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Implementing Siem Correlation Rules For Apt by yanacuti1121

Name: Implementing Siem Correlation Rules For Apt
Rating: 4.2 (5 reviews)
Author: yanacuti1121

Implementing Siem Correlation Rules For Apt

Write multi-event correlation rules that detect APT lateral movement by chaining Windows authentication events, process execution telemetry, and network connection logs across hosts. Uses Splunk SPL and Sigma rule format to correlate Event IDs 4624, 4648, 4688, and Sysmon Events 1/3 within sliding time windows to surface attack sequences invisible to single-event detections.

yanacuti1121 e7fdc97 4 files · 25.8 KB Updated Sep 9, 2026 2 repo stars

File contents

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

---name: implementing-siem-correlation-rules-for-aptdescription: Write multi-event correlation rules that detect APT lateral movement by chaining Windows authentication events, process execution telemetry, and network connection logs across hosts. Uses Splunk SPL and Sigma rule format to correlate Event IDs 4624, 4648, 4688, and Sysmon Events 1/3 within sliding time windows to surface attack sequences invisible to single-event detections.license: Apache-2.0--- # 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 1. Install dependencies: `pip install requests pyyaml sigma-cli` 2. Connect to the Splunk REST API and define correlation searches that chain multiple event types across hosts. 3. 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) 4. Convert Sigma rules to Splunk SPL using `sigma-cli convert`. 5. Deploy correlation searches to Splunk ES via the REST API. 6. Run the agent to generate and install correlation rules, then audit existing rules for coverage gaps. ```bash 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 ```yaml 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 ```

Frequently asked questions