https://malware.news/t/lets-learn-trickbot-implements-network-collector-module-leveraging-cmd-wmi-ldap/19104

* https://attack.mitre.org/techniques/T1482/

* https://www.owasp.org/images/4/4b/Red_Team_Operating_in_a_Modern_Environment.pdf

* https://ss64.com/nt/nltest.html

* https://redcanary.com/threat-detection-report/techniques/domain-trust-discovery/

* https://thedfirreport.com/2020/10/08/ryuks-return/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1482/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### New container uploaded to AWS ECR
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1525](https://attack.mitre.org/techniques/T1525/)
- **Last Updated**: 2020-02-20

<details>
  <summary>details</summary>

#### Search
```

| tstats count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Cloud_Infrastructure.Compute where Compute.user_type!="AssumeRole" AND Compute.http_user_agent="AWS Internal" AND Compute.event_name="PutImage" by Compute.image_id Compute.src_user Compute.src Compute.region Compute.msg Compute.user_type 
| `drop_dm_object_name("Compute")` 
| `new_container_uploaded_to_aws_ecr_filter` 
```
#### Associated Analytic Story

* Container Implantation Monitoring and Investigation


#### 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 AWS 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.

#### Required field

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1525 | Implant Internal Image | Persistence |


#### Kill Chain Phase


#### Known False Positives
Uploading container is a normal behavior from developers or users with access to container registry.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Nishang PowershellTCPOneLine
This query detects the Nishang Invoke-PowerShellTCPOneLine utility that spawns a call back to a remote command and control server. This is a powershell oneliner. In addition, this will capture on the command-line additional utilities used by Nishang. Triage the endpoint and identify any parallel processes that look suspicious. Review the reputation of the remote IP or domain contacted by the powershell process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2021-03-03

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe OR Processes.process_name=pwsh.exe OR Processes.process_name=PowerShell_ISE.exe (Processes.process=*Net.Sockets.TCPClient* AND Processes.process=*System.Text.ASCIIEncoding*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `nishang_powershelltcponeline_filter`
```
#### Associated Analytic Story

* HAFNIUM Group


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives may be present. Filter as needed based on initial analysis.

#### Reference


* https://github.com/samratashok/nishang/blob/master/Shells/Invoke-PowerShellTcpOneLine.ps1

* https://www.volexity.com/blog/2021/03/02/active-exploitation-of-microsoft-exchange-zero-day-vulnerabilities/

* https://www.microsoft.com/security/blog/2021/03/02/hafnium-targeting-exchange-servers/

* https://blog.rapid7.com/2021/03/03/rapid7s-insightidr-enables-detection-and-response-to-microsoft-exchange-0-day/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### No Windows Updates in a time frame
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Updates
- **ATT&CK**: 
- **Last Updated**: 2017-09-15

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` max(_time) as lastTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product 
| rename Updates.dest as Host 
| rename Updates.status as "Update Status" 
| rename Updates.vendor_product as Product 
| eval isOutlier=if(lastTime <= relative_time(now(), "-60d@d"), 1, 0)  
| `security_content_ctime(lastTime)`  
| search isOutlier=1 
| rename lastTime as "Last Update Time", 
| table Host, "Update Status", Product, "Last Update Time" 
| `no_windows_updates_in_a_time_frame_filter`
```
#### Associated Analytic Story

* Monitor for Updates


#### 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.

#### Required field

* _time

* Updates.status

* Updates.vendor_product

* Updates.dest




#### Kill Chain Phase


#### Known False Positives
None identified

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Ntdsutil Export NTDS
Monitor for signs that Ntdsutil is being used to Extract Active Directory database - NTDS.dit, typically used for offline password cracking. It may be used in normal circumstances with no command line arguments or shorthand variations of more common arguments. Ntdsutil.exe is typically seen run on a Windows Server. Typical command used to dump ntds.dit \
ntdsutil "ac i ntds" "ifm" "create full C:\Temp" q q \
This technique uses "Install from Media" (IFM), which will extract a copy of the Active Directory database. A successful export of the Active Directory database will yield a file modification named ntds.dit to the destination.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003/003/)
- **Last Updated**: 2021-01-28

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=ntdsutil.exe Processes.process=*ntds* Processes.process=*create*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `ntdsutil_export_ntds_filter`
```
#### Associated Analytic Story

* Credential Dumping

* HAFNIUM Group


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.003 | NTDS | Credential Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Highly possible Server Administrators will troubleshoot with ntdsutil.exe, generating false positives.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1003.003/T1003.003.md#atomic-test-3---dump-active-directory-database-with-ntdsutil

* https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2012-R2-and-2012/cc753343(v=ws.11)

* https://2017.zeronights.org/wp-content/uploads/materials/ZN17_Kheirkhabarov_Hunting_for_Credentials_Dumping_in_Windows_Environment.pdf

* https://strontic.github.io/xcyclopedia/library/vss_ps.dll-97B15BDAE9777F454C9A6BA25E938DB3.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1003.003/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### O365 Add App Role Assignment Grant User
This search detects the creation of a new Federation setting by alerting about an specific event related to its creation.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136/003/)
- **Last Updated**: 2021-01-26

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` Workload=AzureActiveDirectory Operation="Add app role assignment grant to user." 
| stats count min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(Actor{}.Type) as Actor.Type by ActorIpAddress dest ResultStatus 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `o365_add_app_role_assignment_grant_user_filter`
```
#### Associated Analytic Story

* Office 365 Detections

* Cloud Federated Credential Abuse


#### How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

#### Required field

* _time

* Workload

* Operation

* Actor{}.ID

* Actor{}.Type

* ActorIpAddress

* dest

* ResultStatus



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1136.003 | Cloud Account | Persistence |


#### Kill Chain Phase

* Actions on Objective


#### Known False Positives
The creation of a new Federation is not necessarily malicious, however this events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider.

#### Reference


* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf

* https://us-cert.cisa.gov/ncas/alerts/aa21-008a



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federation/o365_new_federation.json


_version_: 1
</details>

---

### O365 Added Service Principal
This search detects the creation of a new Federation setting by alerting about an specific event related to its creation.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136/003/)
- **Last Updated**: 2021-01-26

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` Workload=AzureActiveDirectory signature="Add service principal credentials." 
| stats min(_time) as firstTime max(_time) as lastTime values(Actor{}.ID) as Actor.ID values(ModifiedProperties{}.Name) as ModifiedProperties.Name values(ModifiedProperties{}.NewValue) as ModifiedProperties.NewValue values(Target{}.ID) as Target.ID by ActorIpAddress signature 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `o365_added_service_principal_filter`
```
#### Associated Analytic Story

* Office 365 Detections

* Cloud Federated Credential Abuse


#### How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

#### Required field

* _time

* Workload

* signature

* Actor{}.ID

* ModifiedProperties{}.Name

* ModifiedProperties{}.NewValue

* Target{}.ID

* ActorIpAddress



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1136.003 | Cloud Account | Persistence |


#### Kill Chain Phase

* Actions on Objective


#### Known False Positives
The creation of a new Federation is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a different cloud provider.

#### Reference


* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf

* https://us-cert.cisa.gov/ncas/alerts/aa21-008a

* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html

* https://www.sygnia.co/golden-saml-advisory



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_add_service_principal/o365_add_service_principal.json


_version_: 1
</details>

---

### O365 Bypass MFA via Trusted IP
This search detects newly added IP addresses/CIDR blocks to the list of MFA Trusted IPs to bypass multi factor authentication. Attackers are often known to use this technique so that they can bypass the MFA system.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1562.007](https://attack.mitre.org/techniques/T1562/007/)
- **Last Updated**: 2021-07-19

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` signature="Set Company Information." ModifiedProperties{}.Name=StrongAuthenticationPolicy 
| rex max_match=100 field=ModifiedProperties{}.NewValue "(?<ip_addresses_new_added>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" 
| rex max_match=100 field=ModifiedProperties{}.OldValue "(?<ip_addresses_old>\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\/\d{1,2})" 
| eval ip_addresses_old=if(isnotnull(ip_addresses_old),ip_addresses_old,"0") 
| mvexpand ip_addresses_new_added 
| where isnull(mvfind(ip_addresses_old,ip_addresses_new_added)) 
|stats count min(_time) as firstTime max(_time) as lastTime values(ip_addresses_old) as ip_addresses_old by user ip_addresses_new_added signature Workload vendor_account status user_id action 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `o365_bypass_mfa_via_trusted_ip_filter`
```
#### Associated Analytic Story

* Office 365 Detections


#### How To Implement
You must install Splunk Microsoft Office 365 add-on. This search works with o365:management:activity

#### Required field

* _time

* signature

* ModifiedProperties{}.Name

* ModifiedProperties{}.NewValue

* ModifiedProperties{}.OldValue

* user

* vendor_account

* status

* user_id

* action



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.007 | Disable or Modify Cloud Firewall | Defense Evasion |


#### Kill Chain Phase

* Actions on Objective


#### Known False Positives
Unless it is a special case, it is uncommon to continually update Trusted IPs to MFA configuration.

#### Reference


* https://i.blackhat.com/USA-20/Thursday/us-20-Bienstock-My-Cloud-Is-APTs-Cloud-Investigating-And-Defending-Office-365.pdf

* https://attack.mitre.org/techniques/T1562/007/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.007/o365_bypass_mfa_via_trusted_ip/o365_bypass_mfa_via_trusted_ip.json


_version_: 2
</details>

---

### O365 Disable MFA
This search detects when multi factor authentication has been disabled, what entitiy performed the action and against what user

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1556](https://attack.mitre.org/techniques/T1556/)
- **Last Updated**: 2020-12-16

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` Operation="Disable Strong Authentication." 
| stats count earliest(_time) as firstTime latest(_time) as lastTime by UserType Operation user status signature dest ResultStatus 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `o365_disable_mfa_filter`
```
#### Associated Analytic Story

* Office 365 Detections


#### How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

#### Required field

* _time

* Operation

* UserType

* user

* status

* signature

* dest

* ResultStatus



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1556 | Modify Authentication Process | Credential Access, Defense Evasion, Persistence |


#### Kill Chain Phase

* Actions on Objective


#### Known False Positives
Unless it is a special case, it is uncommon to disable MFA or Strong Authentication

#### Reference


* https://attack.mitre.org/techniques/T1556/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_disable_mfa/o365_disable_mfa.json


_version_: 1
</details>

---

### O365 Excessive Authentication Failures Alert
This search detects when an excessive number of authentication failures occur this search also includes attempts against MFA prompt codes

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1110](https://attack.mitre.org/techniques/T1110/)
- **Last Updated**: 2020-12-16

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` Workload=AzureActiveDirectory UserAuthenticationMethod=* status=Failed 
| stats count earliest(_time) as firstTime latest(_time) values(UserAuthenticationMethod) AS UserAuthenticationMethod values(UserAgent) AS UserAgent values(status) AS status values(src_ip) AS src_ip by user 
| where count > 10 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `o365_excessive_authentication_failures_alert_filter`
```
#### Associated Analytic Story

* Office 365 Detections


#### How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

#### Required field

* _time

* Workload

* UserAuthenticationMethod

* status

* UserAgent

* src_ip

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1110 | Brute Force | Credential Access |


#### Kill Chain Phase

* Not Applicable


#### Known False Positives
The threshold for alert is above 10 attempts and this should reduce the number of false positives.

#### Reference


* https://attack.mitre.org/techniques/T1110/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1110/o365_brute_force_login/o365_brute_force_login.json


_version_: 1
</details>

---

### O365 Excessive SSO logon errors
This search detects accounts with high number of Single Sign ON (SSO) logon errors. Excessive logon errors may indicate attempts to bruteforce of password or single sign on token hijack or reuse.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1556](https://attack.mitre.org/techniques/T1556/)
- **Last Updated**: 2021-01-26

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity`  Workload=AzureActiveDirectory LogonError=SsoArtifactInvalidOrExpired 
| stats count min(_time) as firstTime max(_time) as lastTime by LogonError ActorIpAddress UserAgent UserId 
| where count > 5 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `o365_excessive_sso_logon_errors_filter`
```
#### Associated Analytic Story

* Office 365 Detections

* Cloud Federated Credential Abuse


#### How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

#### Required field

* _time

* Workload

* LogonError

* ActorIpAddress

* UserAgent

* UserId



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1556 | Modify Authentication Process | Credential Access, Defense Evasion, Persistence |


#### Kill Chain Phase

* Actions on Objective


#### Known False Positives
Logon errors may not be malicious in nature however it may indicate attempts to reuse a token or password obtained via credential access attack.

#### Reference


* https://stealthbits.com/blog/bypassing-mfa-with-pass-the-cookie/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1556/o365_sso_logon_errors/o365_sso_logon_errors.json


_version_: 1
</details>

---

### O365 New Federated Domain Added
This search detects the addition of a new Federated domain.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1136.003](https://attack.mitre.org/techniques/T1136/003/)
- **Last Updated**: 2021-01-26

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` Workload=Exchange Operation="Add-FederatedDomain" 
| stats count min(_time) as firstTime max(_time) as lastTime values(Parameters{}.Value) as Parameters.Value by ObjectId Operation OrganizationName OriginatingServer UserId UserKey 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `o365_new_federated_domain_added_filter`
```
#### Associated Analytic Story

* Office 365 Detections

* Cloud Federated Credential Abuse


#### How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity.

#### Required field

* _time

* Workload

* Operation

* Parameters{}.Value

* ObjectId

* OrganizationName

* OriginatingServer

* UserId

* UserKey



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1136.003 | Cloud Account | Persistence |


#### Kill Chain Phase

* Actions on Objective


#### Known False Positives
The creation of a new Federated domain is not necessarily malicious, however these events need to be followed closely, as it may indicate federated credential abuse or backdoor via federated identities at a similar or different cloud provider.

#### Reference


* https://www.fireeye.com/content/dam/fireeye-www/blog/pdfs/wp-m-unc2452-2021-000343-01.pdf

* https://us-cert.cisa.gov/ncas/alerts/aa21-008a

* https://www.splunk.com/en_us/blog/security/a-golden-saml-journey-solarwinds-continued.html

* https://www.sygnia.co/golden-saml-advisory

* https://o365blog.com/post/aadbackdoor/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.003/o365_new_federated_domain/o365_new_federated_domain.json


_version_: 1
</details>

---

### O365 PST export alert
This search detects when a user has performed an Ediscovery search or exported a PST file from the search. This PST file usually has sensitive information including email body content

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1114](https://attack.mitre.org/techniques/T1114/)
- **Last Updated**: 2020-12-16

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` Category=ThreatManagement Name="eDiscovery search started or exported" 
| stats count earliest(_time) as firstTime latest(_time) as lastTime by Source Severity AlertEntityId Operation Name 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `o365_pst_export_alert_filter`
```
#### Associated Analytic Story

* Office 365 Detections

* Data Exfiltration


#### How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

#### Required field

* _time

* Category

* Name

* Source

* Severity

* AlertEntityId

* Operation



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1114 | Email Collection | Collection |


#### Kill Chain Phase

* Actions on Objective


#### Known False Positives
PST export can be done for legitimate purposes but due to the sensitive nature of its content it must be monitored.

#### Reference


* https://attack.mitre.org/techniques/T1114/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114/o365_export_pst_file/o365_export_pst_file.json


_version_: 1
</details>

---

### O365 Suspicious Admin Email Forwarding
This search detects when an admin configured a forwarding rule for multiple mailboxes to the same destination.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1114.003](https://attack.mitre.org/techniques/T1114/003/)
- **Last Updated**: 2020-12-16

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` Operation=Set-Mailbox 
| spath input=Parameters 
| rename Identity AS src_user 
| search ForwardingAddress=* 
| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingAddress 
| where count_src_user > 1 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
|`o365_suspicious_admin_email_forwarding_filter`
```
#### Associated Analytic Story

* Office 365 Detections

* Data Exfiltration


#### How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

#### Required field

* _time

* Operation

* Parameters



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1114.003 | Email Forwarding Rule | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
unknown

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json


_version_: 1
</details>

---

### O365 Suspicious Rights Delegation
This search detects the assignment of rights to accesss content from another mailbox. This is usually only assigned to a service account.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1114.002](https://attack.mitre.org/techniques/T1114/002/)
- **Last Updated**: 2020-12-15

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` Operation=Add-MailboxPermission 
| spath input=Parameters 
| rename User AS src_user, Identity AS dest_user 
| search AccessRights=FullAccess OR AccessRights=SendAs OR AccessRights=SendOnBehalf 
| stats count earliest(_time) as firstTime latest(_time) as lastTime by user src_user dest_user Operation AccessRights 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
|`o365_suspicious_rights_delegation_filter`
```
#### Associated Analytic Story

* Office 365 Detections


#### How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

#### Required field

* _time

* Operation

* Parameters



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1114.002 | Remote Email Collection | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Service Accounts

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.002/suspicious_rights_delegation/suspicious_rights_delegation.json


_version_: 1
</details>

---

### O365 Suspicious User Email Forwarding
This search detects when multiple user configured a forwarding rule to the same destination.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1114.003](https://attack.mitre.org/techniques/T1114/003/)
- **Last Updated**: 2020-12-16

<details>
  <summary>details</summary>

#### Search
```
`o365_management_activity` Operation=Set-Mailbox 
| spath input=Parameters 
| rename Identity AS src_user 
| search ForwardingSmtpAddress=* 
| stats dc(src_user) AS count_src_user earliest(_time) as firstTime latest(_time) as lastTime values(src_user) AS src_user values(user) AS user by ForwardingSmtpAddress 
| where count_src_user > 1 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
|`o365_suspicious_user_email_forwarding_filter`
```
#### Associated Analytic Story

* Office 365 Detections

* Data Exfiltration


#### How To Implement
You must install splunk Microsoft Office 365 add-on. This search works with o365:management:activity

#### Required field

* _time

* Operation

* Parameters



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1114.003 | Email Forwarding Rule | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
unknown

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1114.003/o365_email_forwarding_rule/o365_email_forwarding_rule.json


_version_: 1
</details>

---

### Office Application Spawn Regsvr32 process
this detection was designed to identifies suspicious spawned process of known MS office application due to macro or malicious code. this technique can be seen in so many malware like Icedid that used MS office as its weapon or attack vector to initially infect the machines.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-07-30

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe" OR Processes.parent_process_name = "outlook.exe") Processes.process_name=regsvr32.exe by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `office_application_spawn_regsvr32_process_filter`
```
#### Associated Analytic Story

* Icedid


#### 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.

#### Required field

* _time

* Processes.parent_process_name

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.process_guid

* Processes.user

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.joesandbox.com/analysis/380662/0/html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/phish_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Office Application Spawn rundll32 process
this detection was designed to identifies suspicious spawned process of known MS office application due to macro or malicious code. this technique can be seen in so many malware like trickbot that used MS office as its weapon or attack vector to initially infect the machines.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-13

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name = "excel.exe" OR Processes.parent_process_name = "powerpnt.exe") Processes.process_name=rundll32.exe  by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `office_application_spawn_rundll32_process_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments

* Trickbot

* Icedid


#### 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.

#### Required field

* Processes.process

* Processes.parent_process_name

* _time

* Processes.process_name

* Processes.dest

* Processes.user

* Processes.process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://any.run/malware-trends/trickbot

* https://any.run/report/47561b4e949041eff0a0f4693c59c81726591779fe21183ae9185b5eb6a69847/aba3722a-b373-4dae-8273-8730fb40cdbe



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log


_version_: 1
</details>

---

### Office Document Creating Schedule Task
this search detects a potential malicious office document that create schedule task entry through macro VBA api or through loading taskschd.dll. This technique was seen in so many malicious macro malware that create persistence , beaconing using task schedule malware entry The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it's possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-14

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE") ImageLoaded = "*\\taskschd.dll" 
| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `office_document_creating_schedule_task_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.

#### Required field

* ImageLoaded

* AllImageLoaded

* Computer

* EventCode

* Image

* process_name

* ProcessId

* ProcessGuid

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/

* https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log


_version_: 1
</details>

---

### Office Document Executing Macro Code
this detection was designed to identifies suspicious office documents that using macro code. Macro code is known to be one of the prevalent weaponization or attack vector of threat actor. This malicious macro code is embed to a office document as an attachment that may execute malicious payload, download malware payload or other malware component. It is really good practice to disable macro by default to avoid automatically execute macro code while opening or closing a office document files.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-14

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=7 process_name IN ("WINWORD.EXE", "EXCEL.EXE", "POWERPNT.EXE") ImageLoaded IN ("*\\VBE7INTL.DLL","*\\VBE7.DLL", "*\\VBEUI.DLL") 
| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) as AllImageLoaded count by Computer EventCode Image process_name ProcessId ProcessGuid 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `office_document_executing_macro_code_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments

* Trickbot

* Icedid


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name and ImageLoaded (Like sysmon EventCode 7) from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Also be sure to include those monitored dll to your own sysmon config.

#### Required field

* ImageLoaded

* AllImageLoaded

* Computer

* EventCode

* Image

* process_name

* ProcessId

* ProcessGuid

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Normal Office Document macro use for automation

#### Reference


* https://www.joesandbox.com/analysis/386500/0/html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets/windows-sysmon.log


_version_: 1
</details>

---

### Office Document Spawned Child Process To Download
this search is to detect potential malicious office document executing lolbin child process to download payload or other malware. Since most of the attacker abused the capability of office document to execute living on land application to blend it to the normal noise in the infected machine to cover its track.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-06-23

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=1 parent_process_name IN ("powerpnt.exe", "winword.exe", "excel.exe", "visio.exe") process_name = "*.exe" cmdline IN ("*http:*","*https:*")  NOT(OriginalFileName IN("firefox.exe", "chrome.exe","iexplore.exe","msedge.exe")) 
| stats min(_time) as firstTime max(_time) as lastTime count by parent_process_name process_name parent_process cmdline process_id OriginalFileName ProcessGuid Computer EventCode 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `office_document_spawned_child_process_to_download_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments


#### 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. Tune and filter known instances office application and browser may be used.

#### Required field

* _time

* parent_process_name

* process_name

* parent_process

* cmdline

* process_id

* OriginalFileName

* ProcessGuid

* Computer

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
default browser not in the filter list

#### Reference


* https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/#



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/datasets2/windows-sysmon.log


_version_: 2
</details>

---

### Office Product Spawn CMD Process
this search is to detect a suspicious office product process that spawn cmd child process. This is commonly seen in a ms office product having macro to execute shell command to download or execute malicious lolbin relative to its malicious code. This is seen in trickbot spear phishing doc where it execute shell cmd to run mshta payload.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218/005/)
- **Last Updated**: 2021-07-19

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name = "winword.exe" OR Processes.parent_process_name= "excel.exe" OR Processes.parent_process_name = "powerpnt.exe") Processes.process_name=cmd.exe by Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.user Processes.dest 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `office_product_spawn_cmd_process_filter`
```
#### Associated Analytic Story

* Trickbot


#### 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.

#### Required field

* _time

* parent_process

* process_name

* process

* process_id

* process_guid



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.005 | Mshta | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
IT or network admin may create an document automation that will run shell script.

#### Reference


* https://twitter.com/cyb3rops/status/1416050325870587910?s=21



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/spear_phish/windows-sysmon.log


_version_: 1
</details>

---

### Office Product Spawning BITSAdmin
The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `bitsadmin.exe`. In malicious instances, the command-line of `bitsadmin.exe` will contain a URL to a remote destination or similar command-line arguments as transfer, Download, priority, Foreground. In addition, Threat Research has released a detections identifying suspicious use of `bitsadmin.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `bitsadmin.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=bitsadmin.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `office_product_spawning_bitsadmin_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest

* user

* parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
No false positives known. Filter as needed.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1197/T1197.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log


_version_: 1
</details>

---

### Office Product Spawning CertUtil
The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `certutil.exe`. In malicious instances, the command-line of `certutil.exe` will contain a URL to a remote destination. In addition, Threat Research has released a detections identifying suspicious use of `certutil.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `certutil.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=certutil.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `office_product_spawning_certutil_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest

* user

* parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
No false positives known. Filter as needed.

#### Reference


* https://redcanary.com/threat-detection-report/threats/TA551/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1105/T1105.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log


_version_: 1
</details>

---

### Office Product Spawning MSHTA
The following detection identifies the latest behavior utilized by different malware families (including TA551, IcedID). This detection identifies any Windows Office Product spawning `mshta.exe`. In malicious instances, the command-line of `mshta.exe` will contain the `hta` file locally, or a URL to the remote destination. In addition, Threat Research has released a detections identifying suspicious use of `mshta.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `mshta.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=mshta.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `office_product_spawning_mshta_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments

* Icedid


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest

* user

* parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
No false positives known. Filter as needed.

#### Reference


* https://redcanary.com/threat-detection-report/threats/TA551/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log


_version_: 1
</details>

---

### Office Product Spawning Rundll32 with no DLL
The following detection identifies the latest behavior utilized by IcedID malware family. This detection identifies any Windows Office Product spawning `rundll32.exe` without a `.dll` file extension. In malicious instances, the command-line of `rundll32.exe` will look like `rundll32 ..\oepddl.igk2,DllRegisterServer`. In addition, Threat Research has released a detection identifying the use of `DllRegisterServer` on the command-line of `rundll32.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze the `DLL` that was dropped to disk. The Office Product will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=rundll32.exe (Processes.process!=*.dll*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `office_product_spawning_rundll32_with_no_dll_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest

* user

* parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives should be limited, but if any are present, filter as needed.

#### Reference


* https://www.joesandbox.com/analysis/395471/0/html

* https://app.any.run/tasks/cef4b8ba-023c-4b3b-b2ef-6486a44f6ed9/

* https://any.run/malware-trends/icedid



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_icedid.log


_version_: 1
</details>

---

### Office Product Spawning Wmic
The following detection identifies the latest behavior utilized by Ursnif malware family. This detection identifies any Windows Office Product spawning `wmic.exe`. In malicious instances, the command-line of `wmic.exe` will contain `wmic process call create`. In addition, Threat Research has released a detection identifying the use of `wmic process call create` on the command-line of `wmic.exe`. In this instance, we narrow our detection down to the Office suite as a parent process. During triage, review all file modifications. Capture and analyze any artifacts on disk. The Office Product, or `wmic.exe` will have reached out to a remote destination, capture and block the IPs or domain. Review additional parallel processes for further activity.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name IN ("winword.exe","excel.exe","powerpnt.exe","mspub.exe","visio.exe") Processes.process_name=wmic.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `office_product_spawning_wmic_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest

* user

* parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
No false positives known. Filter as needed.

#### Reference


* https://app.any.run/tasks/fb894ab8-a966-4b72-920b-935f41756afd/

* https://attack.mitre.org/techniques/T1047/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1047/T1047.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_macros.log


_version_: 1
</details>

---

### Okta Account Lockout Events
Detect Okta user lockout events

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```
`okta` displayMessage="Max sign in attempts exceeded" 
| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city 
| table _time, user, country, state, city, src_ip 
| `okta_account_lockout_events_filter` 
```
#### Associated Analytic Story

* Suspicious Okta Activity


#### How To Implement
This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.

#### Required field

* _time

* displayMessage

* client.geographicalContext.country

* client.geographicalContext.state

* client.geographicalContext.city



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.001 | Default Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Okta Failed SSO Attempts
Detect failed Okta SSO events

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```
`okta` displayMessage="User attempted unauthorized access to app" 
| stats  min(_time) as firstTime max(_time) as lastTime values(app) as Apps count by user, result ,displayMessage, src_ip 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `okta_failed_sso_attempts_filter` 
```
#### Associated Analytic Story

* Suspicious Okta Activity


#### How To Implement
This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.

#### Required field

* _time

* displayMessage

* app

* user

* result

* src_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.001 | Default Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### Known False Positives
There may be a faulty config preventing legitmate users from accessing apps they should have access to.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Okta User Logins From Multiple Cities
This search detects logins from the same user from different cities in a 24 hour period.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078.001](https://attack.mitre.org/techniques/T1078/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```
`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null 
| stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `okta_user_logins_from_multiple_cities_filter` 
| search locations > 1
```
#### Associated Analytic Story

* Suspicious Okta Activity


#### How To Implement
This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.

#### Required field

* _time

* displayMessage

* client.geographicalContext.city

* client.geographicalContext.state

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078.001 | Default Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Overwriting Accessibility Binaries
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1546.008](https://attack.mitre.org/techniques/T1546/008/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where (Filesystem.file_path=*\\Windows\\System32\\sethc.exe* OR Filesystem.file_path=*\\Windows\\System32\\utilman.exe* OR Filesystem.file_path=*\\Windows\\System32\\osk.exe* OR Filesystem.file_path=*\\Windows\\System32\\Magnify.exe* OR Filesystem.file_path=*\\Windows\\System32\\Narrator.exe* OR Filesystem.file_path=*\\Windows\\System32\\DisplaySwitch.exe* OR Filesystem.file_path=*\\Windows\\System32\\AtBroker.exe*) by Filesystem.file_name Filesystem.dest 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `overwriting_accessibility_binaries_filter`
```
#### Associated Analytic Story

* Windows Privilege Escalation


#### 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.

#### Required field

* _time

* Filesystem.dest

* Filesystem.file_path

* Filesystem.file_name

* Filesystem.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1546.008 | Accessibility Features | Privilege Escalation, Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Microsoft may provide updates to these binaries. Verify that these changes do not correspond with your normal software update cycle.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.008/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Permission Modification using Takeown App
This search is to detect a modification of file or directory permission using takeown.exe windows app. This technique was seen in some ransomware that take the ownership of a folder or files to encrypt or delete it.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1222](https://attack.mitre.org/techniques/T1222/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "takeown.exe" Processes.process = "*/f*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `permission_modification_using_takeown_app_filter`
```
#### Associated Analytic Story

* Ransomware


#### 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.

#### Required field

* _time

* Processes.parent_process_name

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.process_id

* Processes.process_guid



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1222 | File and Directory Permissions Modification | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
takeown.exe is a normal windows application that may used by network operator.

#### Reference


* https://research.nccgroup.com/2020/06/23/wastedlocker-a-new-ransomware-variant-developed-by-the-evil-corp-group/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log


_version_: 1
</details>

---

### Phishing Email Detection by Machine Learning Method - SSA
Malicious mails can conduct phishing that induces readers to open attachment, click links or trigger third party service. This detect uses Natural Language Processing (NLP) approach to analyze an email message's content (Sender, Subject and Body) and judge whether it is a phishing email. The detection adopts a deep learning (neural network) model that employs character level embeddings plus LSTM layers to perform classification. The model is pre-trained and then published as ONNX format. Current sample model is trained using the dataset published at https://github.com/splunk/attack_data/tree/master/datasets/T1566_Phishing_Email/splunk_train.json User are expected to re-train the model by combining with their own training data for better accuracy using the provided model file (SMLE notebook). DSP pipeline then processes the email message and passes it as an event to Apply ML Models function, which returns the probability of a phishing email. Current implementation assumes the email is fed to DSP in JSON format contains at least email's sender, subject and its message body, including reply content, if any.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1566](https://attack.mitre.org/techniques/T1566/)
- **Last Updated**: 2020-08-25

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval eventLine=concat(ucast(map_get(input_event, "From"), "string", " "), " ", ucast(map_get(input_event, "Subject"), "string", " "), " ", ucast(map_get(input_event, "Content"), "string", " "), "                                                                                                                                "), _time=map_get(input_event, "_time") 
| where eventLine IS NOT NULL 
| eval mapC={" ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "`": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, "
|": 124, "}": 125, "~": 126}, ml_in = for_each(iterator(mvrange(1,129), "i"), cast(map_get(mapC, substr(eventLine, i, 1)), "float") ) 
| apply_model connection_id="YOUR_S3_ONNX_CONNECTOR_ID" name="phishing_email_v8" path="s3://smle-experiments/models/phishing_email" 
| eval probability = mvindex(ml_out, 0) 
| where probability > 0.5 
| eval start_time=_time, end_time=_time, entities="TBD", body="TBD" 
| select probability, body, entities, start_time, end_time 
| into write_ssa_detected_events();
```
#### Associated Analytic Story


#### How To Implement
Events are fed to DSP contains at least email's sender, subject and its message body.

#### Required field



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566 | Phishing | Initial Access |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Because of imbalance of anomaly data in training, the model will less likely report false positive. Instead, the model is more prone to false negative. Current best recall score is ~85%

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Plain HTTP POST Exfiltrated Data
This search is to detect potential plain HTTP POST method data exfiltration. This network traffic is commonly used by trickbot, trojanspy, keylogger or APT adversary where arguments or commands are sent in plain text to the remote C2 server using HTTP POST method as part of data exfiltration.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048/003/)
- **Last Updated**: 2021-04-22

<details>
  <summary>details</summary>

#### Search
```
`stream_http` http_method=POST form_data IN ("*wermgr.exe*","*svchost.exe*", "*name=\"proclist\"*","*ipconfig*", "*name=\"sysinfo\"*", "*net view*") 
|stats values(form_data) as http_request_body min(_time) as firstTime max(_time) as lastTime count by http_method http_user_agent uri_path url bytes_in bytes_out 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `plain_http_post_exfiltrated_data_filter`
```
#### Associated Analytic Story

* Command and Control

* Data Exfiltration


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the stream HTTP logs or network logs that catch network traffic. Make sure that the http-request-body, payload, or request field is enabled.

#### Required field

* _time

* http_method

* http_user_agent

* uri_path

* url

* bytes_in

* bytes_out



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration |


#### Kill Chain Phase

* Exfiltration


#### Known False Positives
unknown

#### Reference


* https://blog.talosintelligence.com/2020/03/trickbot-primer.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1048.003/plain_exfil_data/stream_http_events.log


_version_: 1
</details>

---

### PowerShell Domain Enumeration
The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies specific PowerShell modules typically used to enumerate an organizations domain or users. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message IN (*get-netdomaintrust*, *get-netforesttrust*, *get-addomain*, *get-adgroupmember*, *get-domainuser*) 
| stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_domain_enumeration_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* Message

* OpCode

* ComputerName

* User

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
It is possible there will be false positives, filter as needed.

#### Reference


* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell.log


_version_: 1
</details>

---

### PowerShell Loading DotNET into Memory via System Reflection Assembly
The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies the use of PowerShell loading .net assembly via reflection. This is commonly found in malicious PowerShell usage, including Empire and Cobalt Strike. In addition, the `load(` value may be modifed by removing `(` and it will identify more events to review. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message IN ("*[system.reflection.assembly]::load(*","*[reflection.assembly]*") 
| stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_loading_dotnet_into_memory_via_system_reflection_assembly_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* Message

* OpCode

* ComputerName

* User

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives should be limited as day to day scripts do not use this method.

#### Reference


* https://docs.microsoft.com/en-us/dotnet/api/system.reflection.assembly?view=net-5.0

* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell.log


_version_: 1
</details>

---

### PowerShell Start-BitsTransfer
Start-BitsTransfer is the PowerShell "version" of BitsAdmin.exe. Similar functionality is present. This technique variation is not as commonly used by adversaries, but has been abused in the past. Lesser known uses include the ability to set the `-TransferType` to `Upload` for exfiltration of files. In an instance where `Upload` is used, it is highly possible files will be archived. During triage, review parallel processes and process lineage. Capture any files on disk and review. For the remote domain or IP, what is the reputation?

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1197](https://attack.mitre.org/techniques/T1197/)
- **Last Updated**: 2021-03-29

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe Processes.process=*start-bitstransfer* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_start_bitstransfer_filter`
```
#### Associated Analytic Story

* BITS Jobs


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process

* Processes.parent_process

* Processes.process_name

* Processes.user

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1197 | BITS Jobs | Defense Evasion, Persistence |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives. It is possible administrators will utilize Start-BitsTransfer for administrative tasks, otherwise filter based parent process or command-line arguments.

#### Reference


* https://isc.sans.edu/diary/Investigating+Microsoft+BITS+Activity/23281

* https://docs.microsoft.com/en-us/windows/win32/bits/using-windows-powershell-to-create-bits-transfer-jobs



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1197/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Powershell Creating Thread Mutex
The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is using the `mutex` function. This function is commonly seen in some obfuscated PowerShell scripts to make sure that only one instance of there process is running on a compromise machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1027.005](https://attack.mitre.org/techniques/T1027/005/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message = "*Threading.Mutex*" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_creating_thread_mutex_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* EventCode

* Message

* ComputerName

* User



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1027.005 | Indicator Removal from Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
powershell developer may used this function in their script for instance checking too.

#### Reference


* https://isc.sans.edu/forums/diary/Some+Powershell+Malicious+Code/22988/

* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/pwsh/windows-powershell.log


_version_: 1
</details>

---

### Powershell Disable Security Monitoring
This search is to identifies a modification in registry to disable the windows denfender real time behavior monitoring. This event or technique is commonly seen in RAT, bot, or Trojan to disable AV to evade detections.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-07-05

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name IN ("powershell.exe", "pwsh.exe", "sqlps.exe", "sqltoolsps.exe") Processes.process="*set-mppreference*" AND Processes.process IN ("*disablerealtimemonitoring*","*disableioavprotection*","*disableintrusionpreventionsystem*","*disablescriptscanning*","*disableblockatfirstseen*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_disable_security_monitoring_filter`
```
#### Associated Analytic Story

* Ransomware

* Revil Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives. However, tune based on scripts that may perform this action.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1562.001/T1562.001.md#atomic-test-15---tamper-with-windows-defender-atp-powershell



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/pwh_defender_disabling/windows-sysmon.log


_version_: 1
</details>

---

### Powershell Enable SMB1Protocol Feature
This search is to detect a suspicious enabling of smb1protocol through "powershell.exe". This technique was seen in some ransomware (like reddot) where it enable smb share to do the lateral movement and encrypt other files within the compromise network system.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1027.005](https://attack.mitre.org/techniques/T1027/005/)
- **Last Updated**: 2021-06-22

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message = "*Enable-WindowsOptionalFeature*" Message = "*SMB1Protocol*" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_enable_smb1protocol_feature_filter`
```
#### Associated Analytic Story

* Malicious PowerShell

* Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the powershell logs  from your endpoints. make sure you enable needed registry to monitor this event.

#### Required field

* _time

* EventCode

* Message

* ComputerName

* User



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1027.005 | Indicator Removal from Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network operator may enable or disable this windows feature.

#### Reference


* https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-powershell.log


_version_: 1
</details>

---

### Powershell Fileless Process Injection via GetProcAddress
The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies `GetProcAddress` in the script block. This is not normal to be used by most PowerShell scripts and is typically unsafe/malicious. Many attack toolkits use GetProcAddress to obtain code execution. \
In use, `$var_gpa = $var_unsafe_native_methods.GetMethod(GetProcAddress` and later referenced/executed elsewhere. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/), [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2021-06-08

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message=*getprocaddress* 
| stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_fileless_process_injection_via_getprocaddress_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* Message

* OpCode

* ComputerName

* User

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives. Filter as needed.

#### Reference


* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell.log


_version_: 1
</details>

---

### Powershell Fileless Script Contains Base64 Encoded Content
The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies `FromBase64String` within the script block. A typical malicious instance will include additional code. \
Command example - `[Byte[]]$var_code = [System.Convert]::FromBase64String(38uqIyMjQ6rG....` \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1027](https://attack.mitre.org/techniques/T1027/), [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2021-06-08

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message=*frombase64string* 
| stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_fileless_script_contains_base64_encoded_content_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* Message

* OpCode

* ComputerName

* User

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1027 | Obfuscated Files or Information | Defense Evasion |
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Exploitation

* Privilege Escalation


#### Known False Positives
False positives should be limited. Filter as needed.

#### Reference


* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell.log


_version_: 1
</details>

---

### Powershell Processing Stream Of Data
The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is processing compressed stream data. This is typically found in obfuscated PowerShell or PowerShell executing embedded .NET or binary files that are stream flattened and will be deflated durnig execution. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message = "*IO.Compression.*" OR Message = "*IO.StreamReader*" OR Message = "*]::Decompress*" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_processing_stream_of_data_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* EventCode

* Message

* ComputerName

* User

* Score



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
powershell may used this function to process compressed data.

#### Reference


* https://medium.com/@ahmedjouini99/deobfuscating-emotets-powershell-payload-e39fb116f7b9

* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/pwsh/windows-powershell.log


_version_: 1
</details>

---

### Powershell Remote Thread To Known Windows Process
this search is designed to detect suspicious powershell process that tries to inject code and to known/critical windows process and execute it using CreateRemoteThread. This technique is seen in several malware like trickbot and offensive tooling like cobaltstrike where it load a shellcode to svchost.exe to execute reverse shell to c2 and download another payload

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-04-19

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode = 8 process_name IN ("powershell_ise.exe", "powershell.exe") TargetImage IN ("*\\svchost.exe","*\\csrss.exe" "*\\gpupdate.exe", "*\\explorer.exe","*\\services.exe","*\\winlogon.exe","*\\smss.exe","*\\wininit.exe","*\\userinit.exe","*\\spoolsv.exe","*\\taskhost.exe") 
| stats  min(_time) as firstTime max(_time) as lastTime count by SourceImage process_name SourceProcessId SourceProcessGuid TargetImage TargetProcessId NewThreadId StartAddress Computer EventCode 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_remote_thread_to_known_windows_process_filter`
```
#### Associated Analytic Story

* Trickbot


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name, Create Remote thread from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of create remote thread may be used.

#### Required field

* _time

* SourceImage

* process_name

* SourceProcessId

* SourceProcessGuid

* TargetImage

* TargetProcessId

* NewThreadId

* StartAddress

* Computer

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://thedfirreport.com/2021/01/11/trickbot-still-alive-and-well/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log


_version_: 1
</details>

---

### Powershell Using memory As Backing Store
The following analytic identifies suspicious PowerShell script execution via EventCode 4104 that is using memory stream as new object backstore. The malicious PowerShell script will contain stream flate data and will be decompressed in memory to run or drop the actual payload. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1140](https://attack.mitre.org/techniques/T1140/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message = "*New-Object IO.MemoryStream*" 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `powershell_using_memory_as_backing_store_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* EventCode

* Message

* ComputerName

* User



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1140 | Deobfuscate/Decode Files or Information | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
powershell may used this function to store out object into memory.

#### Reference


* https://www.carbonblack.com/blog/decoding-malicious-powershell-streams/

* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/pwsh/windows-powershell.log


_version_: 1
</details>

---

### Prevent Automatic Repair Mode using Bcdedit
This search is to detect a suspicious bcdedit.exe execution to ignore all failures. This technique was used by ransomware to prevent the compromise machine automatically boot in repair mode.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "bcdedit.exe" Processes.process = "*bootstatuspolicy*"  Processes.process = "*ignoreallfailures*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `prevent_automatic_repair_mode_using_bcdedit_filter`
```
#### Associated Analytic Story

* Ransomware


#### 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. Tune and filter known instances where renamed bcdedit.exe may be used.

#### Required field

* _time

* Processes.parent_process_name

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.process_id

* Processes.process_guid



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1490 | Inhibit System Recovery | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Administrators may modify the boot configuration ignore failure during testing and debugging.

#### Reference


* https://jsac.jpcert.or.jp/archive/2020/pdf/JSAC2020_1_tamada-yamazaki-nakatsuru_en.pdf



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log


_version_: 1
</details>

---

### Previously Seen AWS Cross Account Activity
This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-06-04

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=AssumeRole 
| spath output=requestingAccountId path=userIdentity.accountId 
| spath output=requestedAccountId path=resources{}.accountId 
| search requestingAccountId=* 
| where requestingAccountId!=requestedAccountId 
| stats earliest(_time) as firstTime latest(_time) as lastTime by requestingAccountId, requestedAccountId 
| outputlookup previously_seen_aws_cross_account_activity 
| stats count
```
#### Associated Analytic Story

* AWS Cross Account Activity


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search.

#### Required field

* _time

* eventName

* userIdentity.accountId

* resources{}.accountId




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen AWS Cross Account Activity - Initial
This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Authentication
- **ATT&CK**: 
- **Last Updated**: 2020-08-15

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role 
|  `drop_dm_object_name(Authentication)` 
| rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" 
|  where  vendor_account != dest_account 
| rename vendor_account as requestingAccountId dest_account as requestedAccountId 
| table requestingAccountId requestedAccountId firstTime lastTime 
| outputlookup previously_seen_aws_cross_account_activity
```
#### Associated Analytic Story

* Suspicious Cloud Authentication Activities


#### How To Implement
You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later)and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search.

#### Required field

* _time

* Authentication.signature

* Authentication.vendor_account

* Authentication.user

* Authentication.src

* Authentication.user_role




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen AWS Cross Account Activity - Update
This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Authentication
- **ATT&CK**: 
- **Last Updated**: 2020-08-15

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=AssumeRole by Authentication.vendor_account Authentication.user Authentication.src Authentication.user_role 
| `drop_dm_object_name(Authentication)` 
| rex field=user_role "arn:aws:sts:*:(?<dest_account>.*):" 
| where vendor_account != dest_account 
| rename vendor_account as requestingAccountId dest_account as requestedAccountId 
| inputlookup append=t previously_seen_aws_cross_account_activity 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by requestingAccountId requestedAccountId 
| outputlookup previously_seen_aws_cross_account_activity
```
#### Associated Analytic Story

* Suspicious Cloud Authentication Activities


#### How To Implement
You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search.

#### Required field

* _time

* Authentication.signature

* Authentication.vendor_account

* Authentication.user

* Authentication.src

* Authentication.user_role




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen AWS Provisioning Activity Sources
This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-03-16

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` (eventName=Run* OR eventName=Create*) 
| iplocation sourceIPAddress 
| stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country 
| outputlookup previously_seen_provisioning_activity_src.csv 
| stats count
```
#### Associated Analytic Story

* AWS Suspicious Provisioning Activities


#### 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.

#### Required field

* _time

* eventName

* sourceIPAddress




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen AWS Regions
This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-01-08

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` StartInstances 
| stats earliest(_time) as earliest latest(_time) as latest by awsRegion 
| outputlookup previously_seen_aws_regions.csv 
| stats count
```
#### Associated Analytic Story

* AWS Cryptomining

* Suspicious AWS EC2 Activities


#### 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.

#### Required field

* _time

* awsRegion




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud API Calls Per User Role - Initial
This search builds a table of the first and last times seen for every user role and command combination. This is broadly defined as any event that runs or creates something. This table is then cached.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-09-03

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) 
| table user, command, firstTimeSeen, lastTimeSeen, enough_data 
| outputlookup previously_seen_cloud_api_calls_per_user_role
```
#### Associated Analytic Story

* Suspicious Cloud User Activities


#### How To Implement
You must be ingesting Cloud infrastructure logs from your cloud provider.

#### Required field

* _time

* All_Changes.user_type

* All_Changes.status

* All_Changes.user

* All_Changes.command




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud API Calls Per User Role - Update
This search updates the table of the first and last times seen for every user role and command combination.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-09-03

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.user_type=AssumedRole AND All_Changes.status=success by All_Changes.user, All_Changes.command 
| `drop_dm_object_name("All_Changes")` 
| table user, command, firstTimeSeen, lastTimeSeen 
| inputlookup previously_seen_cloud_api_calls_per_user_role append=t 
| stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by user, command 
| where lastTimeSeen > relative_time(now(), `previously_seen_cloud_api_calls_per_user_role_forget_window`) 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) 
| table user, command, firstTimeSeen, lastTimeSeen, enough_data 
| outputlookup previously_seen_cloud_api_calls_per_user_role
```
#### Associated Analytic Story

* Suspicious Cloud User Activities


#### How To Implement
You must be ingesting Cloud infrastructure logs from your cloud provider.

#### Required field

* _time

* All_Changes.user_type

* All_Changes.status

* All_Changes.user

* All_Changes.command




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Compute Creations By User - Initial
This search builds a table of previously seen users that have launched a cloud compute instance.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-08-15

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created AND All_Changes.object_category=instance by All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| outputlookup previously_seen_cloud_compute_creations_by_user 
| stats count
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting the approrpiate cloud infrastructure logs and have the proper TAs installed.

#### Required field

* _time

* All_Changes.action

* All_Changes.object_category

* All_Changes.user




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Compute Creations By User - Update
This search builds a table of previously seen users that have launched a cloud compute instance.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-08-15

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created AND All_Changes.object_category=instance by All_Changes.user
| `drop_dm_object_name("All_Changes")` 
| inputlookup append=t previously_seen_cloud_compute_creations_by_user 
| stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by user 
| where lastTimeSeen > relative_time(now(), "-90d@d") 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) 
| outputlookup previously_seen_cloud_compute_creations_by_user
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting the approrpiate cloud infrastructure logs and have the proper TAs installed.

#### Required field

* _time

* All_Changes.action

* All_Changes.object_category

* All_Changes.user




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Compute Images - Initial
This search builds a table of previously seen images used to launch cloud compute instances

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-10-08

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created  by All_Changes.Instance_Changes.image_id 
| `drop_dm_object_name("All_Changes")` 
| `drop_dm_object_name("Instance_Changes")` 
| where image_id != "unknown" 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) 
| outputlookup previously_seen_cloud_compute_images
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting the approrpiate cloud infrastructure logs and have the latest Change Datamodel accelerated

#### Required field

* _time

* All_Changes.action

* All_Changes.Instance_Changes.image_id




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Compute Images - Update
This search builds a table of previously seen images used to launch cloud compute instances

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-08-12

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.image_id 
| `drop_dm_object_name("All_Changes")` 
| `drop_dm_object_name("Instance_Changes")` 
| where image_id != "unknown" 
| inputlookup append=t previously_seen_cloud_compute_images 
| stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by image_id 
| where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_images_forget_window`) 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) 
| outputlookup previously_seen_cloud_compute_images
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting the approrpiate cloud infrastructure logs

#### Required field

* _time

* All_Changes.action

* All_Changes.Instance_Changes.image_id




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Compute Instance Types - Initial
This search builds a table of previously seen cloud compute instance types

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-9-03

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type 
| `drop_dm_object_name("All_Changes.Instance_Changes")` 
| where instance_type != "unknown" 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) 
| outputlookup previously_seen_cloud_compute_instance_types
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.

#### Required field

* _time

* All_Changes.action

* All_Changes.Instance_Changes.instance_type




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Compute Instance Types - Update
This search builds a table of previously seen cloud compute instance types

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-9-03

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type 
| `drop_dm_object_name("All_Changes.Instance_Changes")` 
| where instance_type != "unknown" 
| inputlookup append=t previously_seen_cloud_compute_instance_types 
| stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by instance_type 
| where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_instance_type_forget_window`) 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) 
| outputlookup previously_seen_cloud_compute_instance_types
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting the approrpiate cloud infrastructure logs

#### Required field

* _time

* All_Changes.action

* All_Changes.Instance_Changes.instance_type




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Instance Modifications By User - Initial
This search builds a table of previously seen users that have modified a cloud instance.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-07-29

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 c=success by All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) 
| outputlookup previously_seen_cloud_instance_modifications_by_user
```
#### Associated Analytic Story

* Suspicious Cloud Instance Activities


#### How To Implement
You must be ingesting the approrpiate cloud infrastructure logs and have the latest Change Datamodel accelerated.

#### Required field

* _time

* All_Changes.action

* All_Changes.change_type

* All_Changes.status

* All_Changes.user




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Instance Modifications By User - Update
This search updates a table of previously seen Cloud Instance modifications that have been made by a user

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-07-29

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=modified All_Changes.change_type=EC2 All_Changes.status=success by All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| inputlookup append=t previously_seen_cloud_instance_modifications_by_user 
| stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by user 
| where lastTimeSeen > relative_time(now(), `previously_seen_cloud_compute_images_forget_window`) 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) 
| outputlookup previously_seen_cloud_instance_modifications_by_user
```
#### Associated Analytic Story

* Suspicious Cloud Instance Activities


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.

#### Required field

* _time

* All_Changes.action

* All_Changes.change_type

* All_Changes.status

* All_Changes.user




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Provisioning Activity Sources - Initial
This search builds a table of the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity. This is broadly defined as any event that runs or creates something. This table is then cached.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-08-19

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src 
| `drop_dm_object_name("All_Changes")` 
| iplocation src 
| where isnotnull(Country) 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) 
| table src, City, Country, Region, firstTimeSeen, lastTimeSeen, enough_data 
| outputlookup previously_seen_cloud_provisioning_activity_sources
```
#### Associated Analytic Story

* Suspicious Cloud Provisioning Activities


#### How To Implement
You must be ingesting Cloud infrastructure logs from your cloud provider.

#### Required field

* _time

* All_Changes.action

* All_Changes.src

* All_Changes.status




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Provisioning Activity Sources - Update
This returns the first and last times seen for every IP address (along with its physical location) previously associated with cloud-provisioning activity within the last day. Cloud provisioning is broadly defined as any event that runs or creates something.  It then updates this information with historical data and filters out locations that have not been seen within the specified time window. This updated table is then cached.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-08-20

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where (All_Changes.action=started OR All_Changes.action=created) All_Changes.status=success by All_Changes.src 
| `drop_dm_object_name("All_Changes")` 
| iplocation src 
| where isnotnull(Country) 
| table src, firstTimeSeen, lastTimeSeen, City, Country, Region 
| inputlookup previously_seen_cloud_provisioning_activity_sources append=t 
| stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by src, City, Country, Region 
| where lastTimeSeen > relative_time(now(), `previously_seen_cloud_provisioning_activity_forget_window`) 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-7d@d"), 1, 0) 
| table src, City, Country, Region, firstTimeSeen, lastTimeSeen, enough_data 
| outputlookup previously_seen_cloud_provisioning_activity_sources
```
#### Associated Analytic Story

* Suspicious Cloud Provisioning Activities


#### How To Implement
You must be ingesting Cloud infrastructure logs from your cloud provider.

#### Required field

* _time

* All_Changes.action

* All_Changes.src

* All_Changes.status




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Regions - Initial
This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-09-02

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region 
| `drop_dm_object_name("All_Changes")` 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) 
| outputlookup previously_seen_cloud_regions
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.

#### Required field

* _time

* All_Changes.action

* All_Changes.vendor_region




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Cloud Regions - Update
This search looks for cloud compute events where a compute instance is started and creates a baseline of most recent time, `lastTime` and the first time `firstTime` we've seen this region in our dataset grouped by the region for the last 30 days

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2020-09-02

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen from datamodel=Change where All_Changes.action=created by All_Changes.vendor_region 
| `drop_dm_object_name("All_Changes")` 
| inputlookup append=t previously_seen_cloud_regions 
| stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by vendor_region 
| where lastTimeSeen > relative_time(now(), `previously_seen_cloud_region_forget_window`) 
| eventstats min(firstTimeSeen) as globalFirstTime 
| eval enough_data = if(globalFirstTime <= relative_time(now(), "-14d@d"), 1, 0) 
| outputlookup previously_seen_cloud_regions 
| stats count
```
#### Associated Analytic Story

* Cloud Cryptomining


#### How To Implement
You must be ingesting the approrpiate cloud infrastructure logs and have the Security Research cloud data model installed.

#### Required field

* _time

* All_Changes.action

* All_Changes.vendor_region




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen EC2 AMIs
This search builds a table of previously seen AMIs used to launch EC2 instances

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-03-12

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=RunInstances errorCode=success 
| rename requestParameters.instancesSet.items{}.imageId as amiID 
| stats earliest(_time) as firstTime latest(_time) as lastTime by amiID 
| outputlookup previously_seen_ec2_amis.csv 
| stats count
```
#### Associated Analytic Story

* AWS Cryptomining


#### 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.

#### Required field

* _time

* eventName

* errorCode

* requestParameters.instancesSet.items{}.imageId




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen EC2 Instance Types
This search builds a table of previously seen EC2 instance types

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-03-08

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=RunInstances errorCode=success 
| rename requestParameters.instanceType as instanceType 
| fillnull value="m1.small" instanceType 
| stats earliest(_time) as earliest latest(_time) as latest by instanceType 
| outputlookup previously_seen_ec2_instance_types.csv 
| stats count
```
#### Associated Analytic Story

* AWS Cryptomining


#### 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.

#### Required field

* _time

* eventName

* errorCode

* requestParameters.instanceType




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen EC2 Launches By User
This search builds a table of previously seen ARNs that have launched a EC2 instance.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-03-15

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=RunInstances errorCode=success 
| rename userIdentity.arn as arn 
| stats earliest(_time) as firstTime latest(_time) as lastTime by arn 
| outputlookup previously_seen_ec2_launches_by_user.csv 
| stats count
```
#### Associated Analytic Story

* AWS Cryptomining

* Suspicious AWS EC2 Activities


#### 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.

#### Required field

* _time

* eventName

* errorCode

* requestParameters.instanceType




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen EC2 Modifications By User
This search builds a table of previously seen ARNs that have launched a EC2 instance.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-04-05

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` `ec2_modification_api_calls` errorCode=success 
| spath output=arn userIdentity.arn 
| stats earliest(_time) as firstTime latest(_time) as lastTime by arn 
| outputlookup previously_seen_ec2_modifications_by_user 
| stats count
```
#### Associated Analytic Story

* Unusual AWS EC2 Modifications


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2_modification_api_calls`.

#### Required field

* _time

* userIdentity.arn

* errorCode




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Running Windows Services - Initial
This collects the services that have been started across your entire enterprise.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-06-23

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_system` EventCode=7036 
| rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" 
| where state="running" 
| stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service 
| outputlookup previously_seen_running_windows_services
```
#### Associated Analytic Story

* Orangeworm Attack Group

* Windows Service Abuse

* NOBELIUM Group


#### How To Implement
While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.

#### Required field

* _time

* EventCode

* Message




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Previously Seen Running Windows Services - Update
This search returns the first and last time a Windows service was seen across your enterprise within the last hour. It then updates this information with historical data and filters out Windows services pairs that have not been seen within the specified time window. This updated table is then cached.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-06-23

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_system` EventCode=7036 
| rex field=Message "The (?<service>[-\(\)\s\w]+) service entered the (?<state>\w+) state" 
| where state="running" 
| stats earliest(_time) as firstTimeSeen, latest(_time) as lastTimeSeen by service 
| inputlookup previously_seen_running_windows_services append=t 
| stats min(firstTimeSeen) as firstTimeSeen, max(lastTimeSeen) as lastTimeSeen by service 
| where lastTimeSeen > relative_time(now(), "`previously_seen_windows_service_forget_window`") 
| outputlookup previously_seen_running_windows_services
```
#### Associated Analytic Story

* Orangeworm Attack Group

* Windows Service Abuse

* NOBELIUM Group


#### How To Implement
While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. Please ensure that the Splunk Add-on for Microsoft Windows is version 8.0.0 or above.

#### Required field

* _time

* EventCode

* Message




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Previously Seen Users In CloudTrail - Update
This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by user, within the last hour.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Authentication
- **ATT&CK**: 
- **Last Updated**: 2020-05-28

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src 
| iplocation Authentication.src 
| rename Authentication.user as user Authentication.src as src 
| table user src City Region Country firstTime lastTime 
| inputlookup append=t previously_seen_users_console_logins 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country 
| outputlookup previously_seen_users_console_logins
```
#### Associated Analytic Story

* Suspicious Cloud Authentication Activities


#### How To Implement
You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins`, which is a lookup file created by this support search.

#### Required field

* _time

* Authentication.signature

* Authentication.user

* Authentication.src




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Users in CloudTrail - Initial
This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by username, within the last 30 days.

- **Product**: Splunk Security Analytics for AWS, Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Authentication
- **ATT&CK**: 
- **Last Updated**: 2020-05-28

<details>
  <summary>details</summary>

#### Search
```

| tstats earliest(_time) as firstTime latest(_time) as lastTime from datamodel=Authentication where Authentication.signature=ConsoleLogin by Authentication.user Authentication.src 
| iplocation Authentication.src 
| rename Authentication.user as user Authentication.src as src 
| table user src City Region Country firstTime lastTime 
| outputlookup previously_seen_users_console_logins 
| stats count
```
#### Associated Analytic Story

* Suspicious Cloud Authentication Activities


#### How To Implement
You must install and configure the Splunk Add-on for AWS (version 5.1.0 or later) and Enterprise Security 6.2, which contains the required updates to the Authentication data model for cloud use cases. Validate the user name entries in `previously_seen_users_console_logins`, which is a lookup file created by this support search.

#### Required field

* _time

* Authentication.signature

* Authentication.user

* Authentication.src




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Zoom Child Processes - Initial
This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS). This table is then cached.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2020-05-20

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest
| `drop_dm_object_name(Processes)` 
| table dest, process_name, firstTimeSeen, lastTimeSeen 
| outputlookup zoom_first_time_child_process
```
#### Associated Analytic Story

* Suspicious Zoom Child Processes


#### 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.

#### Required field

* _time

* Processes.parent_process_name

* Processes.process_name

* Processes.dest




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously Seen Zoom Child Processes - Update
This search returns the first and last time a process was seen per endpoint with a parent process of zoom.exe (Windows) or zoom.us (macOS) within the last hour. It then updates this information with historical data and filters out proces_name and endpoint pairs that have not been seen within the specified time window. This updated table is outputed to disk.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2020-05-20

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` min(_time) as firstTimeSeen max(_time) as lastTimeSeen from datamodel=Endpoint.Processes where (Processes.parent_process_name=zoom.exe OR Processes.parent_process_name=zoom.us) by Processes.process_name Processes.dest
| `drop_dm_object_name(Processes)` 
| table firstTimeSeen, lastTimeSeen, process_name, dest 
| inputlookup zoom_first_time_child_process append=t 
| stats min(firstTimeSeen) as firstTimeSeen max(lastTimeSeen) as lastTimeSeen by process_name, dest 
| where lastTimeSeen > relative_time(now(), "`previously_seen_zoom_child_processes_forget_window`") 
| outputlookup zoom_first_time_child_process
```
#### Associated Analytic Story

* Suspicious Zoom Child Processes


#### 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.

#### Required field

* _time

* Processes.parent_process_name

* Processes.process_name

* Processes.dest




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously seen API call per user roles in CloudTrail
This search looks for successful API calls made by different user roles, then creates a baseline of the earliest and latest times we have encountered this user role. It also returns the name of the API call in our dataset--grouped by user role and name of the API call--that occurred within the last 30 days. In this support search, we are only looking for events where the user identity is Assumed Role.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-04-16

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole 
| stats earliest(_time) as earliest latest(_time) as latest by userName eventName 
| outputlookup previously_seen_api_calls_from_user_roles 
| stats count
```
#### Associated Analytic Story

* AWS User Monitoring


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user role entries in `previously_seen_api_calls_from_user_roles.csv`, which is a lookup file created as a result of running this support search.

#### Required field

* _time

* eventType

* errorCode

* userIdentity.type

* userName

* eventName




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously seen S3 bucket access by remote IP
This search looks for successful access to S3 buckets from remote IP addresses, then creates a baseline of the earliest and latest times we have encountered this remote IP within the last 30 days. In this support search, we are only looking for S3 access events where the HTTP response code from AWS is "200"

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-06-28

<details>
  <summary>details</summary>

#### Search
```
`aws_s3_accesslogs` http_status=200  
| stats  earliest(_time) as earliest latest(_time) as latest by bucket_name remote_ip 
| outputlookup previously_seen_S3_access_from_remote_ip 
| stats count
```
#### Associated Analytic Story

* Suspicious AWS S3 Activities


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access-logs inputs. You must validate the remote IP and bucket name entries in `previously_seen_S3_access_from_remote_ip.csv`, which is a lookup file created as a result of running this support search.

#### Required field

* _time

* http_status

* bucket_name

* remote_ip




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Previously seen command line arguments
This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: 
- **Last Updated**: 2019-03-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe AND Processes.process="* /c *" by Processes.process 
| `drop_dm_object_name(Processes)`
```
#### Associated Analytic Story

* DHS Report TA18-074A

* Disabling Security Tools

* Hidden Cobra Malware

* Netsh Abuse

* Orangeworm Attack Group

* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

* Suspicious Command-Line Executions

* Suspicious MSHTA Activity

* Icedid


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Previously seen users in CloudTrail
This search looks for CloudTrail events where a user logs into the console, then creates a baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last 30 days. NOTE - This baseline search is deprecated and has been updated to use the Authentication Datamodel

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-04-30

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=ConsoleLogin 
| rename userIdentity.arn as user 
| iplocation src 
| eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) 
| stats earliest(_time) as firstTime latest(_time) as lastTime by user src City Region Country 
| outputlookup previously_seen_users_console_logins_cloudtrail 
| stats count
```
#### Associated Analytic Story

* Suspicious AWS Login Activities


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`, which is a lookup file created as a result of running this support search.

#### Required field

* _time

* eventName

* userIdentity.arn

* src




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Print Spooler Adding A Printer Driver
The following analytic identifies new printer drivers being load by utilizing the Windows PrintService operational logs, EventCode 316. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \
Within the proof of concept code, the following event will occur - "Printer driver 1234 for Windows x64 Version-3 was added or updated. Files:- UNIDRV.DLL, kernelbase.dll, evil.dll. No user action is required." \
During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events and review the source of where the exploitation began.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1547.012](https://attack.mitre.org/techniques/T1547/012/)
- **Last Updated**: 2021-07-01

<details>
  <summary>details</summary>

#### Search
```
`printservice` EventCode=316 category = "Adding a printer driver" Message = "*kernelbase.dll,*" Message = "*UNIDRV.DLL,*" Message = "*.DLL.*" 
| stats  count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `print_spooler_adding_a_printer_driver_filter`
```
#### Associated Analytic Story

* PrintNightmare CVE-2021-34527


#### How To Implement
You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems.

#### Required field

* _time

* OpCode

* EventCode

* ComputerName

* Message



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1547.012 | Print Processors | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Unknown. This may require filtering.

#### Reference


* https://twitter.com/MalwareJake/status/1410421445608476679?s=20

* https://blog.truesec.com/2021/06/30/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available/

* https://blog.truesec.com/2021/06/30/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675/

* https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-printservice_operational.log


_version_: 1
</details>

---

### Print Spooler Failed to Load a Plug-in
The following analytic identifies driver load errors utilizing the Windows PrintService Admin logs. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. \
Within the proof of concept code, the following error will occur - "The print spooler failed to load a plug-in module C:\Windows\system32\spool\DRIVERS\x64\3\meterpreter.dll, error code 0x45A. See the event user data for context information." \
The analytic is based on file path and failure to load the plug-in. \
During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1547.012](https://attack.mitre.org/techniques/T1547/012/)
- **Last Updated**: 2021-07-01

<details>
  <summary>details</summary>

#### Search
```
`printservice` ((ErrorCode="0x45A" (EventCode="808" OR EventCode="4909")) OR ("The print spooler failed to load a plug-in module" OR "\\drivers\\x64\\")) 
| stats count min(_time) as firstTime max(_time) as lastTime by OpCode EventCode ComputerName Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `print_spooler_failed_to_load_a_plug_in_filter`
```
#### Associated Analytic Story

* PrintNightmare CVE-2021-34527


#### How To Implement
You will need to ensure PrintService Admin and Operational logs are being logged to Splunk from critical or all systems.

#### Required field

* _time

* OpCode

* EventCode

* ComputerName

* Message



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1547.012 | Print Processors | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives are unknown and filtering may be required.

#### Reference


* https://blog.truesec.com/2021/06/30/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available/

* https://blog.truesec.com/2021/06/30/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675/

* https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes



#### Test Dataset


_version_: 1
</details>

---

### Probing Access with Stolen Credentials via PowerSploit modules
This detection identifies use of PowerSploit modules that facilitate access probing with admin credentials as well as probing access to system services.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/)
- **Last Updated**: 2020-11-04

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Test-AdminAccess/)=true OR match_regex(cmd_line, /(?i)Invoke-CheckLocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Test-ServiceDaclPermission/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Privilege Escalation


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_user_id

* dest_device_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1098 | Account Manipulation | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset


_version_: 1
</details>

---

### Process Creating LNK file in Suspicious Location
This search looks for a process launching an `*.lnk` file under `C:\User*` or `*\Local\Temp\*`. This is common behavior used by various spear phishing tools.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1566.002](https://attack.mitre.org/techniques/T1566/002/)
- **Last Updated**: 2021-01-28

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name="*.lnk" AND Filesystem.file_path="C:\\Temp*"  by _time span=1h Filesystem.process_id Filesystem.file_name Filesystem.file_path Filesystem.file_hash Filesystem.user 
| `drop_dm_object_name(Filesystem)` 
| rename process_id as lnk_pid 
| join lnk_pid, _time [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=*  by _time span=1h Processes.parent_process_id Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process 
| `drop_dm_object_name(Processes)` 
| rename parent_process_id as lnk_pid 
| fields _time lnk_pid process_id dest process_name process_path process] 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| table firstTime, lastTime, lnk_pid, process_id, user, dest, file_name, file_path, process_name, process, process_path, file_hash 
| `process_creating_lnk_file_in_suspicious_location_filter` 
```
#### Associated Analytic Story

* Spearphishing Attachments


#### 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 product, such as Carbon Black, or endpoint data sources, such as Sysmon.

#### Required field

* _time

* Filesystem.file_name

* Filesystem.file_path

* Filesystem.process_id

* Filesystem.file_name

* Filesystem.file_path

* Filesystem.file_hash

* Filesystem.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.002 | Spearphishing Link | Initial Access |


#### Kill Chain Phase

* Installation

* Actions on Objectives


#### Known False Positives
This detection should yield little or no false positive results. It is uncommon for LNK files to be executed from temporary or user directories.

#### Reference


* https://attack.mitre.org/techniques/T1566/001/

* https://www.trendmicro.com/en_us/research/17/e/rising-trend-attackers-using-lnk-files-download-malware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.002/lnk_file_temp_folder/windows-sysmon.log


_version_: 4
</details>

---

### Process Deleting Its Process File Path
This detection is to identify a suspicious process that tries to delete the process file path related to its process. This technique is known to be defense evasion once a certain condition of malware is satisfied or not. Clop ransomware use this technique where it will try to delete its process file path using a .bat command if the keyboard layout is not the layout it tries to infect.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003/002/)
- **Last Updated**: 2021-03-17

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=1 cmdline = "*/c del*" Image = "*\\cmd.exe" 
|eval result = if(like(process,"%".parent_process."%"), "Found", "Not Found") 
| stats min(_time) as firstTime max(_time) as lastTime count by Computer user ParentImage ParentCommandLine Image cmdline EventCode ProcessID result 
| where result = "Found" 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `process_deleting_its_process_file_path_filter`
```
#### Associated Analytic Story

* Clop Ransomware


#### 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.

#### Required field

* EventCode

* Computer

* user

* ParentImage

* ParentCommandLine

* Image

* cmdline

* ProcessID

* result

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.002 | Security Account Manager | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log


_version_: 1
</details>

---

### Process Execution via WMI
The following analytic identifies `WmiPrvSE.exe` spawning a process. This typically occurs when a process is instantiated from a local or remote process using `wmic.exe`. During triage, review parallel processes for suspicious behavior or commands executed. Review the process and command-line spawning from `wmiprvse.exe`. Contain and remediate the endpoint as necessary.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/)
- **Last Updated**: 2020-03-16

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=WmiPrvSE.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `process_execution_via_wmi_filter` 
```
#### Associated Analytic Story

* Suspicious WMI Use


#### 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.

#### Required field

* _time

* Processes.process

* Processes.parent_process_name

* Processes.user

* Processes.dest

* Processes.process_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1047 | Windows Management Instrumentation | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, administrators may use wmi to execute commands for legitimate purposes.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Process Kill Base On File Path
The following analytic identifies the use of `wmic.exe` using `delete` to remove a executable path. This is typically ran via a batch file during beginning stages of an adversary setting up for mining on an endpoint.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-05-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where   Processes.process_name = "wmic.exe" AND Processes.process="*process*" AND Processes.process="*executablepath*" AND Processes.process="*delete*" by  Processes.parent_process_name Processes.process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `process_kill_base_on_file_path_filter`
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed wmic.exe may be used.

#### Required field

* _time

* Processes.parent_process_name

* Processes.process_name

* Processes.dest

* Processes.user

* Processes.process

* Processes.process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Unknown.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Processes Tapping Keyboard Events
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

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2019-01-25

<details>
  <summary>details</summary>

#### Search
```

| from datamodel Alerts.Alerts 
| search app=osquery:results name=pack_osx-attacks_Keyboard_Event_Taps 
| rename columns.cmdline as cmd, columns.name as process_name, columns.pid as process_id
| dedup host,process_name 
| table host,process_name, cmd, process_id 
| `processes_tapping_keyboard_events_filter`
```
#### Associated Analytic Story

* ColdRoot MacOS RAT


#### 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.

#### Required field

* _time

* app

* name

* columns.cmdline

* columns.name

* columns.pid

* host




#### Kill Chain Phase

* Command and Control


#### 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.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Processes launching netsh
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.004](https://attack.mitre.org/techniques/T1562/004/)
- **Last Updated**: 2020-07-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) AS Processes.process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*netsh* by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.user Processes.dest 
|`drop_dm_object_name("Processes")` 
|`security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
|`processes_launching_netsh_filter`
```
#### Associated Analytic Story

* Netsh Abuse

* Disabling Security Tools

* DHS Report TA18-074A


#### 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

#### Required field

* _time

* Processes.process

* Processes.parent_process_name

* Processes.parent_process

* Processes.process_name

* Processes.user

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.004 | Disable or Modify System Firewall | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.004/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Prohibited Network Traffic Allowed
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1048](https://attack.mitre.org/techniques/T1048/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action 
| lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport 
| search is_prohibited=true 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Traffic")` 
| `prohibited_network_traffic_allowed_filter`
```
#### Associated Analytic Story

* Prohibited Traffic Allowed or Protocol Mismatch

* Ransomware

* Command and Control


#### 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.

#### Required field

* _time

* All_Traffic.action

* All_Traffic.src_ip

* All_Traffic.dest_ip

* All_Traffic.dest_port



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1048 | Exfiltration Over Alternative Protocol | Exfiltration |


#### Kill Chain Phase

* Delivery

* Command and Control


#### Known False Positives
None identified

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Protocol or Port Mismatch
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1048.003](https://attack.mitre.org/techniques/T1048/003/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where (All_Traffic.app=dns NOT All_Traffic.dest_port=53) OR ((All_Traffic.app=web-browsing OR All_Traffic.app=http) NOT (All_Traffic.dest_port=80 OR All_Traffic.dest_port=8080 OR All_Traffic.dest_port=8000)) OR (All_Traffic.app=ssl NOT (All_Traffic.dest_port=443 OR All_Traffic.dest_port=8443)) OR (All_Traffic.app=smtp NOT All_Traffic.dest_port=25) by All_Traffic.src_ip, All_Traffic.dest_ip, All_Traffic.app, All_Traffic.dest_port 
|`security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Traffic")` 
| `protocol_or_port_mismatch_filter`
```
#### Associated Analytic Story

* Prohibited Traffic Allowed or Protocol Mismatch

* Command and Control


#### 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.

#### Required field

* _time

* All_Traffic.app

* All_Traffic.dest_port

* All_Traffic.src_ip

* All_Traffic.dest_ip



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1048.003 | Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol | Exfiltration |


#### Kill Chain Phase

* Command and Control


#### Known False Positives
None identified

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Protocols passing authentication in cleartext
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: 
- **Last Updated**: 2020-11-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.transport="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) by All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Traffic")` 
| `protocols_passing_authentication_in_cleartext_filter`
```
#### Associated Analytic Story

* Use of Cleartext Protocols


#### How To Implement
This search requires you to be ingesting your network traffic, and populating the Network_Traffic data model.

#### Required field

* _time

* All_Traffic.transport

* All_Traffic.dest_port

* All_Traffic.user

* All_Traffic.src

* All_Traffic.dest




#### Kill Chain Phase

* Reconnaissance

* Actions on Objectives


#### Known False Positives
Some networks may use kerberized FTP or telnet servers, however, this is rare.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Ransomware Notes bulk creation
The following analytics identifies a big number of instance of ransomware notes (filetype e.g .txt, .html, .hta) file creation to the infected machine. This behavior is a good sensor if the ransomware note filename is quite new for security industry or the ransomware note filename is not in your ransomware lookup table list for monitoring.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/)
- **Last Updated**: 2021-03-12

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=11 file_name IN ("*\.txt","*\.html","*\.hta") 
|bin _time span=10s 
| stats min(_time) as firstTime max(_time) as lastTime dc(TargetFilename) as unique_readme_path_count values(TargetFilename) as list_of_readme_path by Computer Image file_name 
| where unique_readme_path_count >= 15 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `ransomware_notes_bulk_creation_filter`
```
#### Associated Analytic Story

* Clop Ransomware

* DarkSide Ransomware


#### How To Implement
You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

#### Required field

* EventCode

* file_name

* _time

* TargetFilename

* Computer

* Image

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1486 | Data Encrypted for Impact | Impact |


#### Kill Chain Phase

* Obfuscation


#### Known False Positives
unknown

#### Reference


* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log


_version_: 1
</details>

---

### Rare Parent-Child Process Relationship
An attacker may use LOLBAS tools spawned from vulnerable applications not typically used by system administrators. This search leverages the Splunk Streaming ML DSP plugin to find rare parent/child relationships. The list of application has been extracted from https://github.com/LOLBAS-Project/LOLBAS/tree/master/yml/OSBinaries

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/), [T1059](https://attack.mitre.org/techniques/T1059/), [T1053](https://attack.mitre.org/techniques/T1053/), [T1072](https://attack.mitre.org/techniques/T1072/)
- **Last Updated**: 2021-05-20

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval parent_process=lower(ucast(map_get(input_event, "parent_process_name"), "string", null)), parent_process_name=mvindex(split(parent_process, "\\"), -1), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where parent_process_name!=null 
| select parent_process_name, process_name, cmd_line, timestamp, dest_device_id, dest_user_id 
| conditional_anomaly conditional="parent_process_name" target="process_name" 
| where (process_name="powershell.exe" OR process_name="regsvcs.exe" OR process_name="ftp.exe" OR process_name="dfsvc.exe" OR process_name="rasautou.exe" OR process_name="schtasks.exe" OR process_name="xwizard.exe" OR process_name="findstr.exe" OR process_name="esentutl.exe" OR process_name="cscript.exe" OR process_name="reg.exe" OR process_name="csc.exe" OR process_name="atbroker.exe" OR process_name="print.exe" OR process_name="pcwrun.exe" OR process_name="vbc.exe" OR process_name="rpcping.exe" OR process_name="wsreset.exe" OR process_name="ilasm.exe" OR process_name="certutil.exe" OR process_name="replace.exe" OR process_name="mshta.exe" OR process_name="bitsadmin.exe" OR process_name="wscript.exe" OR process_name="ieexec.exe" OR process_name="cmd.exe" OR process_name="microsoft.workflow.compiler.exe" OR process_name="runscripthelper.exe" OR process_name="makecab.exe" OR process_name="forfiles.exe" OR process_name="desktopimgdownldr.exe" OR process_name="control.exe" OR process_name="msbuild.exe" OR process_name="register-cimprovider.exe" OR process_name="tttracer.exe" OR process_name="ie4uinit.exe" OR process_name="sc.exe" OR process_name="bash.exe" OR process_name="hh.exe" OR process_name="cmstp.exe" OR process_name="mmc.exe" OR process_name="jsc.exe" OR process_name="scriptrunner.exe" OR process_name="odbcconf.exe" OR process_name="extexport.exe" OR process_name="msdt.exe" OR process_name="diskshadow.exe" OR process_name="extrac32.exe" OR process_name="eventvwr.exe" OR process_name="mavinject.exe" OR process_name="regasm.exe" OR process_name="gpscript.exe" OR process_name="rundll32.exe" OR process_name="regsvr32.exe" OR process_name="regedit.exe" OR process_name="msiexec.exe" OR process_name="gfxdownloadwrapper.exe" OR process_name="presentationhost.exe" OR process_name="regini.exe" OR process_name="wmic.exe" OR process_name="runonce.exe" OR process_name="syncappvpublishingserver.exe" OR process_name="verclsid.exe" OR process_name="psr.exe" OR process_name="infdefaultinstall.exe" OR process_name="explorer.exe" OR process_name="expand.exe" OR process_name="installutil.exe" OR process_name="netsh.exe" OR process_name="wab.exe" OR process_name="dnscmd.exe" OR process_name="at.exe" OR process_name="pcalua.exe" OR process_name="cmdkey.exe" OR process_name="msconfig.exe") 
| eval input = (-1)*log(output) 
| adaptive_threshold algorithm="gaussian" threshold=0.001 window=604800000L 
| where label AND input > mean 
| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body = create_map(["process_name", process_name, "parent_process_name", parent_process_name, "input", input, "mean", mean, "variance", variance, "output", output, "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Unusual Processes


#### How To Implement
Collect endpoint data such as sysmon or 4688 events.

#### Required field

* process

* process_name

* parent_process_name

* _time

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1203 | Exploitation for Client Execution | Execution |
| T1059 | Command and Scripting Interpreter | Execution |
| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation |
| T1072 | Software Deployment Tools | Execution, Lateral Movement |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Some custom tools used by admins could be used rarely to launch remotely applications. This might trigger false positives at the beginning when it hasn't collected yet enough data to construct the baseline.


#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Recon AVProduct Through Pwh or WMI
The following analytic identifies suspicious PowerShell script execution via EventCode 4104 performing checks to identify anti-virus products installed on the endpoint. This technique is commonly found in malware and APT events where the adversary will map all running security applications or services. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1592](https://attack.mitre.org/techniques/T1592/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 (Message = "*SELECT*" OR Message = "*WMIC*") AND (Message = "*AntiVirusProduct*" OR Message = "*AntiSpywareProduct*") 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `recon_avproduct_through_pwh_or_wmi_filter`
```
#### Associated Analytic Story

* Ransomware

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* EventCode

* Message

* ComputerName

* User



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1592 | Gather Victim Host Information | Reconnaissance |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
network administrator may used this command for checking purposes

#### Reference


* https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/

* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/pwsh/windows-powershell.log


_version_: 1
</details>

---

### Recon Using WMI Class
The following analytic identifies suspicious PowerShell via EventCode 4104, where WMI is performing an event query looking for running processes or running services. This technique is commonly found where the adversary will identify services and system information on the compromised machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1592](https://attack.mitre.org/techniques/T1592/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 (Message= "*SELECT*" OR Message= "*Get-WmiObject*") AND (Message= "*Win32_Bios*" OR Message= "*Win32_OperatingSystem*" OR Message= "*Win32_Processor*" OR Message= "*Win32_ComputerSystem*" OR Message= "*Win32_ComputerSystemProduct*" OR Message= "*Win32_ShadowCopy*") 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `recon_using_wmi_class_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* EventCode

* Message

* ComputerName

* User



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1592 | Gather Victim Host Information | Reconnaissance |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
network administrator may used this command for checking purposes

#### Reference


* https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/

* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/pwsh/windows-powershell.log


_version_: 1
</details>

---

### Reconnaissance and Access to Accounts Groups and Policies via PowerSploit modules
This detection identifies access to PowerSploit modules that discover accounts, groups and policies that can be accessed or taken over.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1087](https://attack.mitre.org/techniques/T1087/), [T1484](https://attack.mitre.org/techniques/T1484/)
- **Last Updated**: 2020-11-05

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Invoke-EnumerateLocalAdmin/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Invoke-EventHunter/)=true OR match_regex(cmd_line, /(?i)Find-DomainUserLocation/)=true OR match_regex(cmd_line, /(?i)Invoke-UserHunter/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignGroupMember/)=true OR match_regex(cmd_line, /(?i)Find-ForeignGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainForeignUser/)=true OR match_regex(cmd_line, /(?i)Find-ForeignUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPO/)=true OR match_regex(cmd_line, /(?i)Get-NetGPO/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOComputerLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOComputerAdmin/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGPOGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGPOUserLocalGroupMapping/)=true OR match_regex(cmd_line, /(?i)Find-GPOLocation/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetGroup/)=true OR match_regex(cmd_line, /(?i)Get-DomainGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-DomainManagedSecurityGroup/)=true OR match_regex(cmd_line, /(?i)Find-ManagedSecurityGroups/)=true OR match_regex(cmd_line, /(?i)Get-DomainOU/)=true OR match_regex(cmd_line, /(?i)Get-NetOU/)=true OR match_regex(cmd_line, /(?i)Get-DomainUser/)=true OR match_regex(cmd_line, /(?i)Get-NetUser/)=true OR match_regex(cmd_line, /(?i)Get-DomainUserEvent/)=true OR match_regex(cmd_line, /(?i)Get-UserEvent/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroup/)=true OR match_regex(cmd_line, /(?i)Get-NetLocalGroupMember/)=true OR match_regex(cmd_line, /(?i)Get-NetLoggedon/)=true OR match_regex(cmd_line, /(?i)Get-RegLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegLastLoggedOn/)=true OR match_regex(cmd_line, /(?i)Get-LastLoggedOn/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1087 | Account Discovery | Discovery |
| T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance and Access to Accounts and Groups via Mimikatz modules
This detection identifies use of Mimikatz modules for discovery of accounts and groups and access to them.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/), [T1087](https://attack.mitre.org/techniques/T1087/), [T1484](https://attack.mitre.org/techniques/T1484/)
- **Last Updated**: 2020-11-05

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::user/)=true OR match_regex(cmd_line, /(?i)net::group/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1087 | Account Discovery | Discovery |
| T1484 | Domain Policy Modification | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance and Access to Active Directoty Infrastructure via PowerSploit modules
This detection identifies access to PowerSploit modules for reconnaissance and access to elements of Active Directory infrastructure, such as domain identifiers, AD sites and forests, and trust relations.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1199](https://attack.mitre.org/techniques/T1199/), [T1482](https://attack.mitre.org/techniques/T1482/), [T1590](https://attack.mitre.org/techniques/T1590/), [T1591](https://attack.mitre.org/techniques/T1591/), [T1595](https://attack.mitre.org/techniques/T1595/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainSID/)=true OR match_regex(cmd_line, /(?i)Get-DomainSite/)=true OR match_regex(cmd_line, /(?i)Get-NetSite/)=true OR match_regex(cmd_line, /(?i)Get-DomainSubnet/)=true OR match_regex(cmd_line, /(?i)Get-NetSubnet/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-DomainTrustMapping/)=true OR match_regex(cmd_line, /(?i)Invoke-MapDomainTrust/)=true OR match_regex(cmd_line, /(?i)Get-Forest/)=true OR match_regex(cmd_line, /(?i)Get-NetForest/)=true OR match_regex(cmd_line, /(?i)Get-ForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-NetForestDomain/)=true OR match_regex(cmd_line, /(?i)Get-ForestGlobalCatalog/)=true OR match_regex(cmd_line, /(?i)Get-NetForestCatalog/)=true OR match_regex(cmd_line, /(?i)Get-ForestTrust/)=true OR match_regex(cmd_line, /(?i)Get-NetForestTrust/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1199 | Trusted Relationship | Initial Access |
| T1482 | Domain Trust Discovery | Discovery |
| T1590 | Gather Victim Network Information | Reconnaissance |
| T1591 | Gather Victim Org Information | Reconnaissance |
| T1595 | Active Scanning | Reconnaissance |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance and Access to Computers and Domains via PowerSploit modules
This detection identifies access to PowerSploit modules that discover computers, servers and domains that can be accessed or taken over.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1592](https://attack.mitre.org/techniques/T1592/), [T1590](https://attack.mitre.org/techniques/T1590/), [T1087](https://attack.mitre.org/techniques/T1087/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-ComputerDetail/)=true OR match_regex(cmd_line, /(?i)Get-Domain/)=true OR match_regex(cmd_line, /(?i)Get-NetDomain/)=true OR match_regex(cmd_line, /(?i)Get-DomainComputer/)=true OR match_regex(cmd_line, /(?i)Get-NetComputer/)=true OR match_regex(cmd_line, /(?i)Get-DomainController/)=true OR match_regex(cmd_line, /(?i)Get-NetDomainController/)=true OR match_regex(cmd_line, /(?i)Get-DomainFileServer/)=true OR match_regex(cmd_line, /(?i)Get-NetFileServer/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1592 | Gather Victim Host Information | Reconnaissance |
| T1590 | Gather Victim Network Information | Reconnaissance |
| T1087 | Account Discovery | Discovery |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance and Access to Computers via Mimikatz modules
This detection identifies use of Mimikatz modules for discovery of computers and servers and access to them.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1592](https://attack.mitre.org/techniques/T1592/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::ServerInfo/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1592 | Gather Victim Host Information | Reconnaissance |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance and Access to Operating System Elements via PowerSploit modules
This detection identifies access to PowerSploit modules that discover and access operating system elements, such as processes, services, registry locations, security packages and files.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1007](https://attack.mitre.org/techniques/T1007/), [T1012](https://attack.mitre.org/techniques/T1012/), [T1046](https://attack.mitre.org/techniques/T1046/), [T1047](https://attack.mitre.org/techniques/T1047/), [T1057](https://attack.mitre.org/techniques/T1057/), [T1083](https://attack.mitre.org/techniques/T1083/), [T1518](https://attack.mitre.org/techniques/T1518/), [T1592.002](https://attack.mitre.org/techniques/T1592/002/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainProcess/)=true OR match_regex(cmd_line, /(?i)Invoke-ProcessHunter/)=true OR match_regex(cmd_line, /(?i)Get-ServiceDetail/)=true OR match_regex(cmd_line, /(?i)Get-WMIProcess/)=true OR match_regex(cmd_line, /(?i)Get-NetProcess/)=true OR match_regex(cmd_line, /(?i)Get-SecurityPackage/)=true OR match_regex(cmd_line, /(?i)Find-DomainObjectPropertyOutlier/)=true OR match_regex(cmd_line, /(?i)Get-DomainObject/)=true OR match_regex(cmd_line, /(?i)Get-ADObject/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegMountedDrive/)=true OR match_regex(cmd_line, /(?i)Get-RegistryMountedDrive/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1007 | System Service Discovery | Discovery |
| T1012 | Query Registry | Discovery |
| T1046 | Network Service Scanning | Discovery |
| T1047 | Windows Management Instrumentation | Execution |
| T1057 | Process Discovery | Discovery |
| T1083 | File and Directory Discovery | Discovery |
| T1518 | Software Discovery | Discovery |
| T1592.002 | Software | Reconnaissance |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance and Access to Processes and Services via Mimikatz modules
This detection identifies use of Mimikatz modules for discovery and access to services and processes.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1007](https://attack.mitre.org/techniques/T1007/), [T1046](https://attack.mitre.org/techniques/T1046/), [T1057](https://attack.mitre.org/techniques/T1057/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)process::list/)=true OR match_regex(cmd_line, /(?i)service::list/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1007 | System Service Discovery | Discovery |
| T1046 | Network Service Scanning | Discovery |
| T1057 | Process Discovery | Discovery |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance and Access to Shared Resources via Mimikatz modules
This detection identifies use of Mimikatz modules for discovery and access to network shares.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021/002/), [T1135](https://attack.mitre.org/techniques/T1135/), [T1039](https://attack.mitre.org/techniques/T1039/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)net::share/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.002 | SMB/Windows Admin Shares | Lateral Movement |
| T1135 | Network Share Discovery | Discovery |
| T1039 | Data from Network Shared Drive | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance and Access to Shared Resources via PowerSploit modules
This detection identifies access to PowerSploit modules that discover and access network and distributed file system shares.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021/002/), [T1135](https://attack.mitre.org/techniques/T1135/), [T1039](https://attack.mitre.org/techniques/T1039/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-DomainShare/)=true OR match_regex(cmd_line, /(?i)Invoke-ShareFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainShareFile/)=true OR match_regex(cmd_line, /(?i)Invoke-FileFinder/)=true OR match_regex(cmd_line, /(?i)Find-InterestingFile/)=true OR match_regex(cmd_line, /(?i)Get-DomainDFSShare/)=true OR match_regex(cmd_line, /(?i)Get-DFSshare/)=true OR match_regex(cmd_line, /(?i)Get-NetShare/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.002 | SMB/Windows Admin Shares | Lateral Movement |
| T1135 | Network Share Discovery | Discovery |
| T1039 | Data from Network Shared Drive | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance of Access and Persistence Opportunities via PowerSploit modules
This detection identifies use of PowerSploit modules that discover opportunities for malicious access and persistence. Some examples include access to admin accounts, weak access control policies, landing paths for dropping malicious software or data to exfiltrate, registry locations to land autorun parameters, task scheduling opportunities, as well as services and system files that can be compromised.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1053](https://attack.mitre.org/techniques/T1053/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1543](https://attack.mitre.org/techniques/T1543/), [T1547](https://attack.mitre.org/techniques/T1547/), [T1574](https://attack.mitre.org/techniques/T1574/)
- **Last Updated**: 2020-11-05

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-LocalAdminAccess/)=true OR match_regex(cmd_line, /(?i)Find-InterestingDomainAcl/)=true OR match_regex(cmd_line, /(?i)Invoke-ACLScanner/)=true OR match_regex(cmd_line, /(?i)Find-PathDLLHijack/)=true OR match_regex(cmd_line, /(?i)Find-ProcessDLLHijack/)=true OR match_regex(cmd_line, /(?i)Get-DomainObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-ObjectAcl/)=true OR match_regex(cmd_line, /(?i)Get-DomainPolicy/)=true OR match_regex(cmd_line, /(?i)Get-ModifiablePath/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableRegistryAutoRun/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableScheduledTaskFile/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableService/)=true OR match_regex(cmd_line, /(?i)Get-ModifiableServiceFile/)=true OR match_regex(cmd_line, /(?i)Get-PathAcl/)=true OR match_regex(cmd_line, /(?i)Get-UnattendedInstallFile/)=true OR match_regex(cmd_line, /(?i)Get-UnquotedService/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation |
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1543 | Create or Modify System Process | Persistence, Privilege Escalation |
| T1547 | Boot or Logon Autostart Execution | Persistence, Privilege Escalation |
| T1574 | Hijack Execution Flow | Persistence, Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance of Connectivity via PowerSploit modules
This detection identifies access to PowerSploit modules for reconnaissance of connectivity.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021/002/), [T1135](https://attack.mitre.org/techniques/T1135/), [T1039](https://attack.mitre.org/techniques/T1039/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Get-DomainDNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DNSRecord/)=true OR match_regex(cmd_line, /(?i)Get-DomainDNSZone/)=true OR match_regex(cmd_line, /(?i)Get-DNSZone/)=true OR match_regex(cmd_line, /(?i)Invoke-ReverseDnsLookup/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegCachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-CachedRDPConnection/)=true OR match_regex(cmd_line, /(?i)Get-WMIRegProxy/)=true OR match_regex(cmd_line, /(?i)Get-Proxy/)=true OR match_regex(cmd_line, /(?i)Invoke-Portscan/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.002 | SMB/Windows Admin Shares | Lateral Movement |
| T1135 | Network Share Discovery | Discovery |
| T1039 | Data from Network Shared Drive | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance of Credential Stores and Services via Mimikatz modules
This detection identifies reconnaissance of credential stores and use of CryptoAPI services by Mimikatz modules.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1589.001](https://attack.mitre.org/techniques/T1589/001/), [T1590.001](https://attack.mitre.org/techniques/T1590/001/), [T1590.003](https://attack.mitre.org/techniques/T1590/003/), [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/)
- **Last Updated**: 2020-11-03

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)crypto::capi/)=true OR match_regex(cmd_line, /(?i)crypto::cng/)=true OR match_regex(cmd_line, /(?i)crypto::providers/)=true OR match_regex(cmd_line, /(?i)crypto::stores/)=true OR match_regex(cmd_line, /(?i)crypto::sc/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1589.001 | Credentials | Reconnaissance |
| T1590.001 | Domain Properties | Reconnaissance |
| T1590.003 | Network Trust Dependencies | Reconnaissance |
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1098 | Account Manipulation | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance of Defensive Tools via PowerSploit modules
This detection identifies use of PowerSploit modules for assessment of presence of defensive tools.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1595.002](https://attack.mitre.org/techniques/T1595/002/), [T1592.002](https://attack.mitre.org/techniques/T1592/002/)
- **Last Updated**: 2020-11-05

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Find-AVSignature/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1595.002 | Vulnerability Scanning | Reconnaissance |
| T1592.002 | Software | Reconnaissance |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance of Privilege Escalation Opportunities via PowerSploit modules
This detection identifies use of PowerSploit modules for assessment of privilege escalation opportunities.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/)
- **Last Updated**: 2020-11-05

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Invoke-PrivescAudit/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1098 | Account Manipulation | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset


_version_: 1
</details>

---

### Reconnaissance of Process or Service Hijacking Opportunities via Mimikatz modules
This detection identifies use of Mimikatz modules for discovery of process or service hijacking opportunities via Microsoft Detours compatibility. Microsoft Detours is an open source library for intercepting, monitoring and instrumenting binary functions on Microsoft Windows. Detours intercepts Win32 functions by re-writing the in-memory code for target functions. The Detours package also contains utilities to attach arbitrary DLLs and data segments called payloads to any Win32 binary.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1543](https://attack.mitre.org/techniques/T1543/), [T1055](https://attack.mitre.org/techniques/T1055/), [T1574](https://attack.mitre.org/techniques/T1574/)
- **Last Updated**: 2020-11-05

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::detours/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Discovery Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* _time

* process

* dest_device_id

* dest_user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1543 | Create or Modify System Process | Persistence, Privilege Escalation |
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |
| T1574 | Hijack Execution Flow | Persistence, Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz

* https://en.wikipedia.org/wiki/Microsoft_Detours



#### Test Dataset


_version_: 1
</details>

---

### Recursive Delete of Directory In Batch CMD
This search is to detect a suspicious commandline designed to delete files or directory recursive using batch command. This technique was seen in ransomware (reddot) where it it tries to delete the files in recycle bin to impaire user from recovering deleted files.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1070.004](https://attack.mitre.org/techniques/T1070/004/)
- **Last Updated**: 2021-06-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe Processes.process=*/c*  Processes.process=* rd * Processes.process="*/s*" Processes.process="*/q*" by Processes.user Processes.process_name Processes.parent_process_name Processes.parent_process Processes.process Processes.process_id Processes.dest 
|`drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `recursive_delete_of_directory_in_batch_cmd_filter`
```
#### Associated Analytic Story

* Ransomware


#### 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.

#### Required field

* _time

* Processes.parent_process_name

* Processes.process_name

* Processes.process

* Processes.parent_process

* Processes.process_id

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070.004 | File Deletion | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network operator may use this batch command to delete recursively a directory or files within directory

#### Reference


* https://app.any.run/tasks/c0f98850-af65-4352-9746-fbebadee4f05/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data2/windows-sysmon.log


_version_: 1
</details>

---

### Reg exe Manipulating Windows Services Registry Keys
The search looks for reg.exe modifying registry keys that define Windows services and their configurations.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1574.011](https://attack.mitre.org/techniques/T1574/011/)
- **Last Updated**: 2020-11-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name values(Processes.user) as user FROM datamodel=Endpoint.Processes where Processes.process_name=reg.exe Processes.process=*reg* Processes.process=*add* Processes.process=*Services* by Processes.process_id Processes.dest Processes.process 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `reg_exe_manipulating_windows_services_registry_keys_filter`
```
#### Associated Analytic Story

* Windows Service Abuse

* Windows Persistence Techniques


#### 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 product, 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.

#### Required field

* _time

* Processes.process_name

* Processes.parent_process_name

* Processes.user

* Processes.process

* Processes.process_id

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1574.011 | Services Registry Permissions Weakness | Persistence, Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Installation


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1574.011/change_registry_path_service/windows-sysmon.log


_version_: 5
</details>

---

### Registry Keys Used For Persistence
The search looks for modifications to registry keys that can be used to launch an application or service at system startup.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1547.001](https://attack.mitre.org/techniques/T1547/001/)
- **Last Updated**: 2020-11-27

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest Registry.user 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `registry_keys_used_for_persistence_filter`
```
#### Associated Analytic Story

* Suspicious Windows Registry Activities

* Suspicious MSHTA Activity

* DHS Report TA18-074A

* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

* Ransomware

* Windows Persistence Techniques

* Emotet Malware  DHS Report TA18-201A 

* Icedid


#### 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 product, 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.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.dest

* Registry.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.001/atomic_red_team/windows-sysmon.log


_version_: 5
</details>

---

### Registry Keys Used For Privilege Escalation
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1546.012](https://attack.mitre.org/techniques/T1546/012/)
- **Last Updated**: 2020-11-27

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") AND (Registry.registry_key_name=GlobalFlag OR Registry.registry_key_name=Debugger) by Registry.dest  Registry.user Registry.registry_path Registry.registry_key_name 
| `security_content_ctime(lastTime)`  
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `registry_keys_used_for_privilege_escalation_filter`
```
#### Associated Analytic Story

* Windows Privilege Escalation

* Suspicious Windows Registry Activities

* Cloud Federated Credential Abuse


#### 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 product, 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.

#### Required field

* _time

* Registry.registry_path

* Registry.registry_key_name

* Registry.dest

* Registry.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1546.012 | Image File Execution Options Injection | Privilege Escalation, Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
There are many legitimate applications that must execute upon system startup and will use these registry keys to accomplish that task.

#### Reference


* https://blog.malwarebytes.com/101/2015/12/an-introduction-to-image-file-execution-options/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.012/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Registry Keys for Creating SHIM Databases
This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1546.011](https://attack.mitre.org/techniques/T1546/011/)
- **Last Updated**: 2020-11-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path=*CurrentVersion\\AppCompatFlags\\Custom* OR Registry.registry_path=*CurrentVersion\\AppCompatFlags\\InstalledSDB* by Registry.dest Registry.user 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `registry_keys_for_creating_shim_databases_filter`
```
#### Associated Analytic Story

* Suspicious Windows Registry Activities

* Windows Persistence Techniques


#### 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 product, 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.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_path

* Registry.dest

* Registry.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1546.011 | Application Shimming | Privilege Escalation, Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Remote Desktop Network Bruteforce
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port 
| eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50 
| where count>(avg + stdev*2) 
| rename All_Traffic.src AS src All_Traffic.dest AS dest 
| table firstTime lastTime src dest count avg p50 stdev 
| `remote_desktop_network_bruteforce_filter`
```
#### Associated Analytic Story

* SamSam Ransomware

* Ryuk Ransomware


#### How To Implement
You must ensure that your network traffic data is populating the Network_Traffic data model.

#### Required field

* _time

* All_Traffic.app

* All_Traffic.src

* All_Traffic.dest

* All_Traffic.dest_port



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.001 | Remote Desktop Protocol | Lateral Movement |


#### Kill Chain Phase

* Reconnaissance

* Delivery


#### Known False Positives
RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Remote Desktop Network Traffic
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 will ignore common RDP sources and common RDP destinations so you can focus on the uncommon uses of remote desktop on your network.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021/001/)
- **Last Updated**: 2020-07-07

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port 
| `drop_dm_object_name("All_Traffic")` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `remote_desktop_network_traffic_filter` 
```
#### Associated Analytic Story

* SamSam Ransomware

* Ryuk Ransomware

* Hidden Cobra Malware

* Lateral Movement


#### 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.

#### Required field

* _time

* All_Traffic.dest_port

* All_Traffic.dest_category

* All_Traffic.src_category

* All_Traffic.src

* All_Traffic.dest

* All_Traffic.dest_port



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.001 | Remote Desktop Protocol | Lateral Movement |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Remote Desktop may be used legitimately by users on the network.

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Remote Desktop Process Running On System
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1021.001](https://attack.mitre.org/techniques/T1021/001/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=*mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name(Processes)` 
| `remote_desktop_process_running_on_system_filter` 
```
#### Associated Analytic Story

* Hidden Cobra Malware

* Lateral Movement


#### 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`.

#### Required field

* _time

* Processes.process

* Processes.dest_category

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.001 | Remote Desktop Protocol | Lateral Movement |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Remote Desktop may be used legitimately by users on the network.

#### Reference


#### Test Dataset


_version_: 5
</details>

---

### Remote Process Instantiation via WMI
This search looks for wmic.exe being launched with parameters to spawn a process on a remote system.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/)
- **Last Updated**: 2020-11-30

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wmic.exe Processes.process="*/node*" Processes.process="*process*" Processes.process="*call*" Processes.process="*create*"   by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `remote_process_instantiation_via_wmi_filter`
```
#### Associated Analytic Story

* Ransomware

* Suspicious WMI Use


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1047 | Windows Management Instrumentation | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log


_version_: 5
</details>

---

### Remote WMI Command Attempt
The following analytic identifies usage of `wmic.exe` spawning a local or remote process, identified by the `node` switch. During triage, review parallel processes for additional commands executed. Look for any file modifications before and after `wmic.exe` execution. In addition, identify the remote endpoint and confirm execution or file modifications. Contain and isolate the endpoint as needed.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/)
- **Last Updated**: 2018-12-03

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe Processes.process=*node* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `remote_wmi_command_attempt_filter`
```
#### Associated Analytic Story

* Suspicious WMI Use


#### 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. Deprecated because duplicate of Remote Process Instantiation via WMI.

#### Required field

* _time

* Processes.user

* Processes.process_name

* Processes.parent_process_name

* Processes.dest

* Processes.parent_process

* Processes.parent_process_id

* Processes.process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1047 | Windows Management Instrumentation | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Administrators may use this legitimately to gather info from remote systems. Filter as needed.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1047/T1047.yaml



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Resize ShadowStorage volume
The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum size as much as possible

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/)
- **Last Updated**: 2021-03-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as cmdline values(Processes.parent_process_name) as parent_process values(Processes.process_name) as process_name min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "cmd.exe" OR Processes.parent_process_name = "powershell.exe" OR Processes.parent_process_name = "powershell_ise.exe" OR Processes.parent_process_name =  "wmic.exe" Processes.process_name = "vssadmin.exe" Processes.process="*resize*" Processes.process="*shadowstorage*" Processes.process="*/maxsize*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `resize_shadowstorage_volume_filter`
```
#### Associated Analytic Story

* Clop Ransomware


#### 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.

#### Required field

* Processes.process

* Process.parent_process_name

* _time

* Processes.process_name

* Processes.parent_process

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1490 | Inhibit System Recovery | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network admin can resize the shadowstorage for valid purposes.

#### Reference


* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/clop/clop_a/windows-sysmon.log


_version_: 1
</details>

---

### Resize Shadowstorage Volume
The following analytics identifies the resizing of shadowstorage by ransomware malware to avoid the shadow volumes being made again. this technique is an alternative by ransomware attacker than deleting the shadowstorage which is known alert in defensive team. one example of ransomware that use this technique is CLOP ransomware where it drops a .bat file that will resize the shadowstorage to minimum size as much as possible

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: Endpoint
- **ATT&CK**: [T1489](https://attack.mitre.org/techniques/T1489/)
- **Last Updated**: 2021-06-21

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=lower(ucast(map_get(input_event, "process"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line IS NOT NULL AND like(cmd_line, "%resize%") AND like(cmd_line, "%shadowstorage%") AND like(cmd_line, "%maxsize%") AND process_name="vssadmin.exe" 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name, "process_path", process_path]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Clop Ransomware

* Ransomware


#### 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.

#### Required field

* _time

* dest_device_id

* process_name

* parent_process_name

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1489 | Service Stop | Impact |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network admin can resize the shadowstorage for valid purposes.

#### Reference


* https://www.fireeye.com/blog/threat-research/2020/10/fin11-email-campaigns-precursor-for-ransomware-data-theft.html

* https://blog.virustotal.com/2020/11/keep-your-friends-close-keep-ransomware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/ssa_data1/windows-security.log


_version_: 1
</details>

---

### Revil Common Exec Parameter
This analytic identifies suspicious commandline parameter that are commonly used by REVIL ransomware to encrypts the compromise machine.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1204](https://attack.mitre.org/techniques/T1204/)
- **Last Updated**: 2021-06-02

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "* -nolan *" OR Processes.process = "* -nolocal *" OR Processes.process = "* -fast *" OR Processes.process = "* -full *" by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `revil_common_exec_parameter_filter`
```
#### Associated Analytic Story

* Ransomware

* Revil Ransomware


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.parent_process

* Processes.dest

* Processes.user

* Processes.process_id

* Processes.process_guid



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1204 | User Execution | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
third party tool may have same command line parameters as revil ransomware.

#### Reference


* https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/

* https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log


_version_: 2
</details>

---

### Revil Registry Entry
This analytic identifies suspicious modification in registry entry to keep some malware data during its infection. This technique seen in several apt implant, malware and ransomware like REVIL where it keep some information like the random generated file extension it uses for all the encrypted files and ransomware notes file name in the compromised host.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1112](https://attack.mitre.org/techniques/T1112/)
- **Last Updated**: 2021-06-02

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\Facebook_Assistant\\*" OR Registry.registry_path="*\\SOFTWARE\\WOW6432Node\\BlackLivesMatter*") AND (Registry.registry_value_name = "\.*" OR Registry.registry_value_name = "Binary Data") by Registry.registry_value_name Registry.dest Registry.user 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name(Registry)` 
| `revil_registry_entry_filter`
```
#### Associated Analytic Story

* Ransomware

* Revil Ransomware


#### How To Implement
to successfully implement this search, you need to be ingesting logs with the Image, TargetObject registry key, registry Details from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* Registry.dest

* Registry.user

* Registry.registry_value_name

* Registry.registry_path

* Registry.registry_key_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1112 | Modify Registry | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/

* https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf1/windows-sysmon.log


_version_: 1
</details>

---

### RunDLL Loading DLL By Ordinal
This search looks for executing scripts with rundll32. Adversaries may abuse rundll32.exe to proxy execution of malicious code. Using rundll32.exe, vice executing directly, may avoid triggering security tools that may not monitor execution of the rundll32.exe process because of allowlists or false positives from normal operations.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2020-11-30

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = rundll32.exe by Processes.process_name Processes.parent_process_name Processes.process Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `rundll_loading_dll_by_ordinal_filter`
```
#### Associated Analytic Story

* Unusual Processes


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.parent_process_name

* Processes.process

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Installation


#### Known False Positives
While not common, loading a DLL under %AppData% and calling a function by ordinal is possible by a legitimate process

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Rundll32 Create Remote Thread To A Process
This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to cmd.exe process . This technique was seen in IcedID malware to execute its malicious code in normal process for defense evasion and to steal sensitive information the the compromised host. browser process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-07-29

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage = "*.exe" 
| stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId StartAddress EventCode Computer 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `rundll32_create_remote_thread_to_a_process_filter`
```
#### Associated Analytic Story

* Icedid


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* SourceImage

* TargetImage

* TargetProcessId

* SourceProcessId

* StartAddress

* EventCode

* Computer



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.joesandbox.com/analysis/380662/0/html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Rundll32 CreateRemoteThread In Browser
This analytic identifies the suspicious Remote Thread execution of rundll32.exe process to "firefox.exe" and "chrome.exe" browser. This technique was seen in IcedID malware where it hooks the browser to parse banking information as user used the targetted browser process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-07-26

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=8 SourceImage = "*\\rundll32.exe" TargetImage IN ("*\\firefox.exe", "*\\chrome.exe", "*\\iexplore.exe","*\\microsoftedgecp.exe") 
| stats count min(_time) as firstTime max(_time) as lastTime by SourceImage TargetImage TargetProcessId SourceProcessId StartAddress EventCode Computer 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `rundll32_createremotethread_in_browser_filter`
```
#### Associated Analytic Story

* Icedid


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the SourceImage, TargetImage, and EventCode executions from your endpoints related to create remote thread or injecting codes. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* SourceImage

* TargetImage

* TargetProcessId

* SourceProcessId

* StartAddress

* EventCode

* Computer



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.joesandbox.com/analysis/380662/0/html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Rundll32 DNSQuery
This search is to detect a suspicious rundll32.exe process having a http connection and do a dns query in some web domain. This technique was seen in IcedID malware where the rundll32 that execute its payload will contact amazon.com to check internet connect and to communicate to its C&C server to download config and other file component.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-07-26

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=22 process_name="rundll32.exe" 
| stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus ProcessId direction Computer 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `rundll32_dnsquery_filter`
```
#### Associated Analytic Story

* Icedid


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name and eventcode = 22 dnsquery executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.

#### Required field

* _time

* Image

* QueryName

* QueryStatus

* ProcessId

* direction

* Computer



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://any.run/malware-trends/icedid



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Rundll32 Process Creating Exe Dll Files
This search is to detect a suspicious rundll32 process that drops executable (.exe or .dll) files. this behavior seen in rundll32 process of IcedID that tries to drop copy of itself in temp folder or download executable drop it either appdata or programdata as part of its execution.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-07-26

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=11 process_name="rundll32.exe" TargetFilename IN ("*.exe", "*.dll",) 
| stats count min(_time) as firstTime max(_time) as lastTime by Image TargetFilename ProcessGuid dest user_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `rundll32_process_creating_exe_dll_files_filter`
```
#### Associated Analytic Story

* Icedid


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name, TargetFilename, and eventcode 11 executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances where renamed rundll32.exe may be used.

#### Required field

* _time

* Image

* TargetFilename

* ProcessGuid

* dest

* user_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://any.run/malware-trends/icedid



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Rundll32 with no Command Line Arguments with Network
The following analytic identifies rundll32.exe with no command line arguments and performing a network connection. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, triage any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-04-19

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| regex process="(rundll32\.exe.{0,4}$)" 
| join  process_id [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port
| `drop_dm_object_name(Ports)` 
| rename  dest as connection_to_CNC] 
| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port 
| `rundll32_with_no_command_line_arguments_with_network_filter`
```
#### Associated Analytic Story

* Suspicious Rundll32 Activity

* Cobalt Strike

* PrintNightmare CVE-2021-34527


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `port` node.

#### Required field

* _time

* EventID

* process_name

* process_id

* parent_process_name

* dest_port

* process_path

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.

#### Reference


* https://attack.mitre.org/techniques/T1218/011/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

* https://lolbas-project.github.io/lolbas/Binaries/Rundll32

* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log


_version_: 1
</details>

---

### Ryuk Test Files Detected
The search looks for files that contain the key word *Ryuk* under any folder in the C drive, which is consistent with Ryuk propagation.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem WHERE "Filesystem.file_path"=C:\\*Ryuk* BY "Filesystem.dest", "Filesystem.user", "Filesystem.file_path" 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `ryuk_test_files_detected_filter`
```
#### Associated Analytic Story

* Ryuk Ransomware


#### How To Implement
You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint Filesystem data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

#### Required field

* _time

* Filesystem.file_path

* Filesystem.dest

* Filesystem.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1486 | Data Encrypted for Impact | Impact |


#### Kill Chain Phase

* Delivery


#### Known False Positives
If there are files with this keywoord as file names it might trigger false possitives, please make use of our filters to tune out potential FPs.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log


_version_: 1
</details>

---

### Ryuk Wake on LAN Command
This Splunk query identifies the use of Wake-on-LAN utilized by Ryuk ransomware. The Ryuk Ransomware uses the Wake-on-Lan feature to turn on powered off devices on a compromised network to have greater success encrypting them. This is a high fidelity indicator of Ryuk ransomware executing on an endpoint. Upon triage, isolate the endpoint. Additional file modification events will be within the users profile (\appdata\roaming) and in public directories (users\public\). Review all Scheduled Tasks on the isolated endpoint and across the fleet. Suspicious Scheduled Tasks will include a path to a unknown binary and those endpoints should be isolated until triaged.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.003](https://attack.mitre.org/techniques/T1059/003/)
- **Last Updated**: 2021-03-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*8 LAN*" OR Processes.process="*9 REP*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `ryuk_wake_on_lan_command_filter`
```
#### Associated Analytic Story

* Ryuk Ransomware


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.003 | Windows Command Shell | Execution |


#### Kill Chain Phase

* Exploitation

* Lateral Movement


#### Known False Positives
Limited to no known false positives.

#### Reference


* https://www.bleepingcomputer.com/news/security/ryuk-ransomware-uses-wake-on-lan-to-encrypt-offline-devices/

* https://www.bleepingcomputer.com/news/security/ryuk-ransomware-now-self-spreads-to-other-windows-lan-devices/

* https://www.cert.ssi.gouv.fr/uploads/CERTFR-2021-CTI-006.pdf



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.003/ryuk/windows-sysmon.log


_version_: 1
</details>

---

### SAM Database File Access Attempt
The following analytic identifies access to SAM, SYSTEM or SECURITY databases' within the file path of `windows\system32\config` using Windows Security EventCode 4663. This particular behavior is related to credential access, an attempt to either use a Shadow Copy or recent CVE-2021-36934 to access the SAM database. The Security Account Manager (SAM) is a database file in Windows XP, Windows Vista, Windows 7, 8.1 and 10 that stores users' passwords.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.002](https://attack.mitre.org/techniques/T1003/002/)
- **Last Updated**: 2021-07-23

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` (EventCode=4663)  process_name!=*\\dllhost.exe Object_Name IN ("*\\Windows\\System32\\config\\SAM*","*\\Windows\\System32\\config\\SYSTEM*","*\\Windows\\System32\\config\\SECURITY*") 
| stats values(Accesses) count by process_name Object_Name  dest user 
| `sam_database_file_access_attempt_filter`
```
#### Associated Analytic Story

* Credential Dumping


#### How To Implement
To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663. For 4663, enable "Audit Object Access" in Group Policy. Then check the two boxes listed for both "Success" and "Failure."

#### Required field

* _time

* process_name

* Object_Name

* dest

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.002 | Security Account Manager | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Natively, `dllhost.exe` will access the files. Every environment will have additional native processes that do as well. Filter by process_name. As an aside, one can remove process_name entirely and add `Object_Name=*ShadowCopy*`.

#### Reference


* https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4663

* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4663

* https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-36934

* https://github.com/GossiTheDog/HiveNightmare

* https://github.com/JumpsecLabs/Guidance-Advice/tree/main/SAM_Permissions

* https://en.wikipedia.org/wiki/Security_Account_Manager



#### Test Dataset


_version_: 1
</details>

---

### SLUI RunAs Elevated
The following analytic identifies the Microsoft Software Licensing User Interface Tool, `slui.exe`, elevating access using the `-verb runas` function. This particular bypass utilizes a registry key/value. Identified by two sources, the registry keys are `HKCU\Software\Classes\exefile\shell` and `HKCU\Software\Classes\launcher.Systemsettings\Shell\open\command`. To simulate this behavior, multiple POC are available. The analytic identifies the use of `runas` by `slui.exe`.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548/002/)
- **Last Updated**: 2021-05-13

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=slui.exe (Processes.process=*-verb* Processes.process=*runas*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `slui_runas_elevated_filter`
```
#### Associated Analytic Story

* DarkSide Ransomware

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548.002 | Bypass User Account Control | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives should be present as this is not commonly used by legitimate applications.

#### Reference


* https://www.exploit-db.com/exploits/46998

* https://medium.com/@mattharr0ey/privilege-escalation-uac-bypass-in-changepk-c40b92818d1b

* https://gist.github.com/r00t-3xp10it/0c92cd554d3156fd74f6c25660ccc466

* https://www.rapid7.com/db/modules/exploit/windows/local/bypassuac_sluihijack/

* https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log


_version_: 1
</details>

---

### SLUI Spawning a Process
The following analytic identifies the Microsoft Software Licensing User Interface Tool, `slui.exe`, spawning a child process. This behavior is associated with publicly known UAC bypass. `slui.exe` is commonly associated with software updates and is most often spawned by `svchost.exe`. The `slui.exe` process should not have child processes, and any processes spawning from it will be running with elevated privileges. During triage, review the child process and additional parallel processes. Identify any file modifications that may have lead to the bypass.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548/002/)
- **Last Updated**: 2021-05-13

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=slui.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `slui_spawning_a_process_filter`
```
#### Associated Analytic Story

* DarkSide Ransomware

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548.002 | Bypass User Account Control | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Certain applications may spawn from `slui.exe` that are legitimate. Filtering will be needed to ensure proper monitoring.

#### Reference


* https://www.exploit-db.com/exploits/46998

* https://www.rapid7.com/db/modules/exploit/windows/local/bypassuac_sluihijack/

* https://www.fireeye.com/blog/threat-research/2021/05/shining-a-light-on-darkside-ransomware-operations.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548.002/slui/windows-sysmon.log


_version_: 1
</details>

---

### SMB Traffic Spike
This search looks for spikes in the number of Server Message Block (SMB) traffic connections.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021/002/)
- **Last Updated**: 2020-07-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src 
| `drop_dm_object_name("All_Traffic")` 
| eventstats max(_time) as maxtime 
| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as stdev by src 
| eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound AND num_data_samples >=50, 1, 0) 
| where isOutlier=1 
| table src count 
| `smb_traffic_spike_filter` 
```
#### Associated Analytic Story

* Emotet Malware  DHS Report TA18-201A 

* Hidden Cobra Malware

* Ransomware

* DHS Report TA18-074A


#### How To Implement
This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model.

#### Required field

* _time

* All_Traffic.dest_port

* All_Traffic.app

* All_Traffic.src



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.002 | SMB/Windows Admin Shares | Lateral Movement |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
A file server may experience high-demand loads that could cause this analytic to trigger.

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### SMB Traffic Spike - MLTK
This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021/002/)
- **Last Updated**: 2020-07-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src 
| eval HourOfDay=strftime(_time, "%H") 
| eval DayOfWeek=strftime(_time, "%A") 
| `drop_dm_object_name(All_Traffic)` 
| apply smb_pdfmodel threshold=0.001 
| rename "IsOutlier(count)" as isOutlier 
| search isOutlier > 0 
| sort -count 
| table _time src dest port count 
| `smb_traffic_spike___mltk_filter` 
```
#### Associated Analytic Story

* Emotet Malware  DHS Report TA18-201A 

* Hidden Cobra Malware

* Ransomware

* DHS Report TA18-074A


#### How To Implement
To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model. In addition, the Machine Learning Toolkit (MLTK) version 4.2 or greater must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.\
This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \
1. **Label:** Number of events, **Field:** count\
Detailed documentation on how to create a new field within Incident Review is found here: `https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details`

#### Required field

* _time

* All_Traffic.dest_ip

* All_Traffic.dest_port

* All_Traffic.app

* All_Traffic.src



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.002 | SMB/Windows Admin Shares | Lateral Movement |


#### Kill Chain Phase

* Actions on Objectives


#### 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

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### SQL Injection with Long URLs
This search looks for long URLs that have several SQL commands visible within them.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Web
- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent 
| `drop_dm_object_name("Web")` 
| eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 
| where num_sql_cmds > 3 
| `sql_injection_with_long_urls_filter`
```
#### Associated Analytic Story

* SQL Injection


#### 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.

#### Required field

* _time

* Web.dest_category

* Web.url_length

* Web.http_user_agent_length

* Web.src

* Web.dest

* Web.url

* Web.http_user_agent



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1190 | Exploit Public-Facing Application | Initial Access |


#### Kill Chain Phase

* Delivery


#### 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.

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Samsam Test File Write
The search looks for a file named "test.txt" written to the windows system directory tree, which is consistent with Samsam propagation.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1486](https://attack.mitre.org/techniques/T1486/)
- **Last Updated**: 2018-12-14

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `samsam_test_file_write_filter`
```
#### Associated Analytic Story

* SamSam Ransomware


#### 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.

#### Required field

* _time

* Filesystem.user

* Filesystem.dest

* Filesystem.file_name

* Filesystem.file_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1486 | Data Encrypted for Impact | Impact |


#### Kill Chain Phase

* Delivery


#### Known False Positives
No false positives have been identified.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1486/sam_sam_note/windows-sysmon.log


_version_: 1
</details>

---

### Sc exe Manipulating Windows Services
This search looks for arguments to sc.exe indicating the creation or modification of a Windows service.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1543.003](https://attack.mitre.org/techniques/T1543/003/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sc.exe (Processes.process="* create *" OR Processes.process="* config *") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `sc_exe_manipulating_windows_services_filter`
```
#### Associated Analytic Story

* Windows Service Abuse

* DHS Report TA18-074A

* Orangeworm Attack Group

* Windows Persistence Techniques

* Disabling Security Tools

* NOBELIUM Group


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1543.003 | Windows Service | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Installation


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543.003/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Schedule Task with HTTP Command Arguments
The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with an arguments "HTTP" string that are unique entry of malware or attack that uses lolbin to download other file or payload to the infected machine. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1053](https://attack.mitre.org/techniques/T1053/)
- **Last Updated**: 2021-04-19

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4698 
| xmlkv Message
| search Arguments IN ("*http*") 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `schedule_task_with_http_command_arguments_filter`
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.

#### Required field

* _time

* dest

* Task_Name

* Command

* Author

* Enabled

* Hidden

* Arguments



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://app.any.run/tasks/92d7ef61-bfd7-4c92-bc15-322172b4ebec/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/tasksched/windows-security.log


_version_: 1
</details>

---

### Schedule Task with Rundll32 Command Trigger
The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a Rundll32. This technique is common in new trickbot that uses rundll32 to load is trickbot downloader. The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not. schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`. The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory. Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.'

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1053](https://attack.mitre.org/techniques/T1053/)
- **Last Updated**: 2021-04-19

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4698 
| xmlkv Message 
| search Command IN ("*rundll32*") 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden, Arguments 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `schedule_task_with_rundll32_command_trigger_filter`
```
#### Associated Analytic Story

* Windows Persistence Techniques

* Trickbot

* Icedid


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the task schedule (Exa. Security Log EventCode 4698) endpoints. Tune and filter known instances of Task schedule used in your environment.

#### Required field

* _time

* dest

* Task_Name

* Command

* Author

* Enabled

* Hidden

* Arguments



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://labs.vipre.com/trickbot-and-its-modules/

* https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/tasksched/windows-security.log


_version_: 1
</details>

---

### Scheduled Task Deleted Or Created via CMD
This search looks for flags passed to schtasks.exe on the command-line that indicate a task was created via command like. This has been associated with the Dragonfly threat actor, and the SUNBURST attack against Solarwinds.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053/005/)
- **Last Updated**: 2020-12-17

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `scheduled_task_deleted_or_created_via_cmd_filter` 
```
#### Associated Analytic Story

* DHS Report TA18-074A

* NOBELIUM Group


#### 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.

#### Required field

* _time

* Processes.process

* Processes.parent_process

* Processes.process_name

* Processes.user

* Processes.parent_process_name

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Tasks should not be manually created via CLI, this is rarely done by admins as well

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log


_version_: 5
</details>

---

### Schtasks Run Task On Demand
This analytic identifies an on demand run of a Windows Schedule Task through shell or command-line. This technique has been used by adversaries that force to run their created Schedule Task as their persistence mechanism or for lateral movement as part of their malicious attack to the compromised machine.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1053](https://attack.mitre.org/techniques/T1053/)
- **Last Updated**: 2021-05-07

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime  from datamodel=Endpoint.Processes where Processes.process_name = "schtasks.exe" Processes.process = "*/run*" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `schtasks_run_task_on_demand_filter`
```
#### Associated Analytic Story

* XMRig


#### 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. Tune and filter known instances where renamed schtasks.exe may be used.

#### Required field

* _time

* Processes.process

* Processes.process_id

* Processes.process_name

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053 | Scheduled Task/Job | Execution, Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Administrators may use to debug Schedule Task entries. Filter as needed.

#### Reference


* https://thedfirreport.com/2020/04/20/sqlserver-or-the-miner-in-the-basement/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Schtasks scheduling job on remote system
This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053/005/)
- **Last Updated**: 2020-07-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = schtasks.exe Processes.process="*/create*" (Processes.process="* /s *" OR Processes.process="* /S *") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `schtasks_scheduling_job_on_remote_system_filter`
```
#### Associated Analytic Story

* Lateral Movement

* NOBELIUM Group


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Schtasks used for forcing a reboot
This search looks for flags passed to schtasks.exe on the command-line that indicate that a forced reboot of system is scheduled.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053/005/)
- **Last Updated**: 2020-12-07

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe Processes.process="*shutdown*" Processes.process="*/create *" by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `schtasks_used_for_forcing_a_reboot_filter`
```
#### Associated Analytic Story

* Windows Persistence Techniques

* Ransomware


#### 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.

#### Required field

* _time

* Processes.process

* Processes.process_name

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtask_shutdown/windows-sysmon.log


_version_: 4
</details>

---

### Script Execution via WMI
This search looks for scripts launched via WMI.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/)
- **Last Updated**: 2020-03-16

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=scrcons.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `script_execution_via_wmi_filter` 
```
#### Associated Analytic Story

* Suspicious WMI Use


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.user

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1047 | Windows Management Instrumentation | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, administrators may use wmi to launch scripts for legitimate purposes. Filter as needed.

#### Reference


* https://redcanary.com/blog/child-processes/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1047/execution_scrcons/windows-sysmon.log


_version_: 4
</details>

---

### Sdclt UAC Bypass
This search is to detect a suspicious sdclt.exe registry modification. This technique is commonly seen when attacker try to bypassed UAC by using sdclt.exe application by modifying some registry that sdclt.exe tries to open or query with payload file path on it to be executed.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548/002/)
- **Last Updated**: 2021-07-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where (Registry.registry_path= "*\\Windows\\CurrentVersion\\App Paths\\control.exe*" OR Registry.registry_path= "*\\exefile\\shell\\runas\\command\\*") (Registry.registry_key_name = "(Default)" OR Registry.registry_key_name = "IsolatedCommand") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `sdclt_uac_bypass_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Registry.registry_path

* Registry.registry_key_name

* Registry.registry_value_name

* Registry.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548.002 | Bypass User Account Control | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited to no false positives are expected.

#### Reference


* https://enigma0x3.net/2017/03/17/fileless-uac-bypass-using-sdclt-exe/

* https://github.com/hfiref0x/UACME

* https://www.cyborgsecurity.com/cyborg_labs/threat-hunt-deep-dives-user-account-control-bypass-via-registry-modification/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log


_version_: 1
</details>

---

### SearchProtocolHost with no Command Line with Network
The following analytic identifies searchprotocolhost.exe with no command line arguments and with a network connection. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-04-19

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=searchprotocolhost.exe by _time span=1h  Processes.process_id Processes.process_name Processes.dest Processes.process_path Processes.process Processes.parent_process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| regex process="(searchprotocolhost\.exe.{0,4}$)" 
| join  process_id [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Ports where Ports.dest_port !="0" by Ports.process_id Ports.dest Ports.dest_port 
| `drop_dm_object_name(Ports)` 
| rename  dest as connection_to_CNC] 
| table _time dest parent_process_name process_name process_path process process_id connection_to_CNC dest_port 
| `searchprotocolhost_with_no_command_line_with_network_filter`
```
#### Associated Analytic Story

* Cobalt Strike


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` and `ports` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest_port

* process_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives may be present in small environments. Tuning may be required based on parent process.

#### Reference


* https://github.com/fireeye/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon_searchprotocolhost.log


_version_: 1
</details>

---

### SecretDumps Offline NTDS Dumping Tool
This analytic detects a potential usage of secretsdump.py tool for dumping credentials (ntlm hash) from a copy of ntds.dit and SAM.Security,SYSTEM registrry hive. This technique was seen in some attacker that dump ntlm hashes offline after having a copy of ntds.dit and SAM/SYSTEM/SECURITY registry hive.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1003.003](https://attack.mitre.org/techniques/T1003/003/)
- **Last Updated**: 2021-05-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "python*.exe" Processes.process = "*.py*" Processes.process = "*-ntds*" (Processes.process = "*-system*" OR Processes.process = "*-sam*" OR Processes.process = "*-security*" OR Processes.process = "*-bootkey*") by Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.dest Processes.user Processes.process_id Processes.process_guid 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `secretdumps_offline_ntds_dumping_tool_filter`
```
#### Associated Analytic Story

* Credential Dumping


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.parent_process

* Processes.dest

* Processes.user

* Processes.process_id

* Processes.process_guid



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1003.003 | NTDS | Credential Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://github.com/SecureAuthCorp/impacket/blob/master/examples/secretsdump.py



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/casper/datasets1/windows-sysmon.log


_version_: 1
</details>

---

### Services Escalate Exe
The following analytic identifies the use of `svc-exe` with Cobalt Strike. The behavior typically follows after an adversary has already gained initial access and is escalating privileges. Using `svc-exe`, a randomly named binary will be downloaded from the remote Teamserver and placed on disk within `C:\Windows\400619a.exe`. Following, the binary will be added to the registry under key `HKLM\System\CurrentControlSet\Services\400619a\` with multiple keys and values added to look like a legitimate service. Upon loading, `services.exe` will spawn the randomly named binary from `\\127.0.0.1\ADMIN$\400619a.exe`. The process lineage is completed with `400619a.exe` spawning rundll32.exe, which is the default `spawnto_` value for Cobalt Strike. The `spawnto_` value is arbitrary and may be any process on disk (typically system32/syswow64 binary). The `spawnto_` process will also contain a network connection. During triage, review parallel procesess and identify any additional file modifications.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1548](https://attack.mitre.org/techniques/T1548/)
- **Last Updated**: 2021-05-18

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=services.exe Processes.process_path=*admin$* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `services_escalate_exe_filter`
```
#### Associated Analytic Story

* Cobalt Strike


#### How To Implement
To successfully implement this search, you will need to ensure that DNS data is populating the Network_Resolution data model.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548 | Abuse Elevation Control Mechanism | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation

* Privilege Escalation


#### Known False Positives
False positives should be limited as `services.exe` should never spawn a process from `ADMIN$`. Filter as needed.

#### Reference


* https://thedfirreport.com/2021/03/29/sodinokibi-aka-revil-ransomware/

* https://attack.mitre.org/techniques/T1548/

* https://www.cobaltstrike.com/help-beacon



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log


_version_: 1
</details>

---

### Set Default PowerShell Execution Policy To Unrestricted or Bypass
Monitor for changes of the ExecutionPolicy in the registry to the values "unrestricted" or "bypass," which allows the execution of malicious scripts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059.001](https://attack.mitre.org/techniques/T1059/001/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* Registry.registry_key_name=ExecutionPolicy (Registry.registry_value_name=Unrestricted OR Registry.registry_value_name=Bypass) by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
| `set_default_powershell_execution_policy_to_unrestricted_or_bypass_filter`
```
#### Associated Analytic Story

* Malicious PowerShell

* Credential Dumping

* HAFNIUM Group


#### 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.

#### Required field

* _time

* Registry.registry_path

* Registry.registry_key_name

* Registry.registry_value_name

* Registry.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059.001 | PowerShell | Execution |


#### Kill Chain Phase

* Installation

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_execution_policy/windows-sysmon.log


_version_: 6
</details>

---

### Setting Credentials via DSInternals modules
This detection identifies illegal setting of credentials via DSInternals modules.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/)
- **Last Updated**: 2020-11-03

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), cmd_line=ucast(map_get(input_event, "process"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Add-ADDBSidHistory/)=true OR match_regex(cmd_line, /(?i)Add-ADReplNgcKey/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPassword/)=true OR match_regex(cmd_line, /(?i)Set-ADDBAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-ADDBBootKey/)=true OR match_regex(cmd_line, /(?i)Set-SamAccountPasswordHash/)=true OR match_regex(cmd_line, /(?i)Set-AzureADUserEx/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* process_name

* parent_process_name

* _time

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1098 | Account Manipulation | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/MichaelGrafnetter/DSInternals



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllDSInternalsModules.log


_version_: 1
</details>

---

### Setting Credentials via Mimikatz modules
This detection identifies illegal setting of credentials via Mimikatz modules.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/)
- **Last Updated**: 2020-11-03

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)misc::addsid/)=true OR match_regex(cmd_line, /(?i)CRYPTO::scauth/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1098 | Account Manipulation | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/gentilkiwi/mimikatz



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllMimikatzModules.log


_version_: 1
</details>

---

### Setting Credentials via PowerSploit modules
This detection identifies illegal setting of credentials via PowerSploit modules.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/), [T1078](https://attack.mitre.org/techniques/T1078/), [T1098](https://attack.mitre.org/techniques/T1098/)
- **Last Updated**: 2020-11-03

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events()

| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line != null AND ( match_regex(cmd_line, /(?i)Set-DomainUserPassword/)=true )

| eval start_time = timestamp, end_time = timestamp, entities = mvappend( ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id,  "cmd_line", cmd_line]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### How To Implement
You must be ingesting Windows Security logs from devices of interest, including the event ID 4688 with enabled command line logging.

#### Required field

* dest_device_id

* dest_user_id

* process

* _time



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |
| T1098 | Account Manipulation | Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified.

#### Reference


* https://github.com/PowerShellMafia/PowerSploit



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1098/logAllPowerSploitModulesWithOldNames.log


_version_: 1
</details>

---

### Shim Database File Creation
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1546.011](https://attack.mitre.org/techniques/T1546/011/)
- **Last Updated**: 2020-12-08

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Filesystem.action) values(Filesystem.file_hash) as file_hash values(Filesystem.file_path) as file_path  min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path=*Windows\\AppPatch\\Custom* by Filesystem.file_name Filesystem.dest 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
|`drop_dm_object_name(Filesystem)` 
| `shim_database_file_creation_filter`
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### 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.

#### Required field

* _time

* Filesystem.file_hash

* Filesystem.file_path

* Filesystem.file_name

* Filesystem.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1546.011 | Application Shimming | Privilege Escalation, Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Shim Database Installation With Suspicious Parameters
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1546.011](https://attack.mitre.org/techniques/T1546/011/)
- **Last Updated**: 2020-11-23

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `shim_database_installation_with_suspicious_parameters_filter`
```
#### Associated Analytic Story

* Windows Persistence Techniques


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1546.011 | Application Shimming | Privilege Escalation, Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Short Lived Windows Accounts
This search detects accounts that were created and deleted in a short time period.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: [T1136.001](https://attack.mitre.org/techniques/T1136/001/)
- **Last Updated**: 2020-07-06

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(All_Changes.result_id) as result_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Change where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by _time span=4h All_Changes.user All_Changes.dest 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name("All_Changes")` 
| search result_id = 4720 result_id=4726 
| transaction user connected=false maxspan=240m 
| table firstTime lastTime count user dest result_id 
| `short_lived_windows_accounts_filter`
```
#### Associated Analytic Story

* Account Monitoring and Controls


#### 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/

#### Required field

* _time

* All_Changes.result_id

* All_Changes.user

* All_Changes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1136.001 | Local Account | Persistence |


#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-system.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1136.001/atomic_red_team/windows-sysmon.log


_version_: 2
</details>

---

### SilentCleanup UAC Bypass
This search is to detect a suspicious modification of registry that may related to UAC bypassed. This registry will be trigger once the attacker abuse the silentcleanup task schedule to gain high privilege execution that will bypass User control account.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548/002/)
- **Last Updated**: 2021-07-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\windir" Registry.registry_value_name = "*.exe*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `silentcleanup_uac_bypass_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_path

* Registry.registry_key_name

* Registry.registry_value_name

* Registry.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548.002 | Bypass User Account Control | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://github.com/hfiref0x/UACME

* https://www.intezer.com/blog/malware-analysis/klingon-rat-holding-on-for-dear-life/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log


_version_: 1
</details>

---

### Single Letter Process On Endpoint
This search looks for process names that consist only of a single letter.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1204.002](https://attack.mitre.org/techniques/T1204/002/)
- **Last Updated**: 2020-12-08

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes by Processes.dest, Processes.user, Processes.process, Processes.process_name 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) 
| search process_name_length=5 AND endExe=1 
| table count, firstTime, lastTime, dest, user, process, process_name 
| `single_letter_process_on_endpoint_filter`
```
#### Associated Analytic Story

* DHS Report TA18-074A


#### 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.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.process

* Processes.process_name



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1204.002 | Malicious File | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Single-letter executables are not always malicious. Investigate this activity with your normal incident-response process.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204.002/single_letter_exe/windows-sysmon.log


_version_: 3
</details>

---

### Spike in File Writes
The search looks for a sharp increase in the number of files written to a particular host

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-03-16

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Filesystem where Filesystem.action=created by _time span=1h, Filesystem.dest 
| `drop_dm_object_name(Filesystem)` 
| eventstats max(_time) as maxtime 
| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-1d@d"), count, null))) as "count" avg(eval(if(_time<relative_time(maxtime, "-1d@d"), count,null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-1d@d"), count, null))) as stdev by "dest" 
| eval upperBound=(avg+stdev*4), isOutlier=if((count > upperBound) AND num_data_samples >=20, 1, 0) 
| search isOutlier=1 
| `spike_in_file_writes_filter` 
```
#### Associated Analytic Story

* SamSam Ransomware

* Ryuk Ransomware

* Ransomware


#### 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 product, 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.

#### Required field

* _time

* Filesystem.action

* Filesystem.dest




#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Spoolsv Spawning Rundll32
The following analytic identifies a suspicious child process, `rundll32.exe`, with no command-line arguments being spawned from `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to spawn a process. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1547.012](https://attack.mitre.org/techniques/T1547/012/)
- **Last Updated**: 2021-07-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe Processes.process_name=rundll32.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `spoolsv_spawning_rundll32_filter`
```
#### Associated Analytic Story

* PrintNightmare CVE-2021-34527


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1547.012 | Print Processors | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives have been identified. There are limited instances where `rundll32.exe` may be spawned by a legitimate print driver.

#### Reference


* https://blog.truesec.com/2021/06/30/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available/

* https://blog.truesec.com/2021/06/30/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675/

* https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log


_version_: 1
</details>

---

### Spoolsv Suspicious Loaded Modules
This search is to detect suspicious loading of dll in specific path relative to printnightmare exploitation. In this search we try to detect the loaded modules made by spoolsv.exe after the exploitation.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1547.012](https://attack.mitre.org/techniques/T1547/012/)
- **Last Updated**: 2021-07-01

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=7 Image ="*\\spoolsv.exe" ImageLoaded="*\\Windows\\System32\\spool\\drivers\\x64\\*" ImageLoaded = "*.dll" 
| stats dc(ImageLoaded) as countImgloaded values(ImageLoaded) as ImgLoaded count min(_time) as firstTime max(_time) as lastTime by Image Computer process_id EventCode 
| where countImgloaded >= 3 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `spoolsv_suspicious_loaded_modules_filter`
```
#### Associated Analytic Story

* PrintNightmare CVE-2021-34527


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* Image

* Computer

* EventCode

* ImageLoaded



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1547.012 | Print Processors | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://raw.githubusercontent.com/hieuttmmo/sigma/dceb13fe3f1821b119ae495b41e24438bd97e3d0/rules/windows/image_load/sysmon_cve_2021_1675_print_nightmare.yml



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log


_version_: 1
</details>

---

### Spoolsv Suspicious Process Access
This analytic identifies a suspicious behavior related to PrintNightmare, or CVE-2021-34527 previously (CVE-2021-1675),  to gain privilege escalation on the vulnerable machine. This exploit attacks a critical Windows Print Spooler Vulnerability to elevate privilege. This detection is to look for suspicious process access made by the spoolsv.exe that may related to the attack.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1068](https://attack.mitre.org/techniques/T1068/)
- **Last Updated**: 2021-07-01

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=10 SourceImage = "*\\spoolsv.exe" CallTrace = "*\\Windows\\system32\\spool\\DRIVERS\\x64\\*" TargetImage IN ("*\\rundll32.exe", "*\\spoolsv.exe") GrantedAccess = 0x1fffff 
| stats  count min(_time) as firstTime max(_time) as lastTime by Computer SourceImage TargetImage GrantedAccess CallTrace  EventCode ProcessID
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `spoolsv_suspicious_process_access_filter`
```
#### Associated Analytic Story

* PrintNightmare CVE-2021-34527


#### How To Implement
To successfully implement this search, you need to be ingesting logs with process access event where SourceImage, TargetImage, GrantedAccess and CallTrace executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. Tune and filter known instances of spoolsv.exe.

#### Required field

* _time

* SourceImage

* TargetImage

* GrantedAccess

* CallTrace

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1068 | Exploitation for Privilege Escalation | Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Unknown. Filter as needed.

#### Reference


* https://github.com/cube0x0/impacket/commit/73b9466c17761384ece11e1028ec6689abad6818

* https://blog.truesec.com/2021/06/30/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available/

* https://blog.truesec.com/2021/06/30/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675/

* https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log


_version_: 1
</details>

---

### Spoolsv Writing a DLL
The following analytic identifies a `.dll` being written by `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously (CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to write a `.dll`. Current POC code used will write the suspicious DLL to disk within a path of `\spool\drivers\x64\`. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1547.012](https://attack.mitre.org/techniques/T1547/012/)
- **Last Updated**: 2021-07-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.process_name=spoolsv.exe by _time Processes.process_id Processes.process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| join process_guid, _time [
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_path="*\\spool\\drivers\\x64\\*" Filesystem.file_name="*.dll" by _time Filesystem.dest Filesystem.file_create_time Filesystem.file_name Filesystem.file_path 
| `drop_dm_object_name(Filesystem)` 
| fields _time dest file_create_time file_name file_path process_name process_path process] 
| dedup file_create_time 
| table dest file_create_time, file_name, file_path, process_name 
| `spoolsv_writing_a_dll_filter`
```
#### Associated Analytic Story

* PrintNightmare CVE-2021-34527


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node and `Filesystem` node.

#### Required field

* _time

* Filesystem.dest

* Filesystem.file_create_time

* Filesystem.file_name

* Filesystem.file_path

* Processes.process_name

* Processes.process_id

* Processes.process_name

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1547.012 | Print Processors | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Unknown.

#### Reference


* https://blog.truesec.com/2021/06/30/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available/

* https://blog.truesec.com/2021/06/30/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675/

* https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log


_version_: 1
</details>

---

### Spoolsv Writing a DLL - Sysmon
The following analytic identifies a `.dll` being written by `spoolsv.exe`. This was identified during our testing of CVE-2021-34527 previously(CVE-2021-1675) or PrintNightmare. Typically, this is not normal behavior for `spoolsv.exe` to write a `.dll`. Current POC code used will write the suspicious DLL to disk within a path of `\spool\drivers\x64\`. During triage, isolate the endpoint and review for source of exploitation. Capture any additional file modification events.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1547.012](https://attack.mitre.org/techniques/T1547/012/)
- **Last Updated**: 2021-07-01

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=11 process_name=spoolsv.exe file_path="*\\spool\\drivers\\x64\\*" file_name=*.dll 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, UserID, process_name, file_path, file_name, TargetFilename, process_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `spoolsv_writing_a_dll___sysmon_filter`
```
#### Associated Analytic Story

* PrintNightmare CVE-2021-34527


#### 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. Tune and filter known instances where renamed rundll32.exe may be used.

#### Required field

* _time

* dest

* UserID

* process_name

* file_path

* file_name

* TargetFilename



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1547.012 | Print Processors | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives. Filter as needed.

#### Reference


* https://github.com/cube0x0/impacket/commit/73b9466c17761384ece11e1028ec6689abad6818

* https://blog.truesec.com/2021/06/30/fix-for-printnightmare-cve-2021-1675-exploit-to-keep-your-print-servers-running-while-a-patch-is-not-available/

* https://blog.truesec.com/2021/06/30/exploitable-critical-rce-vulnerability-allows-regular-users-to-fully-compromise-active-directory-printnightmare-cve-2021-1675/

* https://www.reddit.com/r/msp/comments/ob6y02/critical_vulnerability_printnightmare_exposes



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1547.012/printnightmare/windows-sysmon.log


_version_: 1
</details>

---

### Sqlite Module In Temp Folder
This search is to detect a suspicious file creation of sqlite3.dll in %temp% folder. This behavior was seen in IcedID malware where it download sqlite module to parse browser database like for chrome or firefox to stole browser information related to bank, credit card or credentials.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1005](https://attack.mitre.org/techniques/T1005/)
- **Last Updated**: 2021-08-03

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=11 (TargetFilename = "*\\sqlite32.dll" OR TargetFilename = "*\\sqlite64.dll") (TargetFilename = "*\\temp\\*") 
|stats count min(_time) as firstTime max(_time) as lastTime by process_name TargetFilename EventCode ProcessId Image 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `sqlite_module_in_temp_folder_filter`
```
#### Associated Analytic Story

* Icedid


#### 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.

#### Required field

* _time

* process_name

* TargetFilename

* EventCode

* ProcessId

* Image



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1005 | Data from Local System | Collection |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://www.cisecurity.org/white-papers/security-primer-icedid/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/simulated_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Start Up During Safe Mode Boot
This search is to detect a modification or registry add to the safeboot registry as an autostart mechanism. This technique was seen in some ransomware to automatically execute its code upon a safe mode boot.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1547.001](https://attack.mitre.org/techniques/T1547/001/)
- **Last Updated**: 2021-06-10

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*\\System\\CurrentControlSet\\Control\\SafeBoot\\Minimal\*" by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `start_up_during_safe_mode_boot_filter`
```
#### Associated Analytic Story

* Ransomware


#### 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 product, 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.

#### Required field

* _time

* Registry.registry_path

* Registry.registry_key_name

* Registry.registry_value_name

* Registry.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1547.001 | Registry Run Keys / Startup Folder | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
updated windows application needed in safe boot may used this registry

#### Reference


* https://malware.news/t/threat-analysis-unit-tau-threat-intelligence-notification-snatch-ransomware/36365



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ransomware_ttp/data1/windows-sysmon.log


_version_: 1
</details>

---

### Sunburst Correlation DLL and Network Event
The malware sunburst will load the malicious dll by SolarWinds.BusinessLayerHost.exe. After a period of 12-14 days, the malware will attempt to resolve a subdomain of avsvmcloud.com. This detections will correlate both events.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1203](https://attack.mitre.org/techniques/T1203/)
- **Last Updated**: 2020-12-14

<details>
  <summary>details</summary>

#### Search
```
(`sysmon` EventCode=7 ImageLoaded=*SolarWinds.Orion.Core.BusinessLayer.dll) OR (`sysmon` EventCode=22 QueryName=*avsvmcloud.com) 
| eventstats dc(EventCode) AS dc_events 
| where dc_events=2 
| stats min(_time) as firstTime max(_time) as lastTime values(ImageLoaded) AS ImageLoaded values(QueryName) AS QueryName by host 
| rename host as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `sunburst_correlation_dll_and_network_event_filter` 
```
#### Associated Analytic Story

* NOBELIUM Group


#### How To Implement
This detection relies on sysmon logs with the Event ID 7, Driver loaded. Please tune your sysmon config that you DriverLoad event for SolarWinds.Orion.Core.BusinessLayer.dll is captured by Sysmon. Additionally, you need sysmon logs for Event ID 22, DNS Query. We suggest to run this detection at least once a day over the last 14 days.

#### Required field

* _time

* EventCode

* ImageLoaded

* QueryName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1203 | Exploitation for Client Execution | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
unknown

#### Reference


* https://www.fireeye.com/blog/threat-research/2020/12/evasive-attacker-leverages-solarwinds-supply-chain-compromises-with-sunburst-backdoor.html



#### Test Dataset


_version_: 1
</details>

---

### Supernova Webshell
This search aims to detect the Supernova webshell used in the SUNBURST attack.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Web
- **ATT&CK**: [T1505.003](https://attack.mitre.org/techniques/T1505/003/)
- **Last Updated**: 2021-01-06

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s 
| `supernova_webshell_filter`
```
#### Associated Analytic Story

* NOBELIUM Group


#### How To Implement
To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model.

#### Required field

* _time

* Web.url

* Web.src

* Web.dest

* Web.vendor_product

* Web.user

* Web.http_user_agent



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1505.003 | Web Shell | Persistence |


#### Kill Chain Phase

* Exfiltration


#### Known False Positives
There might be false positives associted with this detection since items like args as a web argument is pretty generic.

#### Reference


* https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html

* https://www.guidepointsecurity.com/supernova-solarwinds-net-webshell-analysis/



#### Test Dataset


_version_: 1
</details>

---

### Suspicious Curl Network Connection
The following analytic identifies the use of a curl contacting suspicious remote domains to checkin to command and control servers or download further implants. In the context of Silver Sparrow, curl is identified contacting s3.amazonaws.com. This particular behavior is common with MacOS adware-malicious software.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1105](https://attack.mitre.org/techniques/T1105/)
- **Last Updated**: 2021-02-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=curl Processes.process=s3.amazonaws.com by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `suspicious_curl_network_connection_filter`
```
#### Associated Analytic Story

* Silver Sparrow

* Ingress Tool Transfer


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1105 | Ingress Tool Transfer | Command And Control |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Unknown. Filter as needed.

#### Reference


* https://redcanary.com/blog/clipping-silver-sparrows-wings/

* https://marcosantadev.com/manage-plist-files-plistbuddy/



#### Test Dataset


_version_: 1
</details>

---

### Suspicious DLLHost no Command Line Arguments
The following analytic identifies DLLHost.exe with no command line arguments. It is unusual for DLLHost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. DLLHost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-02-23

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (process_name=dllhost.exe OR OriginalFileName=dllhost.exe) 
| regex CommandLine="(dllhost\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_dllhost_no_command_line_arguments_filter`
```
#### Associated Analytic Story

* Cobalt Strike


#### 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.

#### Required field

* _time

* EventID

* process_name

* OriginalFileName

* CommandLine

* dest

* User

* ParentImage

* ParentCommandLine

* process_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives may be present in small environments. Tuning may be required based on parent process.

#### Reference


* https://raw.githubusercontent.com/threatexpress/malleable-c2/c3385e481159a759f79b8acfe11acf240893b830/jquery-c2.4.2.profile

* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious Driver Loaded Path
This analytic will detect suspicious driver loaded paths. This technique is commonly used by malicious software like coin miners (xmrig) to register its malicious driver from notable directories where executable or drivers do not commonly exist. During triage, validate this driver is for legitimate business use. Review the metadata and certificate information. Unsigned drivers from non-standard paths is not normal, but occurs. In addition, review driver loads into `ntoskrnl.exe` for possible other drivers of interest. Long tail analyze drivers by path (outside of default, and in default) for further review.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1543.003](https://attack.mitre.org/techniques/T1543/003/)
- **Last Updated**: 2021-04-29

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=6 ImageLoaded = "*.sys" NOT (ImageLoaded IN("*\\WINDOWS\\inf","*\\WINDOWS\\System32\\drivers\\*", "*\\WINDOWS\\System32\\DriverStore\\FileRepository\\*")) 
|  stats  min(_time) as firstTime max(_time) as lastTime count by Computer ImageLoaded Hashes IMPHASH Signature Signed 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_driver_loaded_path_filter`
```
#### Associated Analytic Story

* XMRig


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the driver loaded and Signature from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* Computer

* ImageLoaded

* Hashes

* IMPHASH

* Signature

* Signed



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1543.003 | Windows Service | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives will be present. Some applications do load drivers

#### Reference


* https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/

* https://redcanary.com/blog/tracking-driver-inventory-to-expose-rootkits/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious Email Attachment Extensions
This search looks for emails that have attachments with suspicious file extensions.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Email
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2020-07-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Email")` 
| `suspicious_email_attachments` 
| `suspicious_email_attachment_extensions_filter` 
```
#### Associated Analytic Story

* Emotet Malware  DHS Report TA18-201A 

* Suspicious Emails


#### 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.

#### Required field

* _time

* All_Email.file_name

* All_Email.src_user

* All_Email.message_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Delivery


#### Known False Positives
None identified

#### Reference


#### Test Dataset


_version_: 3
</details>

---

### Suspicious Event Log Service Behavior
The following analytic utilizes Windows Event ID 1100 to identify when Windows event log service is shutdown. Note that this is a voluminous analytic that will require tuning or restricted to specific endpoints based on criticality. This event generates every time Windows Event Log service has shut down. It also generates during normal system shutdown. During triage, based on time of day and user, determine if this was planned. If not planned, follow through with reviewing parallel alerts and other data sources to determine what else may have occurred.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1070.001](https://attack.mitre.org/techniques/T1070/001/)
- **Last Updated**: 2021-06-17

<details>
  <summary>details</summary>

#### Search
```
(`wineventlog_security` EventCode=1100) 
| stats count min(_time) as firstTime max(_time) as lastTime by dest Message EventCode 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_event_log_service_behavior_filter`
```
#### Associated Analytic Story

* Windows Log Manipulation

* Ransomware

* Clop Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.

#### Required field

* _time

* EventCode

* dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070.001 | Clear Windows Event Logs | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
It is possible the Event Logging service gets shut down due to system errors or legitimately administration tasks. Filter as needed.

#### Reference


* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-1100

* https://www.ired.team/offensive-security/defense-evasion/disabling-windows-event-logs-by-suspending-eventlog-service-threads

* https://attack.mitre.org/techniques/T1070/001/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-security.log


_version_: 1
</details>

---

### Suspicious GPUpdate no Command Line Arguments
The following analytic identifies gpupdate.exe with no command line arguments. It is unusual for gpupdate.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. gpupdate.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-02-23

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (process_name=gpupdate.exe OR OriginalFileName=GPUpdate.exe) 
| regex CommandLine="(gpupdate\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_gpupdate_no_command_line_arguments_filter`
```
#### Associated Analytic Story

* Cobalt Strike


#### 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.

#### Required field

* _time

* EventID

* process_name

* OriginalFileName

* CommandLine

* dest

* User

* ParentImage

* ParentCommandLine

* process_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives may be present in small environments. Tuning may be required based on parent process.

#### Reference


* https://raw.githubusercontent.com/xx0hcd/Malleable-C2-Profiles/0ef8cf4556e26f6d4190c56ba697c2159faa5822/crimeware/trick_ryuk.profile

* https://blog.cobaltstrike.com/2021/02/09/learn-pipe-fitting-for-all-of-your-offense-projects/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious IcedID Regsvr32 Cmdline
this search is to detect a suspicious regsvr32 commandline "-s" to execute a dll files. This technique was seen in IcedID malware to execute its initial downloader dll that will download the 2nd stage loader that will download and decrypt the config payload.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.010](https://attack.mitre.org/techniques/T1218/010/)
- **Last Updated**: 2021-07-27

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe Processes.process=*-s* by  Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_icedid_regsvr32_cmdline_filter`
```
#### Associated Analytic Story

* Icedid


#### 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. Tune and filter known instances where renamed regsvr32.exe may be used.

#### Required field

* _time

* process_name

* process

* parent_process_name

* parent_process

* process_id

* parent_process_id

* dest

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.010 | Regsvr32 | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
minimal. but network operator can use this application to load dll.

#### Reference


* https://app.any.run/tasks/56680cba-2bbc-4b34-8633-5f7878ddf858/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious IcedID Rundll32 Cmdline
This search is to detect a suspicious rundll32.exe commandline to execute dll file. This technique was seen in IcedID malware to load its payload dll with the following parameter to load encrypted dll payload which is the license.dat.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-07-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*/i:* by  Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_icedid_rundll32_cmdline_filter`
```
#### Associated Analytic Story

* Icedid


#### 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. Tune and filter known instances where renamed rundll32.exe may be used.

#### Required field

* _time

* process_name

* process

* parent_process_name

* parent_process

* process_id

* parent_process_id

* dest

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
limitted. this parameter is not commonly used by windows application but can be used by the network operator.

#### Reference


* https://threatpost.com/icedid-banking-trojan-surges-emotet/165314/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious Java Classes
This search looks for suspicious Java classes that are often used to exploit remote command execution in common Java frameworks, such as Apache Struts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-12-06

<details>
  <summary>details</summary>

#### Search
```
`stream_http` http_method=POST http_content_length>1 
| regex form_data="(?i)java\.lang\.(?:runtime
|processbuilder)" 
| rename src_ip as src 
| stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_java_classes_filter`
```
#### Associated Analytic Story

* Apache Struts Vulnerability


#### 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.

#### Required field

* _time

* http_method

* http_content_length

* src_ip

* url

* status

* http_user_agent

* src

* dest




#### Kill Chain Phase

* Exploitation


#### Known False Positives
There are no known false positives.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Suspicious MSBuild Rename
The following analytic identifies renamed instances of msbuild.exe executing. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. During investigation, identify the code executed and what is executing a renamed instance of MSBuild.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1127.001](https://attack.mitre.org/techniques/T1127/001/), [T1036.003](https://attack.mitre.org/techniques/T1036/003/)
- **Last Updated**: 2021-01-12

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (OriginalFileName=msbuild.exe process_name!=msbuild.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `suspicious_msbuild_rename_filter`
```
#### Associated Analytic Story

* Trusted Developer Utilities Proxy Execution MSBuild

* Cobalt Strike

* Masquerading - Rename System Utilities


#### 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.

#### Required field

* _time

* EventID

* OriginalFileName

* process_name

* Computer

* User

* parent_process_name

* process_path

* CommandLine



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1127.001 | MSBuild | Defense Evasion |
| T1036.003 | Rename System Utilities | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, some legitimate applications may use a moved copy of msbuild, triggering a false positive.

#### Reference


* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md

* https://github.com/infosecn1nja/MaliciousMacroMSBuild/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious MSBuild Spawn
The following analytic identifies wmiprvse.exe spawning msbuild.exe. This behavior is indicative of a COM object being utilized to spawn msbuild from wmiprvse.exe. It is common for MSBuild.exe to be spawned from devenv.exe while using Visual Studio. In this instance, there will be command line arguments and file paths. In a malicious instance, MSBuild.exe will spawn from non-standard processes and have no command line arguments. For example, MSBuild.exe spawning from explorer.exe, powershell.exe is far less common and should be investigated.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1127.001](https://attack.mitre.org/techniques/T1127/001/)
- **Last Updated**: 2021-01-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wmiprvse.exe AND Processes.process_name=msbuild.exe by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_msbuild_spawn_filter`
```
#### Associated Analytic Story

* Trusted Developer Utilities Proxy Execution MSBuild


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.dest

* Processes.parent_process

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1127.001 | MSBuild | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.

#### Reference


* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious PlistBuddy Usage
The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\
- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \
Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1543.001](https://attack.mitre.org/techniques/T1543/001/)
- **Last Updated**: 2021-02-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=PlistBuddy (Processes.process=*LaunchAgents* OR Processes.process=*RunAtLoad* OR Processes.process=*true*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
|  `suspicious_plistbuddy_usage_filter`
```
#### Associated Analytic Story

* Silver Sparrow


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1543.001 | Launch Agent | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.

#### Reference


* https://redcanary.com/blog/clipping-silver-sparrows-wings/

* https://marcosantadev.com/manage-plist-files-plistbuddy/



#### Test Dataset


_version_: 1
</details>

---

### Suspicious PlistBuddy Usage via OSquery
The following analytic identifies the use of a native MacOS utility, PlistBuddy, creating or modifying a properly list (.plist) file. In the instance of Silver Sparrow, the following commands were executed:\
- PlistBuddy -c "Add :Label string init_verx" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :RunAtLoad bool true" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :StartInterval integer 3600" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments array" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:0 string /bin/sh" ~/Library/Launchagents/init_verx.plist \
- PlistBuddy -c "Add :ProgramArguments:1 string -c" ~/Library/Launchagents/init_verx.plist \
Upon triage, capture the property list file being written to disk and review for further indicators. Contain the endpoint and triage further.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1543.001](https://attack.mitre.org/techniques/T1543/001/)
- **Last Updated**: 2021-02-22

<details>
  <summary>details</summary>

#### Search
```
`osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*" OR "columns.cmdline"="*true*" 
|  `suspicious_plistbuddy_usage_via_osquery_filter`
```
#### Associated Analytic Story

* Silver Sparrow


#### How To Implement
OSQuery must be installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. Modify the macro and validate fields are correct.

#### Required field

* _time

* columns.cmdline



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1543.001 | Launch Agent | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.

#### Reference


* https://redcanary.com/blog/clipping-silver-sparrows-wings/

* https://marcosantadev.com/manage-plist-files-plistbuddy/



#### Test Dataset


_version_: 1
</details>

---

### Suspicious Process File Path
The following analytic will detect a suspicious process running in a file path where a process is not commonly seen and is most commonly used by malicious softtware. This behavior has been used by adversaries where they drop and run an exe in a path that is accessible without admin privileges.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1543](https://attack.mitre.org/techniques/T1543/)
- **Last Updated**: 2021-05-05

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\windows\\fonts\\*" OR Processes.process_path = "*\\windows\\temp\\*" OR Processes.process_path = "*\\users\\public\\*" OR Processes.process_path = "*\\windows\\debug\\*" OR Processes.process_path.file_path = "*\\Users\\Administrator\\Music\\*" OR Processes.process_path.file_path = "*\\Windows\\servicing\\*" OR Processes.process_path.file_path = "*\\Users\\Default\\*" OR Processes.process_path.file_path = "*Recycle.bin*" OR Processes.process_path = "*\\Windows\\Media\\*" OR Processes.process_path = "\\Windows\\repair\\*" OR Processes.process_path = "*\\temp\\*" by Processes.parent_process_name Processes.parent_process Processes.process_path Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_process_file_path_filter`
```
#### Associated Analytic Story

* XMRig


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.parent_process

* Processes.process_path

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1543 | Create or Modify System Process | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Administrators may allow execution of specific binaries in non-standard paths. Filter as needed.

#### Reference


* https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious Reg exe Process
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1112](https://attack.mitre.org/techniques/T1112/)
- **Last Updated**: 2020-07-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.parent_process_name != explorer.exe Processes.process_name =cmd.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search [
| tstats `security_content_summariesonly` count FROM datamodel=Endpoint.Processes where Processes.parent_process_name=cmd.exe Processes.process_name= reg.exe by Processes.parent_process_id Processes.dest Processes.process_name 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| rename parent_process_id as process_id 
|dedup process_id
| table process_id dest] 
| `suspicious_reg_exe_process_filter` 
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics

* Disabling Security Tools

* DHS Report TA18-074A


#### 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.

#### Required field

* _time

* Processes.parent_process_name

* Processes.process_name

* Processes.user

* Processes.parent_process_name

* Processes.dest

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1112 | Modify Registry | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


* https://car.mitre.org/wiki/CAR-2013-03-001



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/atomic_red_team/windows-sysmon.log


_version_: 4
</details>

---

### Suspicious Regsvr32 Register Suspicious Path
Adversaries may abuse Regsvr32.exe to proxy execution of malicious code by using non-standard file extensions to load malciious DLLs. Upon investigating, look for network connections to remote destinations (internal or external). Review additional parrallel processes and child processes for additional activity.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.010](https://attack.mitre.org/techniques/T1218/010/)
- **Last Updated**: 2021-01-28

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=regsvr32.exe (Processes.process=*appdata* OR Processes.process=*programdata* OR Processes.process=*windows\temp*) (Processes.process!=*.dll Processes.process!=*.ax Processes.process!=*.ocx) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `suspicious_regsvr32_register_suspicious_path_filter`
```
#### Associated Analytic Story

* Suspicious Regsvr32 Activity

* Iceid


#### 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. Tune the query by filtering additional extensions found to be used by  legitimate processes.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.010 | Regsvr32 | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Limited false positives with the query restricted to specified paths. Add more world writeable paths as tuning continues.

#### Reference


* https://attack.mitre.org/techniques/T1218/010/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.010/T1218.010.md

* https://lolbas-project.github.io/lolbas/Binaries/Regsvr32/

* https://support.microsoft.com/en-us/topic/how-to-use-the-regsvr32-tool-and-troubleshoot-regsvr32-error-messages-a98d960a-7392-e6fe-d90a-3f4e0cb543e5

* https://any.run/report/f29a7d2ecd3585e1e4208e44bcc7156ab5388725f1d29d03e7699da0d4598e7c/0826458b-5367-45cf-b841-c95a33a01718



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.010/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious Rundll32 PluginInit
This search is to detect a suspicious rundll32.exe process with plugininit parameter. This technique is commonly seen in IceID malware to execute its initial dll stager to download another payload to the compromised machine.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-07-26

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*PluginInit* by  Processes.process_name Processes.process Processes.parent_process_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_rundll32_plugininit_filter`
```
#### Associated Analytic Story

* Icedid


#### 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. Tune and filter known instances where renamed rundll32.exe may be used.

#### Required field

* _time

* process_name

* process

* parent_process_name

* parent_process

* process_id

* parent_process_id

* dest

* user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
third party application may used this dll export name to execute function.

#### Reference


* https://threatpost.com/icedid-banking-trojan-surges-emotet/165314/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/icedid/inf_icedid/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious Rundll32 Rename
The following analytic identifies renamed instances of rundll32.exe executing. rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. During investigation, validate it is the legitimate rundll32.exe executing and what script content it is loading. This query relies on the OriginalFileName from Sysmon, or internal name from the PE meta data. Expand the query as needed by looking for specific command line arguments outlined in other analytics.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/), [T1036.003](https://attack.mitre.org/techniques/T1036/003/)
- **Last Updated**: 2021-02-04

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 OriginalFileName=RUNDLL32.EXE NOT process_name=rundll32.exe 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `suspicious_rundll32_rename_filter`
```
#### Associated Analytic Story

* Suspicious Rundll32 Activity

* Masquerading - Rename System Utilities


#### 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. Tune and filter known instances where renamed rundll32.exe may be used.

#### Required field

* _time

* EventID

* OriginalFileName

* process_name

* Computer

* User

* parent_process_name

* process_path

* CommandLine



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |
| T1036.003 | Rename System Utilities | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.

#### Reference


* https://attack.mitre.org/techniques/T1218/011/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

* https://lolbas-project.github.io/lolbas/Binaries/Rundll32



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious Rundll32 StartW
The following analytic identifies rundll32.exe executing a DLL function name, Start and StartW, on the command line that is commonly observed with Cobalt Strike x86 and x64 DLL payloads. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64. Typically, the DLL will be written and loaded from a world writeable path or user location. In most instances it will not have a valid certificate (Unsigned). During investigation, review the parent process and other parallel application execution. Capture and triage the DLL in question. In the instance of Cobalt Strike, rundll32.exe is the default process it opens and injects shellcode into. This default process can be changed, but typically is not.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-02-04

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*start* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_rundll32_startw_filter`
```
#### Associated Analytic Story

* Suspicious Rundll32 Activity

* Cobalt Strike

* Trickbot


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, some legitimate applications may use Start as a function and call it via the command line. Filter as needed.

#### Reference


* https://attack.mitre.org/techniques/T1218/011/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

* https://www.cobaltstrike.com/help-windows-executable

* https://lolbas-project.github.io/lolbas/Binaries/Rundll32

* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious Rundll32 dllregisterserver
The following analytic identifies rundll32.exe using dllregisterserver on the command line to load a DLL. When a DLL is registered, the DllRegisterServer method entry point in the DLL is invoked. This is typically seen when a DLL is being registered on the system. Not every instance is considered malicious, but it will capture malicious use of it. During investigation, review the parent process and parrellel processes executing. Capture the DLL being loaded and inspect further. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-02-09

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=rundll32.exe Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_rundll32_dllregisterserver_filter`
```
#### Associated Analytic Story

* Suspicious Rundll32 Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names.

#### Reference


* https://attack.mitre.org/techniques/T1218/011/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

* https://lolbas-project.github.io/lolbas/Binaries/Rundll32

* https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/seedworm-apt-iran-middle-east

* https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt

* https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/

* https://msdn.microsoft.com/en-us/library/windows/desktop/ms682162(v=vs.85).aspx



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious Rundll32 no Command Line Arguments
The following analytic identifies rundll32.exe with no command line arguments. It is unusual for rundll32.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. Rundll32.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1218.011](https://attack.mitre.org/techniques/T1218/011/)
- **Last Updated**: 2021-02-09

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (process_name=rundll32.exe OR OriginalFileName=RUNDLL32.EXE) 
| regex CommandLine="(rundll32\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_rundll32_no_command_line_arguments_filter`
```
#### Associated Analytic Story

* Suspicious Rundll32 Activity

* Cobalt Strike

* PrintNightmare CVE-2021-34527


#### 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.

#### Required field

* _time

* EventID

* process_name

* OriginalFileName

* CommandLine

* dest

* User

* ParentImage

* ParentCommandLine

* process_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.011 | Rundll32 | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, some legitimate applications may use a moved copy of rundll32, triggering a false positive.

#### Reference


* https://attack.mitre.org/techniques/T1218/011/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

* https://lolbas-project.github.io/lolbas/Binaries/Rundll32

* https://bohops.com/2018/02/26/leveraging-inf-sct-fetch-execute-techniques-for-bypass-evasion-persistence/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.011/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious SQLite3 LSQuarantine Behavior
The following analytic identifies the use of a SQLite3 querying the MacOS preferences to identify the original URL the pkg was downloaded from. This particular behavior is common with MacOS adware-malicious software. Upon triage, review other processes in parallel for suspicious activity. Identify any recent package installations.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1074](https://attack.mitre.org/techniques/T1074/)
- **Last Updated**: 2021-02-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sqlite3 Processes.process=*LSQuarantine* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `suspicious_sqlite3_lsquarantine_behavior_filter`
```
#### Associated Analytic Story

* Silver Sparrow


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1074 | Data Staged | Collection |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Unknown.

#### Reference


* https://redcanary.com/blog/clipping-silver-sparrows-wings/

* https://marcosantadev.com/manage-plist-files-plistbuddy/



#### Test Dataset


_version_: 1
</details>

---

### Suspicious Scheduled Task from Public Directory
The following detection identifies Scheduled Tasks registering (creating a new task) a binary or script to run from a public directory which includes users\public, \programdata\ and \windows\temp. Upon triage, review the binary or script in the command line for legitimacy, whether an approved binary/script or not. In addition, capture the binary or script in question and analyze for further behaviors. Identify the source and contain the endpoint.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053/005/)
- **Last Updated**: 2021-03-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*\\users\\public\\* OR Processes.process=*\\programdata\\* OR Processes.process=*windows\\temp*)  Processes.process=*/create* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `suspicious_scheduled_task_from_public_directory_filter`
```
#### Associated Analytic Story

* Ransomware

* Ryuk Ransomware

* Windows Persistence Techniques


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation

* Privilege Escalation


#### Known False Positives
Limited false positives may be present. Filter as needed by parent process or command line argument.

#### Reference


* https://attack.mitre.org/techniques/T1053/005/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/schtasks/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious SearchProtocolHost no Command Line Arguments
The following analytic identifies searchprotocolhost.exe with no command line arguments. It is unusual for searchprotocolhost.exe to execute with no command line arguments present. This particular behavior is common with malicious software, including Cobalt Strike. During investigation, identify any network connections and parallel processes. Identify any suspicious module loads related to credential dumping or file writes. searchprotocolhost.exe is natively found in C:\Windows\system32 and C:\Windows\syswow64.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-02-23

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (process_name=searchprotocolhost.exe OR OriginalFileName=SearchProtocolHost.exe) 
| regex CommandLine="(searchprotocolhost\.exe.{0,4}$)" 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, User, ParentImage,ParentCommandLine, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_searchprotocolhost_no_command_line_arguments_filter`
```
#### Associated Analytic Story

* Cobalt Strike


#### 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.

#### Required field

* _time

* EventID

* process_name

* OriginalFileName

* CommandLine

* dest

* User

* ParentImage

* ParentCommandLine

* process_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Limited false positives may be present in small environments. Tuning may be required based on parent process.

#### Reference


* https://github.com/fireeye/red_team_tool_countermeasures/blob/master/rules/PGF/supplemental/hxioc/SUSPICIOUS%20EXECUTION%20OF%20SEARCHPROTOCOLHOST%20(METHODOLOGY).ioc



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1055/cobalt_strike/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious microsoft workflow compiler rename
The following analytic identifies a renamed instance of microsoft.workflow.compiler.exe. Microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. A spawned child process from microsoft.workflow.compiler.exe is uncommon. In any instance, microsoft.workflow.compiler.exe spawning from an Office product or any living off the land binary is highly suspect.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1127](https://attack.mitre.org/techniques/T1127/), [T1036.003](https://attack.mitre.org/techniques/T1036/003/)
- **Last Updated**: 2021-01-12

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventID=1 (OriginalFileName=microsoft.workflow.compiler.exe OR process_name=microsoft.workflow.compiler.exe) 
| stats count min(_time) as firstTime max(_time) as lastTime by Computer, User, parent_process_name, process_name, OriginalFileName, process_path, CommandLine 
| rename Computer as dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `suspicious_microsoft_workflow_compiler_rename_filter`
```
#### Associated Analytic Story

* Trusted Developer Utilities Proxy Execution

* Cobalt Strike

* Masquerading - Rename System Utilities


#### 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.

#### Required field

* _time

* EventID

* OriginalFileName

* process_name

* Computer

* User

* parent_process_name

* process_path

* CommandLine



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1127 | Trusted Developer Utilities Proxy Execution | Defense Evasion |
| T1036.003 | Rename System Utilities | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, some legitimate applications may use a moved copy of microsoft.workflow.compiler.exe, triggering a false positive.

#### Reference


* https://lolbas-project.github.io/lolbas/Binaries/Microsoft.Workflow.Compiler/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious microsoft workflow compiler usage
The following analytic identifies microsoft.workflow.compiler.exe usage. microsoft.workflow.compiler.exe is natively found in C:\Windows\Microsoft.NET\Framework64\v4.0.30319 and is rarely utilized. When investigating, identify the executed code on disk and review. It is not a commonly used process by many applications.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1127](https://attack.mitre.org/techniques/T1127/)
- **Last Updated**: 2021-01-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=microsoft.workflow.compiler.exe by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_microsoft_workflow_compiler_usage_filter`
```
#### Associated Analytic Story

* Trusted Developer Utilities Proxy Execution


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.parent_process

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1127 | Trusted Developer Utilities Proxy Execution | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, limited instances have been identified coming from native Microsoft utilities similar to SCCM.

#### Reference


* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218/T1218.md#atomic-test-6---microsoftworkflowcompilerexe-payload-execution



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious msbuild path
The following analytic identifies msbuild.exe executing from a non-standard path. Msbuild.exe is natively found in C:\Windows\Microsoft.NET\Framework\v4.0.30319 and C:\Windows\Microsoft.NET\Framework64\v4.0.30319. Instances of Visual Studio will run a copy of msbuild.exe. A moved instance of MSBuild is suspicious, however there are instances of build applications that will move or use a copy of MSBuild.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1127.001](https://attack.mitre.org/techniques/T1127/001/), [T1036.003](https://attack.mitre.org/techniques/T1036/003/)
- **Last Updated**: 2021-01-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=msbuild.exe AND (Processes.process_path!=c:\\windows\\microsoft.net\\framework*\\v*\\*) by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `suspicious_msbuild_path_filter`
```
#### Associated Analytic Story

* Trusted Developer Utilities Proxy Execution MSBuild

* Cobalt Strike

* Masquerading - Rename System Utilities


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.process_path

* Processes.dest

* Processes.parent_process

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1127.001 | MSBuild | Defense Evasion |
| T1036.003 | Rename System Utilities | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Some legitimate applications may use a moved copy of msbuild.exe, triggering a false positive. Baselining of MSBuild.exe usage is recommended to better understand it's path usage. Visual Studio runs an instance out of a path that will need to be filtered on.

#### Reference


* https://lolbas-project.github.io/lolbas/Binaries/Msbuild/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1127.001/T1127.001.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1127.001/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious mshta child process
The following analytic identifies child processes spawning from  "mshta.exe". 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, parent process "mshta.exe" and its child process.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218/005/)
- **Last Updated**: 2021-01-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=mshta.exe AND (Processes.process_name=powershell.exe OR Processes.process_name=colorcpl.exe OR Processes.process_name=msbuild.exe OR Processes.process_name=microsoft.workflow.compiler.exe OR Processes.process_name=searchprotocolhost.exe OR Processes.process_name=scrcons.exe OR Processes.process_name=cscript.exe OR Processes.process_name=wscript.exe OR Processes.process_name=powershell.exe OR Processes.process_name=cmd.exe) by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_mshta_child_process_filter`
```
#### Associated Analytic Story

* Suspicious MSHTA Activity


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.dest

* Processes.parent_process

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.005 | Mshta | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.

#### Reference


* https://github.com/redcanaryco/AtomicTestHarnesses

* https://redcanary.com/blog/introducing-atomictestharnesses/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious mshta spawn
The following analytic identifies wmiprvse.exe spawning mshta.exe. This behavior is indicative of a DCOM object being utilized to spawn mshta from wmiprvse.exe or svchost.exe. In this instance, adversaries may use LethalHTA that will spawn mshta.exe from svchost.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.005](https://attack.mitre.org/techniques/T1218/005/)
- **Last Updated**: 2021-01-20

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.parent_process_name=svchost.exe OR Processes.parent_process_name=wmiprvse.exe) AND Processes.process_name=mshta.exe by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `suspicious_mshta_spawn_filter`
```
#### Associated Analytic Story

* Suspicious MSHTA Activity


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.dest

* Processes.parent_process

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.005 | Mshta | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Although unlikely, some legitimate applications may exhibit this behavior, triggering a false positive.

#### Reference


* https://codewhitesec.blogspot.com/2018/07/lethalhta.html

* https://github.com/redcanaryco/AtomicTestHarnesses

* https://redcanary.com/blog/introducing-atomictestharnesses/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1218.005/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Suspicious wevtutil Usage
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1070.001](https://attack.mitre.org/techniques/T1070/001/)
- **Last Updated**: 2020-07-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = wevtutil.exe Processes.process="*cl*" (Processes.process="*System*" OR Processes.process="*Security*" OR Processes.process="*Setup*" OR Processes.process="*Application*") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
|`security_content_ctime(lastTime)` 
| `suspicious_wevtutil_usage_filter`
```
#### Associated Analytic Story

* Windows Log Manipulation

* Ransomware

* Clop Ransomware


#### 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.

#### Required field

* _time

* Processes.process

* Processes.process_name

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070.001 | Clear Windows Event Logs | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Suspicious writes to windows Recycle Bin
This search detects writes to the recycle bin by a process other than explorer.exe.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/)
- **Last Updated**: 2020-07-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.file_path) as file_path values(Filesystem.file_name) as file_name FROM datamodel=Endpoint.Filesystem where Filesystem.file_path = "*$Recycle.Bin*" by Filesystem.process_id Filesystem.dest 
| `drop_dm_object_name("Filesystem")`
| search [
| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.process_name) as process_name values(Processes.parent_process_name) as parent_process_name FROM datamodel=Endpoint.Processes where Processes.process_name != "explorer.exe" by Processes.process_id Processes.dest
| `drop_dm_object_name("Processes")` 
| table process_id dest] 
| `suspicious_writes_to_windows_recycle_bin_filter`
```
#### Associated Analytic Story

* Collection and Staging


#### 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.

#### Required field

* _time

* Filesystem.file_path

* Filesystem.file_name

* Filesystem.process_id

* Filesystem.dest

* Processes.user

* Processes.process_name

* Processes.parent_process_name

* Processes.process_id

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1036 | Masquerading | Defense Evasion |


#### Kill Chain Phase


#### 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.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/write_to_recycle_bin/windows-sysmon.log


_version_: 4
</details>

---

### System Information Discovery Detection
Detect system information discovery techniques used by attackers to understand configurations of the system to further exploit it.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/)
- **Last Updated**: 2020-10-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*wmic* qfe*" OR Processes.process=*systeminfo* OR Processes.process=*hostname*) by Processes.user Processes.process_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| eventstats dc(process) as dc_processes_by_dest by dest 
| where dc_processes_by_dest > 2 
| stats values(process) min(firstTime) as firstTime max(lastTime) as lastTime by user, dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `system_information_discovery_detection_filter`
```
#### Associated Analytic Story

* Discovery Techniques


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process

* Processes.user

* Processes.process_name

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1082 | System Information Discovery | Discovery |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Administrators debugging servers

#### Reference


* https://oscp.infosecsanyam.in/priv-escalation/windows-priv-escalation



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1082/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### System Process Running from Unexpected Location
An attacker tries might try to use different version of a system command without overriding original, or they might try to avoid some detection running the process from a different folder. This detection checks that a list of system processes run inside C:\\Windows\System32 or C:\\Windows\SysWOW64 The list of system processes has been extracted from https://github.com/splunk/security_content/blob/develop/lookups/is_windows_system_file.csv and the original detection https://github.com/splunk/security_content/blob/develop/detections/system_processes_run_from_unexpected_locations.yml

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: [T1036](https://attack.mitre.org/techniques/T1036/)
- **Last Updated**: 2020-08-25

<details>
  <summary>details</summary>

#### Search
```
 $ssa_input = 
| from read_ssa_enriched_events() 
| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), user=ucast(map_get(input_event, "dest_user_id"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), process_name=lower(ucast(map_get(input_event, "process_name"), "string", null)), process_path=lower(ucast(map_get(input_event, "process_path"), "string", null)), event_id=ucast(map_get(input_event, "event_id"), "string", null);
$cond_1 = 
| from $ssa_input 
| where process_name="arp.exe" OR process_name="adaptertroubleshooter.exe" OR process_name="applicationframehost.exe" OR process_name="atbroker.exe" OR process_name="authhost.exe" OR process_name="autoworkplace.exe" OR process_name="axinstui.exe" OR process_name="backgroundtransferhost.exe" OR process_name="bdehdcfg.exe" OR process_name="bdeuisrv.exe" OR process_name="bdeunlockwizard.exe" OR process_name="bitlockerdeviceencryption.exe" OR process_name="bitlockerwizard.exe" OR process_name="bitlockerwizardelev.exe" OR process_name="bytecodegenerator.exe" OR process_name="camerasettingsuihost.exe" OR process_name="castsrv.exe" OR process_name="certenrollctrl.exe" OR process_name="checknetisolation.exe" OR process_name="clipup.exe" OR process_name="cloudexperiencehostbroker.exe" OR process_name="cloudnotifications.exe" OR process_name="cloudstoragewizard.exe" OR process_name="compmgmtlauncher.exe" OR process_name="compattelrunner.exe" OR process_name="computerdefaults.exe" OR process_name="credentialuibroker.exe" OR process_name="dfdwiz.exe" OR process_name="dwwin.exe" OR process_name="dataexchangehost.exe" OR process_name="defrag.exe" OR process_name="devicedisplayobjectprovider.exe" OR process_name="deviceeject.exe" OR process_name="deviceenroller.exe" OR process_name="devicepairingwizard.exe" OR process_name="deviceproperties.exe" OR process_name="disksnapshot.exe" OR process_name="dism.exe" OR process_name="displayswitch.exe" OR process_name="dmnotificationbroker.exe" OR process_name="dmomacpmo.exe" OR process_name="dpiscaling.exe" OR process_name="dsmusertask.exe" OR process_name="dxpserver.exe" OR process_name="edpcleanup.exe" OR process_name="eosnotify.exe" OR process_name="eap3host.exe" OR process_name="easpoliciesbrokerhost.exe" OR process_name="easeofaccessdialog.exe" OR process_name="ehstorauthn.exe" OR process_name="fxscover.exe" OR process_name="fxssvc.exe" OR process_name="fxsunatd.exe" OR process_name="filehistory.exe" OR process_name="fondue.exe" OR process_name="gamepanel.exe" OR process_name="genvalobj.exe" OR process_name="gettingstarted.exe" OR process_name="hostname.exe" OR process_name="icsentitlementhost.exe" OR process_name="infdefaultinstall.exe" OR process_name="installagent.exe" OR process_name="languagecomponentsinstallercomhandler.exe" OR process_name="launchtm.exe" OR process_name="launchwinapp.exe" OR process_name="legacynetuxhost.exe" OR process_name="licensemanagershellext.exe" OR process_name="licensingui.exe" OR process_name="locationnotificationwindows.exe" OR process_name="locationnotifications.exe" OR process_name="locator.exe" OR process_name="lockapphost.exe" OR process_name="lockscreencontentserver.exe" OR process_name="logonui.exe" OR process_name="lsaiso.exe" OR process_name="mdeserver.exe" OR process_name="mdmagent.exe" OR process_name="mdmappinstaller.exe" OR process_name="mrinfo.exe" OR process_name="mrt.exe" OR process_name="mschedexe.exe" OR process_name="magnify.exe" OR process_name="mbaeparsertask.exe" OR process_name="mdres.exe" OR process_name="mdsched.exe" OR process_name="migautoplay.exe" OR process_name="mpsigstub.exe" OR process_name="msspellcheckinghost.exe" OR process_name="muiunattend.exe" OR process_name="multidigimon.exe" OR process_name="musnotification.exe" OR process_name="musnotificationux.exe" OR process_name="napstat.exe" OR process_name="netstat.exe" OR process_name="narrator.exe" OR process_name="netcfgnotifyobjecthost.exe" OR process_name="netevtfwdr.exe" OR process_name="netproj.exe" OR process_name="netplwiz.exe" OR process_name="networkuxbroker.exe";
$cond_2 = 
| from $ssa_input 
| where process_name="openwith.exe" OR process_name="optionalfeatures.exe" OR process_name="pathping.exe" OR process_name="ping.exe" OR process_name="passwordonwakesettingflyout.exe" OR process_name="pickerhost.exe" OR process_name="pkgmgr.exe" OR process_name="pnpunattend.exe" OR process_name="pnputil.exe" OR process_name="presentationhost.exe" OR process_name="presentationsettings.exe" OR process_name="printbrmui.exe" OR process_name="printdialoghost.exe" OR process_name="printdialoghost3d.exe" OR process_name="printisolationhost.exe" OR process_name="proximityuxhost.exe" OR process_name="rdspnf.exe" OR process_name="rmactivate.exe" OR process_name="rmactivate_isv.exe" OR process_name="rmactivate_ssp.exe" OR process_name="rmactivate_ssp_isv.exe" OR process_name="route.exe" OR process_name="rdpsa.exe" OR process_name="rdpsaproxy.exe" OR process_name="rdpsauachelper.exe" OR process_name="reagentc.exe" OR process_name="recoverydrive.exe" OR process_name="register-cimprovider.exe" OR process_name="registeriepkeys.exe" OR process_name="relpost.exe" OR process_name="remoteposworker.exe" OR process_name="rmclient.exe" OR process_name="robocopy.exe" OR process_name="rpcping.exe" OR process_name="runlegacycplelevated.exe" OR process_name="runtimebroker.exe" OR process_name="sihclient.exe" OR process_name="searchfilterhost.exe" OR process_name="searchindexer.exe" OR process_name="searchprotocolhost.exe" OR process_name="secedit.exe" OR process_name="sensordataservice.exe" OR process_name="setieinstalleddate.exe" OR process_name="settingsynchost.exe" OR process_name="slidetoshutdown.exe" OR process_name="smartscreensettings.exe" OR process_name="sndvol.exe" OR process_name="snippingtool.exe" OR process_name="soundrecorder.exe" OR process_name="spaceagent.exe" OR process_name="sppextcomobj.exe" OR process_name="srtasks.exe" OR process_name="stikynot.exe" OR process_name="synchost.exe" OR process_name="sysreseterr.exe" OR process_name="systempropertiesadvanced.exe" OR process_name="systempropertiescomputername.exe" OR process_name="systempropertiesdataexecutionprevention.exe" OR process_name="systempropertieshardware.exe" OR process_name="systempropertiesperformance.exe" OR process_name="systempropertiesprotection.exe" OR process_name="systempropertiesremote.exe" OR process_name="systemsettingsadminflows.exe" OR process_name="systemsettingsbroker.exe" OR process_name="systemsettingsremovedevice.exe" OR process_name="tcpsvcs.exe" OR process_name="tracert.exe" OR process_name="tstheme.exe" OR process_name="tswbprxy.exe" OR process_name="tapiunattend.exe" OR process_name="taskmgr.exe" OR process_name="thumbnailextractionhost.exe" OR process_name="tokenbrokercookies.exe" OR process_name="tpminit.exe" OR process_name="tswpfwrp.exe" OR process_name="ui0detect.exe" OR process_name="upgraderesultsui.exe" OR process_name="useraccountbroker.exe" OR process_name="useraccountcontrolsettings.exe" OR process_name="usoclient.exe" OR process_name="utilman.exe" OR process_name="vssvc.exe" OR process_name="vaultcmd.exe" OR process_name="vaultsysui.exe" OR process_name="wfs.exe" OR process_name="wmpdmc.exe" OR process_name="wpdshextautoplay.exe" OR process_name="wscollect.exe" OR process_name="wsmanhttpconfig.exe" OR process_name="wsreset.exe" OR process_name="wudfhost.exe" OR process_name="wwahost.exe" OR process_name="wallpaperhost.exe" OR process_name="webcache.exe" OR process_name="werfault.exe" OR process_name="werfaultsecure.exe" OR process_name="winsat.exe" OR process_name="windows.media.backgroundplayback.exe" OR process_name="windowsactiondialog.exe" OR process_name="windowsanytimeupgrade.exe" OR process_name="windowsanytimeupgraderesults.exe";
$cond_3 = 
| from $ssa_input 
| where process_name="windowsanytimeupgradeui.exe" OR process_name="windowsupdateelevatedinstaller.exe" OR process_name="workfolders.exe" OR process_name="wpcmon.exe" OR process_name="acu.exe" OR process_name="aitagent.exe" OR process_name="aitstatic.exe" OR process_name="alg.exe" OR process_name="appidcertstorecheck.exe" OR process_name="appidpolicyconverter.exe" OR process_name="at.exe" OR process_name="attrib.exe" OR process_name="audiodg.exe" OR process_name="auditpol.exe" OR process_name="autochk.exe" OR process_name="autoconv.exe" OR process_name="autofmt.exe" OR process_name="baaupdate.exe" OR process_name="backgroundtaskhost.exe" OR process_name="bcastdvr.exe" OR process_name="bcdboot.exe" OR process_name="bcdedit.exe" OR process_name="bdechangepin.exe" OR process_name="bdeunlock.exe" OR process_name="bitsadmin.exe" OR process_name="bootcfg.exe" OR process_name="bootim.exe" OR process_name="bootsect.exe" OR process_name="bridgeunattend.exe" OR process_name="browser_broker.exe" OR process_name="bthudtask.exe" OR process_name="cacls.exe" OR process_name="calc.exe" OR process_name="cdpreference.exe" OR process_name="certreq.exe" OR process_name="certutil.exe" OR process_name="change.exe" OR process_name="changepk.exe" OR process_name="charmap.exe" OR process_name="chglogon.exe" OR process_name="chgport.exe" OR process_name="chgusr.exe" OR process_name="chkdsk.exe" OR process_name="chkntfs.exe" OR process_name="choice.exe" OR process_name="cipher.exe" OR process_name="cleanmgr.exe" OR process_name="cliconfg.exe" OR process_name="clip.exe" OR process_name="cmd.exe" OR process_name="cmdkey.exe" OR process_name="cmdl32.exe" OR process_name="cmmon32.exe" OR process_name="cmstp.exe" OR process_name="cofire.exe" OR process_name="colorcpl.exe" OR process_name="comp.exe" OR process_name="compact.exe" OR process_name="conhost.exe" OR process_name="consent.exe" OR process_name="control.exe" OR process_name="convert.exe" OR process_name="credwiz.exe" OR process_name="cscript.exe" OR process_name="csrss.exe" OR process_name="ctfmon.exe" OR process_name="cttune.exe" OR process_name="cttunesvr.exe" OR process_name="dashost.exe" OR process_name="dccw.exe" OR process_name="dcomcnfg.exe" OR process_name="ddodiag.exe" OR process_name="dfrgui.exe" OR process_name="dialer.exe" OR process_name="diantz.exe" OR process_name="dinotify.exe" OR process_name="diskpart.exe" OR process_name="diskperf.exe" OR process_name="diskraid.exe" OR process_name="dispdiag.exe" OR process_name="djoin.exe" OR process_name="dllhost.exe" OR process_name="dllhst3g.exe" OR process_name="dmcertinst.exe" OR process_name="dmcfghost.exe" OR process_name="dmclient.exe" OR process_name="dnscacheugc.exe" OR process_name="doskey.exe" OR process_name="dpapimig.exe" OR process_name="dpnsvr.exe" OR process_name="driverquery.exe" OR process_name="drvcfg.exe" OR process_name="drvinst.exe" OR process_name="dsregcmd.exe" OR process_name="dstokenclean.exe" OR process_name="dvdplay.exe" OR process_name="dvdupgrd.exe" OR process_name="dwm.exe" OR process_name="dxdiag.exe" OR process_name="easinvoker.exe" OR process_name="efsui.exe";
$cond_4 = 
| from $ssa_input 
| where process_name="embeddedapplauncher.exe" OR process_name="esentutl.exe" OR process_name="eudcedit.exe" OR process_name="eventcreate.exe" OR process_name="eventvwr.exe" OR process_name="expand.exe" OR process_name="extrac32.exe" OR process_name="fc.exe" OR process_name="fhmanagew.exe" OR process_name="find.exe" OR process_name="findstr.exe" OR process_name="finger.exe" OR process_name="fixmapi.exe" OR process_name="fltmc.exe" OR process_name="fodhelper.exe" OR process_name="fontdrvhost.exe" OR process_name="fontview.exe" OR process_name="forfiles.exe" OR process_name="fsavailux.exe" OR process_name="fsquirt.exe" OR process_name="fsutil.exe" OR process_name="ftp.exe" OR process_name="fvenotify.exe" OR process_name="fveprompt.exe" OR process_name="getmac.exe" OR process_name="gpresult.exe" OR process_name="gpscript.exe" OR process_name="gpupdate.exe" OR process_name="grpconv.exe" OR process_name="hdwwiz.exe" OR process_name="help.exe" OR process_name="hwrcomp.exe" OR process_name="hwrreg.exe" OR process_name="icacls.exe" OR process_name="icardagt.exe" OR process_name="icsunattend.exe" OR process_name="ie4uinit.exe" OR process_name="ieunatt.exe" OR process_name="ieetwcollector.exe" OR process_name="iexpress.exe" OR process_name="immersivetpmvscmgrsvr.exe" OR process_name="ipconfig.exe" OR process_name="irftp.exe" OR process_name="iscsicli.exe" OR process_name="iscsicpl.exe" OR process_name="isoburn.exe" OR process_name="klist.exe" OR process_name="ksetup.exe" OR process_name="ktmutil.exe" OR process_name="label.exe" OR process_name="licensingdiag.exe" OR process_name="lodctr.exe" OR process_name="logagent.exe" OR process_name="logman.exe" OR process_name="logoff.exe" OR process_name="lpkinstall.exe" OR process_name="lpksetup.exe" OR process_name="lpremove.exe" OR process_name="lsass.exe" OR process_name="lsm.exe" OR process_name="makecab.exe" OR process_name="manage-bde.exe" OR process_name="mblctr.exe" OR process_name="mcbuilder.exe" OR process_name="mctadmin.exe" OR process_name="mfpmp.exe" OR process_name="mmc.exe" OR process_name="mobsync.exe" OR process_name="mountvol.exe" OR process_name="mpnotify.exe" OR process_name="msconfig.exe" OR process_name="msdt.exe" OR process_name="msdtc.exe" OR process_name="msfeedssync.exe" OR process_name="msg.exe" OR process_name="mshta.exe" OR process_name="msiexec.exe" OR process_name="msinfo32.exe" OR process_name="mspaint.exe" OR process_name="msra.exe" OR process_name="mstsc.exe" OR process_name="mtstocom.exe" OR process_name="nbtstat.exe" OR process_name="ndadmin.exe" OR process_name="net.exe" OR process_name="net1.exe" OR process_name="netbtugc.exe" OR process_name="netcfg.exe" OR process_name="netiougc.exe" OR process_name="netsh.exe" OR process_name="newdev.exe" OR process_name="nltest.exe" OR process_name="notepad.exe" OR process_name="nslookup.exe" OR process_name="ntoskrnl.exe" OR process_name="ntprint.exe" OR process_name="ocsetup.exe" OR process_name="odbcad32.exe" OR process_name="odbcconf.exe" OR process_name="omadmclient.exe" OR process_name="omadmprc.exe";
$cond_5 = 
| from $ssa_input 
| where process_name="openfiles.exe" OR process_name="osk.exe" OR process_name="p2phost.exe" OR process_name="pcalua.exe" OR process_name="pcaui.exe" OR process_name="pcawrk.exe" OR process_name="pcwrun.exe" OR process_name="perfmon.exe" OR process_name="phoneactivate.exe" OR process_name="plasrv.exe" OR process_name="poqexec.exe" OR process_name="powercfg.exe" OR process_name="prevhost.exe" OR process_name="print.exe" OR process_name="printfilterpipelinesvc.exe" OR process_name="printui.exe" OR process_name="proquota.exe" OR process_name="provtool.exe" OR process_name="psr.exe" OR process_name="pwlauncher.exe" OR process_name="qappsrv.exe" OR process_name="qprocess.exe" OR process_name="query.exe" OR process_name="quser.exe" OR process_name="qwinsta.exe" OR process_name="rasautou.exe" OR process_name="rasdial.exe" OR process_name="raserver.exe" OR process_name="rasphone.exe" OR process_name="rdpclip.exe" OR process_name="rdpinput.exe" OR process_name="rdrleakdiag.exe" OR process_name="recdisc.exe" OR process_name="recover.exe" OR process_name="reg.exe" OR process_name="regedt32.exe" OR process_name="regini.exe" OR process_name="regsvr32.exe" OR process_name="rekeywiz.exe" OR process_name="relog.exe" OR process_name="repair-bde.exe" OR process_name="replace.exe" OR process_name="reset.exe" OR process_name="resmon.exe" OR process_name="rmttpmvscmgrsvr.exe" OR process_name="rrinstaller.exe" OR process_name="rstrui.exe" OR process_name="runas.exe" OR process_name="rundll32.exe" OR process_name="runonce.exe" OR process_name="rwinsta.exe" OR process_name="sbunattend.exe" OR process_name="sc.exe" OR process_name="schtasks.exe" OR process_name="sdbinst.exe" OR process_name="sdchange.exe" OR process_name="sdclt.exe" OR process_name="sdiagnhost.exe" OR process_name="secinit.exe" OR process_name="services.exe" OR process_name="sessionmsg.exe" OR process_name="sethc.exe" OR process_name="setspn.exe" OR process_name="setupcl.exe" OR process_name="setupugc.exe" OR process_name="setx.exe" OR process_name="sfc.exe" OR process_name="shadow.exe" OR process_name="shrpubw.exe" OR process_name="shutdown.exe" OR process_name="sigverif.exe" OR process_name="sihost.exe" OR process_name="slui.exe" OR process_name="smss.exe" OR process_name="snmptrap.exe" OR process_name="sort.exe" OR process_name="spinstall.exe" OR process_name="spoolsv.exe" OR process_name="sppsvc.exe" OR process_name="spreview.exe" OR process_name="srdelayed.exe" OR process_name="subst.exe" OR process_name="svchost.exe" OR process_name="sxstrace.exe" OR process_name="syskey.exe" OR process_name="systeminfo.exe" OR process_name="systemreset.exe" OR process_name="systray.exe" OR process_name="tabcal.exe" OR process_name="takeown.exe" OR process_name="taskeng.exe" OR process_name="taskhost.exe" OR process_name="taskhostw.exe" OR process_name="taskkill.exe" OR process_name="tasklist.exe" OR process_name="taskmgr.exe" OR process_name="tcmsetup.exe" OR process_name="timeout.exe" OR process_name="tpmvscmgr.exe" OR process_name="tpmvscmgrsvr.exe";
$cond_6 = 
| from $ssa_input 
| where process_name="tracerpt.exe" OR process_name="tscon.exe" OR process_name="tsdiscon.exe" OR process_name="tskill.exe" OR process_name="typeperf.exe" OR process_name="tzsync.exe" OR process_name="tzutil.exe" OR process_name="ucsvc.exe" OR process_name="unlodctr.exe" OR process_name="unregmp2.exe" OR process_name="upnpcont.exe" OR process_name="userinit.exe" OR process_name="vds.exe" OR process_name="vdsldr.exe" OR process_name="verclsid.exe" OR process_name="verifier.exe" OR process_name="verifiergui.exe" OR process_name="vmicsvc.exe" OR process_name="vssadmin.exe" OR process_name="w32tm.exe" OR process_name="waitfor.exe" OR process_name="wbadmin.exe" OR process_name="wbengine.exe" OR process_name="wecutil.exe" OR process_name="wermgr.exe" OR process_name="wevtutil.exe" OR process_name="wextract.exe" OR process_name="where.exe" OR process_name="whoami.exe" OR process_name="wiaacmgr.exe" OR process_name="wiawow64.exe" OR process_name="wifitask.exe" OR process_name="wimserv.exe" OR process_name="wininit.exe" OR process_name="winload.exe" OR process_name="winlogon.exe" OR process_name="winresume.exe" OR process_name="winrs.exe" OR process_name="winrshost.exe" OR process_name="winver.exe" OR process_name="wisptis.exe" OR process_name="wkspbroker.exe" OR process_name="wksprt.exe" OR process_name="wlanext.exe" OR process_name="wlrmdr.exe" OR process_name="wowreg32.exe" OR process_name="wpnpinst.exe" OR process_name="wpr.exe" OR process_name="write.exe" OR process_name="wscript.exe" OR process_name="wsmprovhost.exe" OR process_name="wsqmcons.exe" OR process_name="wuapihost.exe" OR process_name="wuapp.exe" OR process_name="wuauclt.exe" OR process_name="wusa.exe" OR process_name="xcopy.exe" OR process_name="xpsrchvw.exe" OR process_name="xwizard.exe";

| from $cond_1 
| union $cond_2 
| union $cond_3 
| union $cond_4 
| union $cond_5 
| union $cond_6 
| where match_regex(process_path, /(?i)\\windows\\system32/)=false AND match_regex(process_path, /(?i)\\windows\\syswow64/)=false 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(device, user), body=create_map(["event_id", event_id, "process_path", process_path, "process_name", process_name]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics

* Masquerading - Rename System Utilities


#### How To Implement
Collect endpoint data such as sysmon or 4688 events.

#### Required field

* dest_device_id

* process_name

* _time

* dest_user_id

* process_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1036 | Masquerading | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036/system_process_running_unexpected_location/windows-security.log


_version_: 3
</details>

---

### System Processes Run From Unexpected Locations
This search looks for system processes that typically execute from `C:\Windows\System32\` or `C:\Windows\SysWOW64`.  This may indicate a malicious process that is trying to hide as a legitimate process.\
This detection utilizes a lookup that is deduped `system32` and `syswow64` directories from Server 2016 and Windows 10.\
During triage, review the parallel processes - what process moved the native Windows binary? identify any artifacts on disk and review. If a remote destination is contacted, what is the reputation?

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1036.003](https://attack.mitre.org/techniques/T1036/003/)
- **Last Updated**: 2020-12-08

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes where Processes.process_path !="C:\\Windows\\System32*" Processes.process_path !="C:\\Windows\\SysWOW64*" by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_hash 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `is_windows_system_file` 
| `system_processes_run_from_unexpected_locations_filter`
```
#### Associated Analytic Story

* Suspicious Command-Line Executions

* Unusual Processes

* Ransomware

* Masquerading - Rename System Utilities


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_path

* Processes.user

* Processes.dest

* Processes.process_name

* Processes.process_id

* Processes.parent_process_name

* Processes.process_hash



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1036.003 | Rename System Utilities | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
This detection may require tuning based on third party applications utilizing native Windows binaries in non-standard paths.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1036.003/T1036.003.yaml

* https://attack.mitre.org/techniques/T1036/003/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log


_version_: 6
</details>

---

### Systems Ready for Spectre-Meltdown Windows Patch
Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Change
- **ATT&CK**: 
- **Last Updated**: 2018-01-08

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `drop_dm_object_name("All_Changes")`
```
#### Associated Analytic Story

* Spectre And Meltdown Vulnerabilities


#### 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.

#### Required field

* _time

* All_Changes.object_category

* All_Changes.object_path

* All_Changes.dest

* All_Changes.command

* All_Changes.user

* All_Changes.object




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### TOR Traffic
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Network_Traffic
- **ATT&CK**: [T1071.001](https://attack.mitre.org/techniques/T1071/001/)
- **Last Updated**: 2020-07-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=tor AND All_Traffic.action=allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `drop_dm_object_name("All_Traffic")` 
| `tor_traffic_filter`
```
#### Associated Analytic Story

* Prohibited Traffic Allowed or Protocol Mismatch

* Ransomware

* Command and Control

* NOBELIUM Group


#### 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.

#### Required field

* _time

* All_Traffic.app

* All_Traffic.action

* All_Traffic.src_ip

* All_Traffic.dest_ip

* All_Traffic.dest_port



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1071.001 | Web Protocols | Command And Control |


#### Kill Chain Phase

* Command and Control


#### Known False Positives
None at this time

#### Reference


#### Test Dataset


_version_: 2
</details>

---

### Trickbot Named Pipe
this search is to detect potential trickbot infection through the create/connected named pipe to the system. This technique is used by trickbot to communicate to its c2 to post or get command during infection.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1055](https://attack.mitre.org/techniques/T1055/)
- **Last Updated**: 2021-04-26

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode IN (17,18) PipeName="\\pipe\\*lacesomepipe" 
| stats  min(_time) as firstTime max(_time) as lastTime count by Computer user_id EventCode PipeName signature Image process_id 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `trickbot_named_pipe_filter`
```
#### Associated Analytic Story

* Trickbot


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name and pipename from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. .

#### Required field

* _time

* Computer

* user_id

* EventCode

* PipeName

* signature

* Image

* process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1055 | Process Injection | Defense Evasion, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://labs.vipre.com/trickbot-and-its-modules/

* https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/namedpipe/windows-sysmon.log


_version_: 1
</details>

---

### UAC Bypass MMC Load Unsigned Dll
This search is to detect a suspicious loaded unsigned dll by MMC.exe application. This technique is commonly seen in attacker that tries to bypassed UAC feature or gain privilege escalation. This is done by modifying some CLSID registry that will trigger the mmc.exe to load the dll path

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548/002/)
- **Last Updated**: 2021-07-12

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=7  ImageLoaded = "*.dll" Image = "*\\mmc.exe" Signed=false Company != "Microsoft Corporation" 
| stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded Signed ProcessId OriginalFileName Computer EventCode Company 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `uac_bypass_mmc_load_unsigned_dll_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* Image

* ImageLoaded

* Signed

* ProcessId

* OriginalFileName

* Computer

* EventCode

* Company



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548.002 | Bypass User Account Control | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown. all of the dll loaded by mmc.exe is microsoft signed dll.

#### Reference


* https://offsec.almond.consulting/UAC-bypass-dotnet.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon2.log


_version_: 1
</details>

---

### USN Journal Deletion
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1070](https://attack.mitre.org/techniques/T1070/)
- **Last Updated**: 2018-12-03

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest  
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| search process="*deletejournal*" AND process="*usn*" 
| `usn_journal_deletion_filter`
```
#### Associated Analytic Story

* Windows Log Manipulation

* Ransomware


#### 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.

#### Required field

* _time

* Processes.process

* Processes.parent_process

* Processes.process_name

* Processes.user

* Processes.parent_process_name

* Processes.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070 | Indicator Removal on Host | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
None identified

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070/atomic_red_team/windows-sysmon.log


_version_: 2
</details>

---

### Unified Messaging Service Spawning a Process
This detection identifies Microsoft Exchange Server's Unified Messaging services, umworkerprocess.exe and umservice.exe, spawning a child process, indicating possible exploitation of CVE-2021-26857 vulnerability. The query filters out werfault.exe and wermgr.exe mostly due to potential false positives, however, if there is an excessive amount of "wermgr.exe" or "WerFault.exe" failures, it may be due to the active exploitation. During triage, identify any additional suspicious parallel processes. Identify any recent out of place file modifications. Review Exchange logs following Microsofts guide. To contain, perform egress filtering or restrict public access to Exchange. In final, patch the vulnerablity and monitor.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/)
- **Last Updated**: 2021-03-02

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="umworkerprocess.exe" OR Processes.parent_process_name="UMService.exe" (Processes.process_name!="wermgr.exe" OR Processes.process_name!="werfault.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `unified_messaging_service_spawning_a_process_filter`
```
#### Associated Analytic Story

* HAFNIUM Group


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1190 | Exploit Public-Facing Application | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Unknown. Tune out child processes as needed to limit volume of false positives.

#### Reference


* https://www.volexity.com/blog/2021/03/02/active-exploitation-of-microsoft-exchange-zero-day-vulnerabilities/

* https://www.microsoft.com/security/blog/2021/03/02/hafnium-targeting-exchange-servers/

* https://blog.rapid7.com/2021/03/03/rapid7s-insightidr-enables-detection-and-response-to-microsoft-exchange-0-day/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon_umservices.log


_version_: 1
</details>

---

### Unload Sysmon Filter Driver
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2020-07-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fltMC.exe AND Processes.process=*unload* AND Processes.process=*SysmonDrv*  by Processes.process_name Processes.process_id Processes.parent_process_name Processes.process Processes.dest Processes.user 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
|`security_content_ctime(lastTime)` 
|`unload_sysmon_filter_driver_filter`
| table firstTime lastTime dest user count process_name process_id parent_process_name process
```
#### Associated Analytic Story

* Disabling Security Tools


#### 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.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives


#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### Unloading AMSI via Reflection
The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify suspicious PowerShell execution. Script Block Logging captures the command sent to PowerShell, the full command to be executed. Upon enabling, logs will output to Windows event logs. Dependent upon volume, enable no critical endpoints or all. \
This analytic identifies the behavior of AMSI being tampered with. Implemented natively in many frameworks, the command will look similar to `SEtValuE($Null,(New-OBJEct COLlECtionS.GenerIC.HAshSEt{[StrINg]))}$ReF=[ReF].AsSeMbLY.GeTTyPe("System.Management.Automation.Amsi"+"Utils")` taken from Powershell-Empire. \
During triage, review parallel processes using an EDR product or 4688 events. It will be important to understand the timeline of events around this activity. Review the entire logged PowerShell script block.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1562](https://attack.mitre.org/techniques/T1562/)
- **Last Updated**: 2021-06-09

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message=*system.management.automation.amsi* 
| stats count min(_time) as firstTime max(_time) as lastTime by OpCode ComputerName User EventCode Message 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `unloading_amsi_via_reflection_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* Message

* OpCode

* ComputerName

* User

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562 | Impair Defenses | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Potential for some third party applications to disable AMSI upon invocation. Filter as needed.

#### Reference


* https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

* https://blog.palantir.com/tampering-with-windows-event-tracing-background-offense-and-defense-4be7ac62ac63

* https://static1.squarespace.com/static/552092d5e4b0661088167e5c/t/59c1814829f18782e24f1fe2/1505853768977/Windows+PowerShell+Logging+Cheat+Sheet+ver+Sept+2017+v2.1.pdf

* https://www.crowdstrike.com/blog/investigating-powershell-command-and-script-logging/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1059.001/powershell_script_block_logging/windows-powershell.log


_version_: 1
</details>

---

### Unusually Long Command Line
Command lines that are extremely long may be indicative of malicious activity on your hosts. This search leverages the Splunk Streaming ML DSP plugin to help identify command lines with lengths that are unusual for a given user. This detection is inspired on Unusually Long Command Line authored by Rico Valdez.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-10-06

<details>
  <summary>details</summary>

#### Search
```
 
| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval cmd_line=ucast(map_get(input_event, "process"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line!=null and dest_user_id!=null 
| eval cmd_line_norm=replace(cast(cmd_line, "string"), /\s(--?\w+)
|(\/\w+)/, " ARG"), cmd_line_norm=replace(cmd_line_norm, /\w:\\[^\s]+/, "PATH"), cmd_line_norm=replace(cmd_line_norm, /\d+/, "N"), input=parse_double(len(coalesce(cmd_line_norm, ""))) 
| select timestamp, process_name, dest_device_id, dest_user_id, cmd_line, input 
| adaptive_threshold algorithm="quantile" entity="process_name" window=60480000 
| where label AND quantile>0.99 
| first_time_event input_columns=["dest_device_id", "cmd_line"] 
| where first_time_dest_device_id_cmd_line 
| eval start_time = timestamp, end_time = timestamp, entities = mvappend(dest_device_id, dest_user_id), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Unusual Processes


#### How To Implement
You must be ingesting sysmon endpoint data that monitors command lines.

#### Required field

* process_name

* _time

* dest_device_id

* dest_user_id

* process




#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
This detection may flag suspiciously long command lines when there is not sufficient evidence (samples) for a given process that this detection is tracking; or when there is high variability in the length of the command line for the tracked process. Also, some legitimate applications may use long command lines. Such is the case of Ansible, that encodes Powershell scripts using long base64. Attackers may use this technique to obfuscate their payloads.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Unusually Long Command Line
Command lines that are extremely long may be indicative of malicious activity on your hosts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2020-12-08

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process 
| `drop_dm_object_name("Processes")` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
|  eval processlen=len(process) 
| eventstats stdev(processlen) as stdev, avg(processlen) as avg by dest 
| stats max(processlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, user, process_name, process 
| `unusually_long_command_line_filter` 
|eval threshold = 3 
| where maxlen > ((threshold*stdevperhost) + avgperhost)
```
#### Associated Analytic Story

* Suspicious Command-Line Executions

* Unusual Processes

* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

* Ransomware


#### 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.

#### Required field

* _time

* Processes.user

* Processes.dest

* Processes.process_name

* Processes.process




#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Some legitimate applications start with long command lines.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1036.003/atomic_red_team/windows-sysmon.log


_version_: 5
</details>

---

### Unusually Long Command Line - MLTK
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.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2019-05-08

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes by Processes.user Processes.dest Processes.process_name Processes.process 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| eval processlen=len(process) 
| search user!=unknown 
| apply cmdline_pdfmodel threshold=0.01 
| rename "IsOutlier(processlen)" as isOutlier 
| search isOutlier > 0 
| table firstTime lastTime user dest process_name process processlen count 
| `unusually_long_command_line___mltk_filter`
```
#### Associated Analytic Story

* Suspicious Command-Line Executions

* Unusual Processes

* Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns

* Ransomware


#### 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.

#### Required field

* _time

* Processes.user

* Processes.dest

* Processes.process_name

* Processes.process




#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Unusually Long Content-Type Length
This search looks for unusually long strings in the Content-Type http header that the client sends the server.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-10-13

<details>
  <summary>details</summary>

#### Search
```
`stream_http` 
| eval cs_content_type_length = len(cs_content_type) 
| where cs_content_type_length > 100 
| table endtime src_ip dest_ip cs_content_type_length cs_content_type url 
| `unusually_long_content_type_length_filter`
```
#### Associated Analytic Story

* Apache Struts Vulnerability


#### 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.

#### Required field

* _time

* cs_content_type

* endtime

* src_ip

* dest_ip

* url




#### Kill Chain Phase

* Delivery


#### Known False Positives
Very few legitimate Content-Type fields will have a length greater than 100 characters.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Update previously seen users in CloudTrail
This search looks for CloudTrail events where a user logs into the console, then updates the baseline of the latest and earliest times, City, Region, and Country we have encountered this user in our dataset, grouped by ARN, within the last hour. NOTE - This baseline search is deprecated and has been updated to use the Authentication Datamodel

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2018-04-30

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` eventName=ConsoleLogin 
| rename userIdentity.arn as user 
| iplocation src 
| eval City=if(City LIKE "",src,City),Region=if(Region LIKE "",src,Region) 
| stats earliest(_time) AS firstTime latest(_time) AS lastTime by user src City Region Country 
| inputlookup append=t previously_seen_users_console_logins_cloudtrail 
| stats min(firstTime) as firstTime max(lastTime) as lastTime by user src City Region Country 
| outputlookup previously_seen_users_console_logins_cloudtrail
```
#### Associated Analytic Story

* Suspicious AWS Login Activities


#### How To Implement
You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Please validate the user name entries in `previously_seen_users_console_logins_cloudtrail`, which is a lookup file created as a result of running this support search.

#### Required field

* _time

* eventName

* userIdentity.arn

* src




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### W3WP Spawning Shell
This query identifies a shell, PowerShell.exe or Cmd.exe, spawning from W3WP.exe, or IIS. In addition to IIS logs, this behavior with an EDR product will capture potential webshell activity, similar to the HAFNIUM Group abusing CVEs, on publicly available Exchange mail servers. During triage, review the parent process and child process of the shell being spawned. Review the command-line arguments and any file modifications that may occur. Identify additional parallel process, child processes, that may highlight further commands executed. After triaging, work to contain the threat and patch the system that is vulnerable.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1505.003](https://attack.mitre.org/techniques/T1505/003/)
- **Last Updated**: 2021-03-03

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=w3wp.exe AND Processes.process_name=cmd.exe OR Processes.process_name=powershell.exe by Processes.dest Processes.parent_process Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `w3wp_spawning_shell_filter`
```
#### Associated Analytic Story

* HAFNIUM Group


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user

* Processes.parent_process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1505.003 | Web Shell | Persistence |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
Baseline your environment before production. It is possible build systems using IIS will spawn cmd.exe to perform a software build. Filter as needed.

#### Reference


* https://www.microsoft.com/security/blog/2020/02/04/ghost-in-the-shell-investigating-web-shell-attacks/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1505.003/windows-sysmon.log


_version_: 1
</details>

---

### WBAdmin Delete System Backups
This search looks for flags passed to wbadmin.exe (Windows Backup Administrator Tool) that delete backup files. This is typically used by ransomware to prevent recovery.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1490](https://attack.mitre.org/techniques/T1490/)
- **Last Updated**: 2021-01-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wbadmin.exe Processes.process="*delete*" AND (Processes.process="*catalog*" OR Processes.process="*systemstatebackup*") by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `wbadmin_delete_system_backups_filter`
```
#### Associated Analytic Story

* Ryuk Ransomware

* Ransomware


#### 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. Tune based on parent process names.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.parent_process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1490 | Inhibit System Recovery | Impact |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Administrators may modify the boot configuration.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1490/T1490.md

* https://thedfirreport.com/2020/10/08/ryuks-return/

* https://attack.mitre.org/techniques/T1490/

* https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/wbadmin



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1490/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### WMI Permanent Event Subscription
This search looks for the creation of WMI permanent event subscriptions.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/)
- **Last Updated**: 2018-10-23

<details>
  <summary>details</summary>

#### Search
```
`wmi` EventCode=5861 Binding 
| rex field=Message "Consumer =\s+(?<consumer>[^;
|^$]+)" 
| search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" 
| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| rename ComputerName as dest 
| `wmi_permanent_event_subscription_filter`
```
#### Associated Analytic Story

* Suspicious WMI Use


#### 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].

#### Required field

* _time

* EventCode

* Message

* consumer

* ComputerName



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1047 | Windows Management Instrumentation | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, administrators may use event subscriptions for legitimate purposes.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### WMI Permanent Event Subscription - Sysmon
This analytic looks for the creation of WMI permanent event subscriptions. The following analytic identifies the use of WMI Event Subscription to establish persistence or perform privilege escalation.  WMI can be used to install event filters, providers, consumers, and bindings that execute code when a defined event occurs. WMI subscription execution is proxied by the WMI Provider Host process (WmiPrvSe.exe) and thus may result in elevated SYSTEM privileges. This analytic is restricted by commonly added process execution and a path. If the volume is low enough, remove the values and flag on any new subscriptions.\
All event subscriptions have three components \
1. Filter - WQL Query for the events we want. EventID = 19 \
1. Consumer - An action to take upon triggering the filter. EventID = 20 \
1. Binding - Registers a filter to a consumer. EventID = 21 \
Monitor for the creation of new WMI EventFilter, EventConsumer, and FilterToConsumerBinding. It may be pertinent to review all 3 to identify the flow of execution. In addition, EventCode 4104 may assist with any other PowerShell script usage that registered the subscription.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1546.003](https://attack.mitre.org/techniques/T1546/003/)
- **Last Updated**: 2020-12-08

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=21 
| rename host as dest 
| table _time, dest, user, Operation, EventType, Query, Consumer, Filter 
| `wmi_permanent_event_subscription___sysmon_filter`
```
#### Associated Analytic Story

* Suspicious WMI Use


#### 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 (eventID= 19, 20, 21). In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields.

#### Required field

* _time

* EventCode

* host

* user

* Operation

* EventType

* Query

* Consumer

* Filter



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1546.003 | Windows Management Instrumentation Event Subscription | Privilege Escalation, Persistence |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Although unlikely, administrators may use event subscriptions for legitimate purposes.

#### Reference


* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1546.003/T1546.003.md

* https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/

* https://github.com/trustedsec/SysmonCommunityGuide/blob/master/WMI-events.md

* https://in.security/an-intro-into-abusing-and-identifying-wmi-event-subscriptions-for-persistence/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.003/atomic_red_team/windows-sysmon.log


_version_: 3
</details>

---

### WMI Recon Running Process Or Services
The following analytic identifies suspicious PowerShell script execution via EventCode 4104, where WMI is performing an event query looking for running processes or running services. This technique is commonly found in malware and APT events where the adversary will map all running security applications or services on the compromised machine. During triage, review parallel processes within the same timeframe. Review the full script block to identify other related artifacts.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1592](https://attack.mitre.org/techniques/T1592/)
- **Last Updated**: 2021-06-14

<details>
  <summary>details</summary>

#### Search
```
`powershell` EventCode=4104 Message= "*SELECT*" AND (Message="*Win32_Process*" OR Message="*Win32_Service*") 
| stats count min(_time) as firstTime max(_time) as lastTime by EventCode Message ComputerName User 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `wmi_recon_running_process_or_services_filter`
```
#### Associated Analytic Story

* Malicious PowerShell


#### How To Implement
To successfully implement this analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/5.0.4.1/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.

#### Required field

* _time

* EventCode

* Message

* ComputerName

* User



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1592 | Gather Victim Host Information | Reconnaissance |


#### Kill Chain Phase

* Reconnaissance


#### Known False Positives
network administrator may used this command for checking purposes

#### Reference


* https://news.sophos.com/en-us/2020/05/12/maze-ransomware-1-year-counting/

* https://www.eideon.com/2018-03-02-THL03-WMIBackdoors/

* https://github.com/trustedsec/SysmonCommunityGuide/blob/master/WMI-events.md

* https://in.security/an-intro-into-abusing-and-identifying-wmi-event-subscriptions-for-persistence/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/honeypots/pwsh/windows-powershell.log


_version_: 1
</details>

---

### WMI Temporary Event Subscription
This search looks for the creation of WMI temporary event subscriptions.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1047](https://attack.mitre.org/techniques/T1047/)
- **Last Updated**: 2018-10-23

<details>
  <summary>details</summary>

#### Search
```
`wmi` EventCode=5860 Temporary 
| rex field=Message "NotificationQuery =\s+(?<query>[^;
|^$]+)" 
| search query!="SELECT * FROM Win32_ProcessStartTrace WHERE ProcessName = 'wsmprovhost.exe'" AND query!="SELECT * FROM __InstanceOperationEvent WHERE TargetInstance ISA 'AntiVirusProduct' OR TargetInstance ISA 'FirewallProduct' OR TargetInstance ISA 'AntiSpywareProduct'" 
| stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, query  
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `wmi_temporary_event_subscription_filter`
```
#### Associated Analytic Story

* Suspicious WMI Use


#### 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].

#### Required field

* _time

* EventCode

* Message

* query



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1047 | Windows Management Instrumentation | Execution |


#### Kill Chain Phase

* Actions on Objectives


#### 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.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### WSReset UAC Bypass
This search is to detect a suspicious modification of registry related to UAC bypass. This technique is to modify the registry in this detection, create a registry value with the path of the payload and run WSreset.exe to bypass User account Control.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1548.002](https://attack.mitre.org/techniques/T1548/002/)
- **Last Updated**: 2021-07-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\AppX82a6gwre4fdg3bt635tn5ctqjf8msdd2\\Shell\\open\\command*" (Registry.registry_key_name = "(Default)" OR Registry.registry_key_name = "DelegateExecute") by Registry.registry_path Registry.registry_key_name Registry.registry_value_name Registry.dest 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `wsreset_uac_bypass_filter`
```
#### Associated Analytic Story

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.

#### Required field

* _time

* Registry.registry_path

* Registry.registry_key_name

* Registry.registry_value_name

* Registry.dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1548.002 | Bypass User Account Control | Privilege Escalation, Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://github.com/hfiref0x/UACME

* https://blog.morphisec.com/trickbot-uses-a-new-windows-10-uac-bypass



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1548/uac_bypass/windows-sysmon.log


_version_: 1
</details>

---

### Wbemprox COM Object Execution
this search is designed to detect potential malicious process loading COM object to wbemprox.dll,

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1218.003](https://attack.mitre.org/techniques/T1218/003/)
- **Last Updated**: 2021-06-02

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=7  ImageLoaded IN ("*\\fastprox.dll", "*\\wbemprox.dll", "*\\wbemcomn.dll") NOT (process_name IN ("wmiprvse.exe", "WmiApSrv.exe", "unsecapp.exe")) NOT(Image IN("*\\windows\\*","*\\program files*", "*\\wbem\\*")) 
| stats count min(_time) as firstTime max(_time) as lastTime by Image ImageLoaded process_name Computer EventCode Signed ProcessId Hashes IMPHASH 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `wbemprox_com_object_execution_filter`
```
#### Associated Analytic Story

* Ransomware

* Revil Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name and imageloaded executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* Image

* ImageLoaded

* process_name

* Computer

* EventCode

* Signed

* ProcessId

* Hashes

* IMPHASH



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1218.003 | CMSTP | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
legitimate process that are not in the exception list may trigger this event.

#### Reference


* https://krebsonsecurity.com/2021/05/a-closer-look-at-the-darkside-ransomware-gang/

* https://www.mcafee.com/blogs/other-blogs/mcafee-labs/mcafee-atr-analyzes-sodinokibi-aka-revil-ransomware-as-a-service-what-the-code-tells-us/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/revil/inf2/windows-sysmon.log


_version_: 1
</details>

---

### Web Servers Executing Suspicious Processes
This search looks for suspicious processes on all systems labeled as web servers.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1082](https://attack.mitre.org/techniques/T1082/)
- **Last Updated**: 2019-04-01

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.dest_category="web_server" AND (Processes.process="*whoami*" OR Processes.process="*ping*" OR Processes.process="*iptables*" OR Processes.process="*wget*" OR Processes.process="*service*" OR Processes.process="*curl*") by Processes.process Processes.process_name, Processes.dest Processes.user
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `web_servers_executing_suspicious_processes_filter`
```
#### Associated Analytic Story

* Apache Struts Vulnerability


#### 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.

#### Required field

* _time

* Processes.dest_category

* Processes.process

* Processes.process_name

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1082 | System Information Discovery | Discovery |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
Some of these processes may be used legitimately on web servers during maintenance or other administrative tasks.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Wermgr Process Connecting To IP Check Web Services
this search is designed to detect suspicious wermgr.exe process that tries to connect to known IP web services. This technique is know for trickbot and other trojan spy malware to recon the infected machine and look for its ip address without so much finger print on the commandline process. Since wermgr.exe is designed for error handling process of windows it is really suspicious that this process is trying to connect to this IP web services cause that maybe cause of some malicious code injection.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1590.005](https://attack.mitre.org/techniques/T1590/005/)
- **Last Updated**: 2021-04-19

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode =22 process_name = wermgr.exe QueryName IN ("*wtfismyip.com", "*checkip.amazonaws.com", "*ipecho.net", "*ipinfo.io", "*api.ipify.org", "*icanhazip.com", "*ip.anysrc.com","*api.ip.sb", "ident.me", "www.myexternalip.com", "*zen.spamhaus.org", "*cbl.abuseat.org", "*b.barracudacentral.org","*dnsbl-1.uceprotect.net", "*spam.dnsbl.sorbs.net") 
|  stats  min(_time) as firstTime max(_time) as lastTime count by  process_path process_name process_id QueryName QueryStatus QueryResults Computer EventCode 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `wermgr_process_connecting_to_ip_check_web_services_filter`
```
#### Associated Analytic Story

* Trickbot


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name, dns query name process path , and query ststus from your endpoints like EventCode 22. If you are using Sysmon, you must have at least version 12 of the Sysmon TA.

#### Required field

* _time

* process_path

* process_name

* process_id

* QueryName

* QueryStatus

* QueryResults

* Computer

* EventCode



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1590.005 | IP Addresses | Reconnaissance |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://labs.vipre.com/trickbot-and-its-modules/

* https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log


_version_: 1
</details>

---

### Wermgr Process Create Executable File
this search is designed to detect potential malicious wermgr.exe process that drops or create executable file. Since wermgr.exe is an application trigger when error encountered in a process, it is really un ussual to this process to drop executable file. This technique is commonly seen in trickbot malware where it injects it code to this process to execute it malicious behavior like downloading other payload

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1027](https://attack.mitre.org/techniques/T1027/)
- **Last Updated**: 2021-04-19

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=11 process_name = "wermgr.exe" TargetFilename = "*.exe" 
| stats  min(_time) as firstTime max(_time) as lastTime count by  Image TargetFilename process_name dest EventCode ProcessId 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `wermgr_process_create_executable_file_filter`
```
#### Associated Analytic Story

* Trickbot


#### 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. Tune and filter known instances of wermgr.exe may be used.

#### Required field

* _time

* Image

* TargetFilename

* process_name

* dest

* EventCode

* ProcessId



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1027 | Obfuscated Files or Information | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://labs.vipre.com/trickbot-and-its-modules/

* https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log


_version_: 1
</details>

---

### Wermgr Process Spawned CMD Or Powershell Process
This search is designed to detect suspicious cmd and powershell process spawned by wermgr.exe process. This suspicious behavior are commonly seen in code injection technique technique like trickbot to execute a shellcode, dll modules to run malicious behavior.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1059](https://attack.mitre.org/techniques/T1059/)
- **Last Updated**: 2021-04-19

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` values(Processes.process) as cmdline min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name = "wermgr.exe" Processes.process_name = "cmd.exe" OR Processes.process_name = "powershell.exe" by Processes.parent_process_name Processes.parent_process_id  Processes.process_name Processes.process Processes.process_id Processes.process_guid Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `wermgr_process_spawned_cmd_or_powershell_process_filter`
```
#### Associated Analytic Story

* Trickbot


#### 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.

#### Required field

* _time

* Processes.parent_process_name

* Processes.parent_process_id

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.process_guid

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1059 | Command and Scripting Interpreter | Execution |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
unknown

#### Reference


* https://labs.vipre.com/trickbot-and-its-modules/

* https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/infection/windows-sysmon.log


_version_: 1
</details>

---

### WevtUtil Usage To Clear Logs
The wevtutil.exe application is the windows event log utility. This searches for wevtutil.exe with parameters for clearing the application, security, setup, powershell, sysmon, or system event logs.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: Endpoint
- **ATT&CK**: [T1070.001](https://attack.mitre.org/techniques/T1070/001/)
- **Last Updated**: 2021-06-15

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line IS NOT NULL AND like(cmd_line, "% cl %") AND (match_regex(cmd_line, /(?i)security/)=true OR match_regex(cmd_line, /(?i)system/)=true OR match_regex(cmd_line, /(?i)sysmon/)=true OR match_regex(cmd_line, /(?i)application/)=true OR match_regex(cmd_line, /(?i)setup/)=true OR match_regex(cmd_line, /(?i)powershell/)=true) AND process_name="wevtutil.exe" 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name, "process_path", process_path]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Log Manipulation

* Ransomware

* Clop Ransomware


#### 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.

#### Required field

* _time

* dest_device_id

* process_name

* parent_process_name

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070.001 | Clear Windows Event Logs | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs.

#### Reference


* https://www.splunk.com/en_us/blog/security/detecting-clop-ransomware.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/ssa_wevtutil/clear_evt.log


_version_: 1
</details>

---

### Wevtutil Usage To Disable Logs
This search is to detect execution of wevtutil.exe to disable logs. This technique was seen in several ransomware to disable the event logs to evade alerts and detections in compromised host.

- **Product**: Splunk Behavioral Analytics
- **Datamodel**: Endpoint
- **ATT&CK**: [T1070.001](https://attack.mitre.org/techniques/T1070/001/)
- **Last Updated**: 2021-06-15

<details>
  <summary>details</summary>

#### Search
```

| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), cmd_line=ucast(map_get(input_event, "process"), "string", null), process_name=ucast(map_get(input_event, "process_name"), "string", null), process_path=ucast(map_get(input_event, "process_path"), "string", null), parent_process_name=ucast(map_get(input_event, "parent_process_name"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where cmd_line IS NOT NULL AND like(cmd_line, "% sl %") AND like(cmd_line, "%/e:false%") AND process_name="wevtutil.exe" 
| eval start_time=timestamp, end_time=timestamp, entities=mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)), body=create_map(["event_id", event_id, "cmd_line", cmd_line, "process_name", process_name, "parent_process_name", parent_process_name, "process_path", process_path]) 
| into write_ssa_detected_events();
```
#### Associated Analytic Story

* Windows Log Manipulation

* Ransomware


#### 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.

#### Required field

* _time

* dest_device_id

* process_name

* parent_process_name

* process_path

* dest_user_id

* process



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070.001 | Clear Windows Event Logs | Defense Evasion |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
network operator may disable audit event logs for debugging purposes.

#### Reference


* https://www.bleepingcomputer.com/news/security/new-ransom-x-ransomware-used-in-texas-txdot-cyberattack/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/ssa_wevtutil/disable_evt.log


_version_: 1
</details>

---

### WinEvent Scheduled Task Created Within Public Path
The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed from a user writeable file path.\
The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not.\
schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`.\
The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory.\
Upon triage, identify the task scheduled source. Was it schtasks.exe or was it via TaskService. Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053/005/)
- **Last Updated**: 2021-04-08

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4698 
| xmlkv Message 
| search Command IN ("*\\users\\public\\*", "*\\programdata\\*", "*\\temp\\*", "*\\Windows\\Tasks\\*", "*\\appdata\\*") 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `winevent_scheduled_task_created_within_public_path_filter`
```
#### Associated Analytic Story

* Windows Persistence Techniques

* Ransomware

* Ryuk Ransomware

* Icedid


#### How To Implement
To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.

#### Required field

* _time

* dest

* Task_Name

* Description

* Command



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation |


#### Kill Chain Phase

* Privilege Escalation


#### Known False Positives
False positives are possible if legitimate applications are allowed to register tasks in public paths. Filter as needed based on paths that are used legitimately.

#### Reference


* https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/

* https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698

* https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/

* https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN

* https://app.any.run/tasks/e26f1b2e-befa-483b-91d2-e18636e2faf3/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/taskschedule/windows-security.log


_version_: 1
</details>

---

### WinEvent Scheduled Task Created to Spawn Shell
The following query utilizes Windows Security EventCode 4698, `A scheduled task was created`, to identify suspicious tasks registered on Windows either via schtasks.exe OR TaskService with a command to be executed with a native Windows shell (PowerShell, Cmd, Wscript, Cscript).\
The search will return the first time and last time the task was registered, as well as the `Command` to be executed, `Task Name`, `Author`, `Enabled`, and whether it is `Hidden` or not.\
schtasks.exe is natively found in `C:\Windows\system32` and `C:\Windows\syswow64`.\
The following DLL(s) are loaded when schtasks.exe or TaskService is launched -`taskschd.dll`. If found loaded by another process, it is possible a scheduled task is being registered within that process context in memory.\
Upon triage, identify the task scheduled source. Was it schtasks.exe or via TaskService? Review the job created and the Command to be executed. Capture any artifacts on disk and review. Identify any parallel processes within the same timeframe to identify source.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1053.005](https://attack.mitre.org/techniques/T1053/005/)
- **Last Updated**: 2021-04-12

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=4698 
| xmlkv Message 
| search Command IN ("*powershell.exe*", "*wscript.exe*", "*cscript.exe*", "*cmd.exe*", "*sh.exe*", "*ksh.exe*", "*zsh.exe*", "*bash.exe*", "*scrcons.exe*", "*pwsh.exe*") 
| stats count min(_time) as firstTime max(_time) as lastTime by dest, Task_Name, Command, Author, Enabled, Hidden 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `winevent_scheduled_task_created_to_spawn_shell_filter`
```
#### Associated Analytic Story

* Windows Persistence Techniques

* Ransomware

* Ryuk Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting Windows Security Event Logs with 4698 EventCode enabled. The Windows TA is also required.

#### Required field

* _time

* dest

* Task_Name

* Description

* Command



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1053.005 | Scheduled Task | Execution, Persistence, Privilege Escalation |


#### Kill Chain Phase

* Privilege Escalation


#### Known False Positives
False positives are possible if legitimate applications are allowed to register tasks that call a shell to be spawned. Filter as needed based on command-line or processes that are used legitimately.

#### Reference


* https://research.checkpoint.com/2021/irans-apt34-returns-with-an-updated-arsenal/

* https://www.ultimatewindowssecurity.com/securitylog/encyclopedia/event.aspx?eventID=4698

* https://redcanary.com/threat-detection-report/techniques/scheduled-task-job/

* https://docs.microsoft.com/en-us/windows/win32/taskschd/time-trigger-example--scripting-?redirectedfrom=MSDN



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.005/atomic_red_team/windows-security.log


_version_: 1
</details>

---

### WinRM Spawning a Process
The following analytic identifies suspicious processes spawning from WinRM (wsmprovhost.exe). This analytic is related to potential exploitation of CVE-2021-31166. which is a kernel-mode device driver http.sys vulnerability. Current proof of concept code will blue-screen the operating system. However, http.sys used by many different Windows processes, including WinRM. In this case, identifying suspicious process create (child processes) from `wsmprovhost.exe` is what this analytic is identifying.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1190](https://attack.mitre.org/techniques/T1190/)
- **Last Updated**: 2021-05-21

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=wsmprovhost.exe Processes.process_name IN ("cmd.exe","sh.exe","bash.exe","powershell.exe","pwsh.exe","schtasks.exe","certutil.exe","whoami.exe","bitsadmin.exe","scp.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `winrm_spawning_a_process_filter`
```
#### Associated Analytic Story

* Unusual Processes


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* Processes.dest

* Processes.user

* Processes.parent_process

* Processes.process_name

* Processes.process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1190 | Exploit Public-Facing Application | Initial Access |


#### Kill Chain Phase

* Exploitation

* Privilege Escalation

* Denial of Service


#### Known False Positives
Unknown. Add new processes or filter as needed. It is possible system management software may spawn processes from `wsmprovhost.exe`.

#### Reference


* https://github.com/SigmaHQ/sigma/blob/master/rules/windows/process_access/win_susp_shell_spawn_from_winrm.yml

* https://www.zerodayinitiative.com/blog/2021/5/17/cve-2021-31166-a-wormable-code-execution-bug-in-httpsys

* https://github.com/0vercl0k/CVE-2021-31166/blob/main/cve-2021-31166.py



#### Test Dataset


_version_: 1
</details>

---

### Windows AdFind Exe
This search looks for the execution of `adfind.exe` with command-line arguments that it uses by default. Specifically the filter or search functions. It also considers the arguments necessary like objectcategory, see readme for more details: https://www.joeware.net/freetools/tools/adfind/usage.htm. This has been seen used before by Wizard Spider, FIN6 and actors whom also launched SUNBURST. AdFind.exe is usually used a recon tool to enumare a domain controller.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1018](https://attack.mitre.org/techniques/T1018/)
- **Last Updated**: 2020-12-16

<details>
  <summary>details</summary>

#### Search
```
 
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process=*-f* OR Processes.process=*-b*) AND (Processes.process=*objectcategory* OR Processes.process=*-gcb* OR Processes.process=*-sc*) by Processes.dest Processes.user Processes.process_name Processes.process  Processes.parent_process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `windows_adfind_exe_filter` 
```
#### Associated Analytic Story

* NOBELIUM Group

* Domain Trust Discovery


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the process name, 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.

#### Required field

* _time

* Processes.process

* Processes.dest

* Processes.user

* Processes.process_name

* Processes.parent_process

* Processes.process_id

* Processes.parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1018 | Remote System Discovery | Discovery |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
administrators rarely use adfind, usually not used for legitimate reasons

#### Reference


* https://www.volexity.com/blog/2020/12/14/dark-halo-leverages-solarwinds-compromise-to-breach-organizations/

* https://www.fireeye.com/blog/threat-research/2019/01/a-nasty-trick-from-credential-theft-malware-to-business-disruption.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1018/atomic_red_team/windows-sysmon.log


_version_: 1
</details>

---

### Windows DisableAntiSpyware Registry
The search looks for the Registry Key DisableAntiSpyware set to disable. This is consistent with Ryuk infections across a fleet of endpoints. This particular behavior is typically executed when an ransomware actor gains access to an endpoint and beings to perform execution. Usually, a batch (.bat) will be executed and multiple registry and scheduled task modifications will occur. During triage, review parallel processes and identify any further file modifications. Endpoint should be isolated.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1562.001](https://attack.mitre.org/techniques/T1562/001/)
- **Last Updated**: 2021-03-02

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_key_name="DisableAntiSpyware" AND Registry.registry_value_name="DWORD (0x00000001)" by Registry.dest Registry.user Registry.registry_path Registry.registry_value_name 
| `drop_dm_object_name(Registry)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `windows_disableantispyware_registry_filter`
```
#### Associated Analytic Story

* Ryuk Ransomware

* Windows Defense Evasion Tactics


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Registry` node.

#### Required field

* _time

* Registry.registry_key_name

* Registry.registry_value_name

* Registry.dest

* Registry.user

* Registry.registry_path



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1562.001 | Disable or Modify Tools | Defense Evasion |


#### Kill Chain Phase

* Delivery


#### Known False Positives
It is unusual to turn this feature off a Windows system since it is a default security control, although it is not rare for some policies to disable it. Although no false positives have been identified, use the provided filter macro to tune the search.

#### Reference


* https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/atomic_red_team/windows-sysmon.log


_version_: 2
</details>

---

### Windows Event Log Cleared
The following analytic utilizes Windows Security Event ID 1102 or System log event 104 to identify when a Windows event log is cleared. Note that this analytic will require tuning or restricted to specific endpoints based on criticality. During triage, based on time of day and user, determine if this was planned. If not planned, follow through with reviewing parallel alerts and other data sources to determine what else may have occurred.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1070.001](https://attack.mitre.org/techniques/T1070/001/)
- **Last Updated**: 2020-07-06

<details>
  <summary>details</summary>

#### Search
```
(`wineventlog_security` EventCode=1102) OR (`wineventlog_system` EventCode=104) 
| stats count min(_time) as firstTime max(_time) as lastTime by dest Message EventCode 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `windows_event_log_cleared_filter`
```
#### Associated Analytic Story

* Windows Log Manipulation

* Ransomware

* Clop Ransomware


#### How To Implement
To successfully implement this search, you need to be ingesting Windows event logs from your hosts. In addition, the Splunk Windows TA is needed.

#### Required field

* _time

* EventCode

* dest



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1070.001 | Clear Windows Event Logs | Defense Evasion |


#### Kill Chain Phase

* Actions on Objectives


#### Known False Positives
It is possible that these logs may be legitimately cleared by Administrators. Filter as needed.

#### Reference


* https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-1102

* https://www.ired.team/offensive-security/defense-evasion/disabling-windows-event-logs-by-suspending-eventlog-service-threads

* https://attack.mitre.org/techniques/T1070/001/

* https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1070.001/T1070.001.md



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-security.log

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1070.001/atomic_red_team/windows-system.log


_version_: 6
</details>

---

### Windows Security Account Manager Stopped
The search looks for a Windows Security Account Manager (SAM) was stopped via command-line. This is consistent with Ryuk infections across a fleet of endpoints.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1489](https://attack.mitre.org/techniques/T1489/)
- **Last Updated**: 2020-11-06

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes WHERE ("Processes.process_name"="net*.exe" "Processes.process"="*stop \"samss\"*") BY "Processes.dest", "Processes.user", "Processes.process" 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(lastTime)` 
| `security_content_ctime(firstTime)` 
| `windows_security_account_manager_stopped_filter`
```
#### Associated Analytic Story

* Ryuk Ransomware


#### How To Implement
You must be ingesting data that records the process-system activity from your hosts to populate the Endpoint Processes data-model object. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data.

#### Required field

* _time

* Processes.process_name

* Processes.process

* Processes.dest

* Processes.user



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1489 | Service Stop | Impact |


#### Kill Chain Phase

* Delivery


#### Known False Positives
SAM is a critical windows service, stopping it would cause major issues on an endpoint this makes false positive rare. AlthoughNo false positives have been identified.

#### Reference


#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/ryuk/windows-sysmon.log


_version_: 1
</details>

---

### Windows Updates Install Failures
This search is intended to give you a feel for how often Windows updates fail to install in your environment. Fluctuations in these numbers will allow you to determine when you should be concerned.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-09-14

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure by _time span=1d
```
#### Associated Analytic Story


#### How To Implement
You must be ingesting your Windows Update Logs

#### Required field

* _time

* Updates.vendor_product

* Updates.status




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Windows Updates Install Successes
This search is intended to give you a feel for how often successful Windows updates are applied in your environments. Fluctuations in these numbers will allow you to determine when you should be concerned.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: 
- **Last Updated**: 2017-09-14

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=installed by _time span=1d
```
#### Associated Analytic Story


#### How To Implement
You must be ingesting your Windows Update Logs

#### Required field

* _time

* Updates.vendor_product

* Updates.status




#### Kill Chain Phase


#### Known False Positives
none

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### Winword Spawning Cmd
The following detection identifies Microsoft Word spawning `cmd.exe`. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). Cmd.exe spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line will indicate what is being executed. During triage, review parallel processes and identify any files that may have been written. It is possible that COM is utilized to trampoline the child process to `explorer.exe` or `wmiprvse.exe`.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-22

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=winword.exe Processes.process_name=cmd.exe by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `winword_spawning_cmd_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest

* user

* parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives should be limited, but if any are present, filter as needed.

#### Reference


* https://app.any.run/tasks/73af0064-a785-4c0a-ab0d-cde593fe16ef/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log


_version_: 1
</details>

---

### Winword Spawning PowerShell
The following detection identifies Microsoft Word spawning PowerShell. Typically, this is not common behavior and not default with winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). PowerShell spawning from winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command executed will most likely be encoded and captured via another detection. During triage, review parallel processes and identify any files that may have been written.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("powershell.exe", "pwsh.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `winword_spawning_powershell_filter`
```
#### Associated Analytic Story

* Spearphishing Attachments


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest

* user

* parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives should be limited, but if any are present, filter as needed.

#### Reference


* https://redcanary.com/threat-detection-report/techniques/powershell/

* https://attack.mitre.org/techniques/T1566/001/

* https://app.any.run/tasks/b79fa381-f35c-4b3e-8d02-507e7ee7342f/

* https://app.any.run/tasks/181ac90b-0898-4631-8701-b778a30610ad/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon.log


_version_: 1
</details>

---

### Winword Spawning Windows Script Host
The following detection identifies Microsoft Winword.exe spawning Windows Script Host - `cscript.exe` or `wscript.exe`. Typically, this is not common behavior and not default with Winword.exe. Winword.exe will generally be found in the following path `C:\Program Files\Microsoft Office\root\Office16` (version will vary). `cscript.exe` or `wscript.exe` default location is `c:\windows\system32\` or c:windows\syswow64\`. `cscript.exe` or `wscript.exe` spawning from Winword.exe is common for a spearphishing attachment and is actively used. Albeit, the command-line executed will most likely be obfuscated and captured via another detection. During triage, review parallel processes and identify any files that may have been written. Review the reputation of the remote destination and block accordingly.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1566.001](https://attack.mitre.org/techniques/T1566/001/)
- **Last Updated**: 2021-04-12

<details>
  <summary>details</summary>

#### Search
```

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name="winword.exe" Processes.process_name IN ("cscript.exe", "wscript.exe") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `winword_spawning_windows_script_host_filter`
```
#### Associated Analytic Story

* Spearphishing Attachment


#### How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Processes` node.

#### Required field

* _time

* process_name

* process_id

* parent_process_name

* dest

* user

* parent_process_id



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1566.001 | Spearphishing Attachment | Initial Access |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
There will be limited false positives and it will be different for every environment. Tune by child process or command-line as needed.

#### Reference


* https://attack.mitre.org/techniques/T1566/001/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1566.001/macro/windows-sysmon_wsh.log


_version_: 1
</details>

---

### Write Executable in SMB Share
This search is to detect suspicious dropping or creating an executable file in known sensitive SMB share. This technique is commonly used for lateral movement like how trickbot try to infect other machine in the infected network. This detection catch the access event (FILE WRITE) access to a share.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1021.002](https://attack.mitre.org/techniques/T1021/002/)
- **Last Updated**: 2021-04-23

<details>
  <summary>details</summary>

#### Search
```
`wineventlog_security` EventCode=5145 Relative_Target_Name IN ("*.exe","*.dll") Object_Type=File Share_Name IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") Access_Mask= "0x2" 
| stats min(_time) as firstTime max(_time) as lastTime count by EventCode Share_Name Relative_Target_Name Object_Type Access_Mask user src_port Source_Address 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `write_executable_in_smb_share_filter`
```
#### Associated Analytic Story

* Trickbot


#### How To Implement
To successfully implement this search, you need to be ingesting Windows Security Event Logs with 5145 EventCode enabled. The Windows TA is also required. Also enable the object Audit access success/failure in your group policy.

#### Required field

* _time

* EventCode

* Share_Name

* Relative_Target_Name

* Object_Type

* Access_Mask

* user

* src_port

* Source_Address



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1021.002 | SMB/Windows Admin Shares | Lateral Movement |


#### Kill Chain Phase

* Lateral Movement


#### Known False Positives
unknown

#### Reference


* https://labs.vipre.com/trickbot-and-its-modules/

* https://blog.whitehat.eu/2019/05/incident-trickbot-ryuk-2.html



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/trickbot/exe_smbshare/windows-security.log


_version_: 1
</details>

---

### XMRIG Driver Loaded
This analytic identifies XMRIG coinminer driver installation on the system. The XMRIG driver name by default is `WinRing0x64.sys`. This cpu miner is an open source project that is commonly abused by adversaries to infect and mine bitcoin.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: Endpoint
- **ATT&CK**: [T1543.003](https://attack.mitre.org/techniques/T1543/003/)
- **Last Updated**: 2021-04-29

<details>
  <summary>details</summary>

#### Search
```
`sysmon` EventCode=6 Signature="Noriyuki MIYAZAKI" OR ImageLoaded= "*\\WinRing0x64.sys" 
|  stats  min(_time) as firstTime max(_time) as lastTime count by  Computer ImageLoaded Hashes IMPHASH Signature Signed 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `xmrig_driver_loaded_filter`
```
#### Associated Analytic Story

* XMRig


#### How To Implement
To successfully implement this search, you need to be ingesting logs with the driver loaded and Signature from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.

#### Required field

* _time

* Computer

* ImageLoaded

* Hashes

* IMPHASH

* Signature

* Signed



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1543.003 | Windows Service | Persistence, Privilege Escalation |


#### Kill Chain Phase

* Exploitation


#### Known False Positives
False positives should be limited.

#### Reference


* https://www.trendmicro.com/vinfo/hk/threat-encyclopedia/malware/trojan.ps1.powtran.a/



#### Test Dataset

* https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/xmrig_miner/windows-sysmon.log


_version_: 1
</details>

---

### aws detect attach to role policy
This search provides detection of an user attaching itself to a different role trust policy. This can be used for lateral movement and escalation of privileges.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2020-07-27

<details>
  <summary>details</summary>

#### Search
```
`aws_cloudwatchlogs_eks` attach policy
| spath requestParameters.policyArn 
| table sourceIPAddress user_access_key userIdentity.arn userIdentity.sessionContext.sessionIssuer.arn eventName errorCode errorMessage status action requestParameters.policyArn userIdentity.sessionContext.attributes.mfaAuthenticated userIdentity.sessionContext.attributes.creationDate  
| `aws_detect_attach_to_role_policy_filter`
```
#### Associated Analytic Story

* AWS Cross Account Activity


#### How To Implement
You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs

#### Required field

* _time

* requestParameters.policyArn



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Lateral Movement


#### Known False Positives
Attach to policy can create a lot of noise. This search can be adjusted to provide specific values to identify cases of abuse (i.e status=failure). The search can provide context for common users attaching themselves to higher privilege policies or even newly created policies.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### aws detect permanent key creation
This search provides detection of accounts creating permanent keys. Permanent keys are not created by default and they are only needed for programmatic calls. Creation of Permanent key is an important event to monitor.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2020-07-27

<details>
  <summary>details</summary>

#### Search
```
`aws_cloudwatchlogs_eks` CreateAccessKey 
| spath eventName 
| search eventName=CreateAccessKey "userIdentity.type"=IAMUser 
| table sourceIPAddress userName userIdentity.type userAgent action status responseElements.accessKey.createDate responseElements.accessKey.status responseElements.accessKey.accessKeyId 
|`aws_detect_permanent_key_creation_filter`
```
#### Associated Analytic Story

* AWS Cross Account Activity


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with cloudwatch logs

#### Required field

* _time

* eventName

* userIdentity.type

* sourceIPAddress

* userName userIdentity.type

* userAgent

* action

* status

* responseElements.accessKey.createDate

* esponseElements.accessKey.status

* responseElements.accessKey.accessKeyId



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Lateral Movement


#### Known False Positives
Not all permanent key creations are malicious. If there is a policy of rotating keys this search can be adjusted to provide better context.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### aws detect role creation
This search provides detection of role creation by IAM users. Role creation is an event by itself if user is creating a new role with trust policies different than the available in AWS and it can be used for lateral movement and escalation of privileges.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2020-07-27

<details>
  <summary>details</summary>

#### Search
```
`aws_cloudwatchlogs_eks` event_name=CreateRole action=created userIdentity.type=AssumedRole requestParameters.description=Allows* 
| table sourceIPAddress userIdentity.principalId userIdentity.arn action event_name awsRegion http_user_agent mfa_auth msg requestParameters.roleName requestParameters.description responseElements.role.arn responseElements.role.createDate 
| `aws_detect_role_creation_filter`
```
#### Associated Analytic Story

* AWS Cross Account Activity


#### How To Implement
You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs

#### Required field

* _time

* event_name

* action

* userIdentity.type

* requestParameters.description

* sourceIPAddress

* userIdentity.principalId

* userIdentity.arn

* action

* event_name

* awsRegion

* http_user_agent

* mfa_auth

* msg

* requestParameters.roleName

* requestParameters.description

* responseElements.role.arn

* responseElements.role.createDate



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Lateral Movement


#### Known False Positives
CreateRole is not very common in common users. This search can be adjusted to provide specific values to identify cases of abuse. In general AWS provides plenty of trust policies that fit most use cases.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### aws detect sts assume role abuse
This search provides detection of suspicious use of sts:AssumeRole. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1078](https://attack.mitre.org/techniques/T1078/)
- **Last Updated**: 2020-07-27

<details>
  <summary>details</summary>

#### Search
```
`cloudtrail` user_type=AssumedRole userIdentity.sessionContext.sessionIssuer.type=Role 
| table sourceIPAddress userIdentity.arn user_agent user_access_key status action requestParameters.roleName responseElements.role.roleName responseElements.role.createDate 
| `aws_detect_sts_assume_role_abuse_filter`
```
#### Associated Analytic Story

* AWS Cross Account Activity


#### How To Implement
You must install splunk AWS add on and Splunk App for AWS. This search works with AWS CloudTrail logs

#### Required field

* _time

* user_type

* userIdentity.sessionContext.sessionIssuer.type

* sourceIPAddress

* userIdentity.arn

* user_agent

* user_access_key

* status

* action

* requestParameters.roleName

* esponseElements.role.roleName

* esponseElements.role.createDate



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1078 | Valid Accounts | Defense Evasion, Persistence, Privilege Escalation, Initial Access |


#### Kill Chain Phase

* Lateral Movement


#### Known False Positives
Sts:AssumeRole can be very noisy as it is a standard mechanism to provide cross account and cross resources access. This search can be adjusted to provide specific values to identify cases of abuse.

#### Reference


#### Test Dataset


_version_: 1
</details>

---

### aws detect sts get session token abuse
This search provides detection of suspicious use of sts:GetSessionToken. These tokens can be created on the go and used by attackers to move laterally and escalate privileges.

- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: 
- **ATT&CK**: [T1550](https://attack.mitre.org/techniques/T1550/)
- **Last Updated**: 2020-07-27

<details>
  <summary>details</summary>

#### Search
```
`aws_cloudwatchlogs_eks` ASIA  userIdentity.type=IAMUser
| spath eventName 
| search eventName=GetSessionToken 
| table sourceIPAddress eventTime userIdentity.arn userName userAgent user_type status region 
| `aws_detect_sts_get_session_token_abuse_filter`
```
#### Associated Analytic Story

* AWS Cross Account Activity


#### How To Implement
You must install splunk AWS add-on and Splunk App for AWS. This search works with cloudwatch logs

#### Required field

* _time

* userIdentity.type

* eventName

* sourceIPAddress

* eventTime

* userIdentity.arn

* userName

* userAgent

* user_type

* status

* region



#### ATT&CK

| ID          | Technique   | Tactic       |
| ----------- | ----------- |--------------|
| T1550 | Use Alternate Authentication Material | Defense Evasion, Lateral Movement |


#### Kill Chain Phase

* Lateral Movement


#### Known False Positives
Sts:GetSessionToken can be very noisy as in certain environments numerous calls of this type can be executed. This search can be adjusted to provide specific values to identify cases of abuse. In specific environments the use of field requestParameters.serialNumber will need to be used.

#### Reference


#### Test Dataset


_version_: 1
</details>

---
