Purpose

During DFIR analysis, it’s easy to rely on static data.
To train and validate detections, I built a PowerShell-based Random DFIR Noise Simulator that produces realistic but safe Windows event activity for hunting and Sigma rule tuning.

Evolution of the Simulator

During early DFIR lab builds, I wrote a sequence of PowerShell scripts to simulate benign Windows activity and inject lightweight IOC noise into Sysmon and Security logs for Chainsaw and Sigma testing.

🧱 Phase 1 — Static Event Generator (Simulate-DFIR-Noise.ps1)

  • Deterministic event sequences (logons, file operations, encoded PowerShell commands)
  • Perfect for parser validation because each run produced identical logs
  • Proved the value of synthetic telemetry for Sigma mapping
# Example (static baseline)
.\Simulate-DFIR-Noise.ps1 -Preset Default -OfflineMode On

Limitation: predictability — identical 4688 / 4104 / 1 events every run.

🧩 Phase 2 — Static + Encoded Commands (Simulate-DFIR-Noise-PS EncodedCommand)

  • Added Base64-encoded PowerShell commands to trigger Event ID 4104 and 4688
  • Brought attacker-like realism to rule testing
# EncodedCommand sample
powershell.exe -EncodedCommand JAB0AGkAbQBlACAAPQAgACcAMgAnADsA

🔀 Phase 3 — Randomized DFIR Noise (Random_DFIR_Noise_Simulator.ps1)

To mimic real systems, I redesigned the simulator with controlled entropy.

Key Upgrades

  • Random variety: -Variety high shuffles event order and timestamps
  • Dynamic IOC source: reads custom-iocs.json for IPs/domains/hashes
  • Offline-mode safe: -OfflineMode Auto avoids network egress
  • Encoded bursts: optional Base64 payloads
  • Per-run subfolders: unique log sets per execution
.\Random_DFIR_Noise_Simulator.ps1 `
  -Preset custom `
  -ConfigFile .\custom-iocs.json `
  -PerRunSubfolder `
  -Scenario Random `
  -Variety high `
  -OfflineMode Auto `
  -DetectionsMax 1700

Each run now behaves like a unique mini-incident, ideal for validating detection resiliency and AI summarization in ForenSynth AI.

📊 Version Comparison Overview

VersionNoise TypeIOC VarietyEncodedCommand ActivityPurposeOutcome
Simulate-DFIR-Noise.ps1StaticNoneNoBaseline Chainsaw/Sigma parser validationReliable but predictable
Simulate-DFIR-Noise-PS EncodedCommandStatic + PowerShellModerateYesIntroduce attacker-like 4104/4688 eventsAdds realism
Random_DFIR_Noise_Simulator.ps1DynamicHigh (via JSON)Optional burstsStress-test detections + AI summariesClosest to real endpoint noise

🧠 Lessons Learned

Developing these simulators proved that noise is data with context.
Each iteration deepened my understanding of how telemetry behaves under controlled chaos:

  • Deterministic scripts verify that the pipeline works.
  • Randomized scripts test whether it still works when nothing repeats.
  • Offline-safe automation + per-run logging preserve forensic integrity while enabling endless experimentation.

This simulate → observe → refine loop defines my DFIR Journey approach, culminating in ForenSynth AI, where structured randomness fuels both detection accuracy and analyst speed.

⚙️ Integration Workflow

  1. Run the simulator to populate fresh logs.
  2. Hunt with Chainsaw + Sigma.
  3. Feed detections into ForenSynth AI for summarization.
  4. Compare findings across runs for coverage validation.

🧭 This utility turns empty environments into living DFIR sandboxes — perfect for sharpening detection engineering and AI-assisted analysis.