Correlating Events to Detect APT Lateral Movement

v20260601

implementing-siem-correlation-rules-for-apt

This guide details how to implement advanced SIEM correlation rules to detect sophisticated Advanced Persistent Threats (APTs). By chaining multiple event types—including Windows authentication events, process execution telemetry, and network connection logs—across hosts, users can surface complex attack sequences that are invisible to single-event detections. It utilizes Splunk SPL and Sigma rule formats for robust security posture enhancement.

SIEM Security Operations APT Detection Correlation Rules Lateral Movement Windows Logs Splunk Sigma

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Overview

Implementing SIEM Correlation Rules for APT

When to Use

When deploying or configuring implementing siem correlation rules for apt capabilities in your environment
When establishing security controls aligned to compliance requirements
When building or improving security architecture for this domain
When conducting security assessments that require this implementation

Prerequisites

Familiarity with security operations concepts and tools
Access to a test or lab environment for safe execution
Python 3.8+ with required dependencies installed
Appropriate authorization for any testing activities

Instructions

Install dependencies: pip install requests pyyaml sigma-cli
Connect to the Splunk REST API and define correlation searches that chain multiple event types across hosts.
Build Sigma rules in YAML that express multi-step detection logic for lateral movement patterns:
- RDP logon (4624 LogonType=10) followed by service installation (7045) on same target within 15 minutes
- Pass-the-Hash: NTLM logon (4624 LogonType=3) followed by process creation (4688) of admin tools
- PsExec-style: Named pipe creation (Sysmon 17/18) correlated with remote service creation (7045)
Convert Sigma rules to Splunk SPL using sigma-cli convert.
Deploy correlation searches to Splunk ES via the REST API.
Run the agent to generate and install correlation rules, then audit existing rules for coverage gaps.

python scripts/agent.py --splunk-url https://localhost:8089 --username admin --password changeme --output correlation_report.json

Examples

Detect RDP Lateral Movement Chain

index=wineventlog (EventCode=4624 Logon_Type=10) OR (EventCode=7045)
| transaction Computer maxspan=15m startswith=(EventCode=4624) endswith=(EventCode=7045)
| where eventcount >= 2
| table _time Computer Account_Name ServiceName

Sigma Rule for PsExec Lateral Movement

title: PsExec Lateral Movement Detection
logsource:
  product: windows
  service: sysmon
detection:
  pipe_created:
    EventID: 17
    PipeName|startswith: '\PSEXESVC'
  service_installed:
    EventID: 7045
    ServiceFileName|contains: 'PSEXESVC'
  timeframe: 5m
  condition: pipe_created | near service_installed
level: high

Info

Category Development

Name implementing-siem-correlation-rules-for-apt

Version v20260601

Size 9.96KB

Source mukul975/Anthropic-Cybersecurity-Skills

Updated At 2026-06-03