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SOC 2.0 — Self-Hosted SIEM/SOAR Lab

A working Security Operations Center built from open-source parts and driven with real telemetry from real machines — not synthetic demo data. This repository is a curated, public showcase of the detection engineering, telemetry pipeline, and lab automation. The full platform lives in a separate private repository.

▶ Live dashboard: (enable GitHub Pages on this repo — see below) The landing page (index.html) is a sci-fi command console rendered from a real snapshot of the running lab.


What it actually is

Layer Implementation
SIEM core FastAPI + PostgreSQL + Redis + OpenSearch + Vector, Dockerized, behind a Caddy TLS proxy
Detection A real Sigma engine with logsource routing + field-mapping, running 618 rules (18 custom + 600 vendored SigmaHQ)
Correlation Stateful threshold + sequence engine (brute-force, password-spray, multi-stage kill-chains)
Endpoints 2 real VirtualBox VMs — Ubuntu 24.04 (auditd → rsyslog) and Windows 11 (Sysmon), each running a Wazuh agent
Attack simulation Atomic Red Team on the Windows victim
Enrichment AbuseIPDB + VirusTotal + OTX with consensus scoring; self-hosted IOC store

Real results (measured 2026-08-09)

  • 2 live endpoints + a SOC core, both Wazuh agents enrolled and Active simultaneously
  • Real attack chain detected end-to-end: Recon → Credential Access (T1003) raised a critical multi-stage kill-chain alert from genuine host telemetry, not an injected event
  • Detections on real data include encoded PowerShell, Mimikatz, LOLBins, and Linux discovery — plus vendored SigmaHQ rules firing on real activity for the first time
  • Host under load during the run: 68% CPU / 83% RAM (AMD Ryzen 5 5500U, 12 threads)

What's in this repo

index.html                          the live command-center dashboard (GitHub Pages)
detection/rules/*.yml               the 18 custom Sigma rules (Windows, Linux, cloud, WiFi, exfil)
detection/correlation/*.yml         threshold + sequence correlation rules
pipeline/auditd_normalizer.py       Linux auditd -> ECS (the parser that made Linux rules fire)
pipeline/threat_intel_scoring.py    TI consensus scoring + the false-positive story
lab/provision_linux_endpoint.sh     turns a bare Ubuntu VM into a monitored endpoint
lab/provision_windows_endpoint.ps1  Sysmon + Wazuh + Atomic Red Team on a Windows victim
docs/ENGINEERING-NOTES.md           the bugs that only real telemetry exposed, and their fixes

This is a subset, chosen for signal — the full FastAPI backend, frontend, and infrastructure are not published here.

The honest part

Most of the engineering value in this project came from pointing real machines at it and fixing what broke. A rule that fired wrong on real traffic and was then tuned is stronger evidence than a rule that passed a test its author wrote. See docs/ENGINEERING-NOTES.md for the specifics — including a threat-intel scoring bug that produced ~82% false positives on live traffic and how it was diagnosed in layers and fixed.

Enable the dashboard (GitHub Pages)

  1. Push this directory to a new public GitHub repository.
  2. Settings → Pages → Build and deployment → Source: Deploy from a branch.
  3. Branch: main, folder: / (root). Save.
  4. The dashboard goes live at https://<user>.github.io/<repo>/ in ~1 minute.

Stack

SIEM · SOAR · Sigma · MITRE ATT&CK · Detection Engineering · Wazuh · Sysmon · auditd · OpenSearch · Docker · Atomic Red Team · Threat Intelligence · Python · FastAPI


Lab snapshot and dashboard data are real measured values. The dashboard's packet animation is scripted; it is a self-contained portfolio piece, not a remote console.

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SOC 2.0 lab showcase: interactive dashboard, detection rules, telemetry engineering

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