本文へ移動
cccskills
無料GitHub で公開

detecting-lateral-movement-with-splunk

Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.

インストール方法を見る

含まれるファイル(8)

  • SKILL.md4.0 KB
  • assets/template.md1.2 KB
  • LICENSE11.0 KB
  • references/api-reference.md1.5 KB
  • references/standards.md2.6 KB
  • references/workflows.md4.6 KB
  • scripts/agent.py4.9 KB
  • scripts/process.py12.5 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Detecting Lateral Movement with Splunk

When to Use

  • When hunting for adversary movement between compromised systems
  • After detecting credential theft to trace subsequent lateral activity
  • When investigating unusual authentication patterns across the network
  • During incident response to scope the breadth of compromise
  • When proactively hunting for TA0008 (Lateral Movement) techniques

Prerequisites

  • Splunk Enterprise or Splunk Cloud with Windows event data ingested
  • Windows Security Event Logs forwarded (4624, 4625, 4648, 4672, 4768, 4769)
  • Sysmon deployed for process creation and network connection data
  • Network flow data or firewall logs for SMB/RDP/WinRM correlation
  • Active Directory user and group membership reference data

Workflow

  1. Define Lateral Movement Scope: Identify which lateral movement techniques to hunt (RDP, SMB/Admin Shares, WinRM, PsExec, WMI, DCOM, SSH).
  2. Query Authentication Events: Use SPL to search for Type 3 (Network) and Type 10 (RemoteInteractive) logons across the environment.
  3. Build Authentication Graphs: Map source-to-destination authentication relationships to identify unusual connection patterns.
  4. Detect First-Time Relationships: Identify new source-destination pairs that have not been seen in the historical baseline.
  5. Correlate with Process Activity: Link authentication events to subsequent process creation on destination hosts.
  6. Identify Anomalous Patterns: Flag lateral movement to sensitive servers, unusual hours, service account misuse, or rapid multi-host access.
  7. Report and Contain: Document lateral movement path, affected systems, and coordinate containment response.

Key Concepts

ConceptDescription
T1021Remote Services (parent technique)
T1021.001Remote Desktop Protocol (RDP)
T1021.002SMB/Windows Admin Shares
T1021.003Distributed COM (DCOM)
T1021.004SSH
T1021.006Windows Remote Management (WinRM)
T1570Lateral Tool Transfer
T1047Windows Management Instrumentation
T1569.002Service Execution (PsExec)
Logon Type 3Network logon (SMB, WinRM, mapped drives)
Logon Type 10Remote Interactive (RDP)
Event ID 4624Successful logon
Event ID 4648Explicit credential logon (runas, PsExec)

Tools & Systems

ToolPurpose
Splunk EnterpriseSIEM for log aggregation and SPL queries
Splunk Enterprise SecurityThreat detection and notable events
Windows Event ForwardingCentralize Windows logs
SysmonDetailed process and network telemetry
BloodHoundAD attack path analysis
PingCastleAD security assessment

Common Scenarios

  1. PsExec Lateral Movement: Adversary uses PsExec to execute commands on remote systems via SMB, generating Type 3 logon with ADMIN$ share access.
  2. RDP Pivoting: Attacker RDPs to internal systems using stolen credentials, creating Type 10 logon events.
  3. WMI Remote Execution: Adversary uses WMIC process call create to spawn processes on remote hosts.
  4. WinRM PowerShell Remoting: Attacker uses Enter-PSSession or Invoke-Command to execute code on remote systems.
  5. Pass-the-Hash via SMB: Compromised NTLM hashes used to authenticate to remote systems without knowing the plaintext password.

Output Format

Hunt ID: TH-LATMOV-[DATE]-[SEQ]
Movement Type: [RDP/SMB/WinRM/WMI/DCOM/PsExec]
Source Host: [Hostname/IP]
Destination Host: [Hostname/IP]
Account Used: [Username]
Logon Type: [3/10/other]
First Seen: [Timestamp]
Event Count: [Number of events]
Risk Level: [Critical/High/Medium/Low]
Lateral Movement Path: [A -> B -> C -> D]

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.

日本語の概要は準備中です。原文の説明を表示しています。

micsapp/micstec-skills42026年3月20日 更新

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

日本語の概要は準備中です。原文の説明を表示しています。

micsapp/micstec-skills42026年3月20日 更新

Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths

日本語の概要は準備中です。原文の説明を表示しています。

micsapp/micstec-skills42026年3月20日 更新

Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.

日本語の概要は準備中です。原文の説明を表示しています。

micsapp/micstec-skills42026年3月20日 更新

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.

日本語の概要は準備中です。原文の説明を表示しています。

micsapp/micstec-skills42026年3月20日 更新

Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.

日本語の概要は準備中です。原文の説明を表示しています。

micsapp/micstec-skills42026年3月20日 更新

micsapp のスキルをすべて見る

このスキルの問題を報告する