Skills Data Science Detecting LOLBAS Abuse Via Process Telemetry

Detecting LOLBAS Abuse Via Process Telemetry

v20260601
detecting-living-off-the-land-with-lolbas
This skill provides a comprehensive framework for detecting Living Off the Land Binaries (LOLBAS) abuse, such as misuse of certutil, regsvr32, and mshta. It leverages process telemetry from Sysmon and Windows Event Logs, combined with advanced Sigma rule-based detection and parent-child process anomaly analysis. Ideal for SOC analysts and threat hunters investigating sophisticated adversaries aiming to evade traditional security controls.
Get Skill
346 downloads
Overview

Detecting Living Off the Land with LOLBAS

Overview

Living Off the Land Binaries, Scripts, and Libraries (LOLBAS) are legitimate system utilities abused by attackers to execute malicious actions while evading detection. This skill covers detecting abuse of certutil.exe, regsvr32.exe, mshta.exe, rundll32.exe, msbuild.exe, and other LOLBins using process telemetry from Sysmon and Windows Event Logs, combined with Sigma rule-based detection.

When to Use

  • When investigating security incidents that require detecting living off the land with lolbas
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • Sysmon or Windows Security Event Log (Event ID 4688) with command-line logging enabled
  • Sigma rule conversion tool (sigmac or sigma-cli)
  • SIEM platform (Splunk, Elastic, or similar) for log ingestion
  • Python 3.8+ with pySigma library
  • LOLBAS project reference database

Steps

  1. Establish LOLBin Watchlist — Build a prioritized list of monitored binaries (certutil, mshta, regsvr32, rundll32, msbuild, installutil, cmstp, wmic, bitsadmin)
  2. Collect Process Telemetry — Ingest Sysmon Event ID 1 (Process Create) and Windows 4688 events with full command-line capture
  3. Build Sigma Detection Rules — Create Sigma rules matching suspicious command-line arguments, network activity, and parent-child process anomalies for each LOLBin
  4. Analyze Parent-Child Relationships — Flag unexpected parent processes spawning LOLBins (e.g., Excel spawning certutil, Word spawning mshta)
  5. Score and Prioritize Alerts — Apply risk scoring based on argument anomaly, parent process, execution path, and network indicators
  6. Generate Detection Report — Produce a structured report of all LOLBin abuse detections with MITRE ATT&CK mapping

Expected Output

  • JSON report listing detected LOLBin abuse events with severity scores
  • MITRE ATT&CK technique mapping for each detection (T1218, T1105, T1140, T1127)
  • Parent-child process anomaly analysis
  • Sigma rule match details with raw event data
Info
Category Data Science
Name detecting-living-off-the-land-with-lolbas
Version v20260601
Size 9.34KB
Updated At 2026-06-03
Language