SOC-in-a-Box is a hands-on security engineering project that simulates how a real Security Operations Center (SOC) detects, correlates, investigates, and manages security incidents.
This project was built incrementally (step-by-step) to reflect how SOC platforms evolve in real environments.
SOC-in-a-Box takes raw log data and turns it into actionable security incidents by:
- Ingesting logs
- Running detection logic
- Correlating alerts
- Building incident timelines (DFIR)
- Managing cases
- Displaying SOC dashboards
- Parses authentication, web, and firewall logs
- Normalizes events into a common structure
- Brute-force login detection
- Suspicious successful login detection
- Web access anomaly detection
- Groups related alerts into a single incident
- Reduces alert noise
- Produces higher-confidence incidents
- Orders events chronologically
- Helps analysts understand attack progression
- Maps detected activity to MITRE ATT&CK techniques
- Adds threat context to incidents
- Automatic case creation
- Case lifecycle tracking (OPEN, INVESTIGATING, ESCALATED, CLOSED)
- HIGH severity → Escalated automatically
- MEDIUM severity → Marked for investigation
- LOW severity → Auto-closed
Used like an internal SOC tool:
- Case counts
- Severity breakdown
- Status overview
- Recent incidents
Run:
python dashboard/soc_dashboard.pyRead-only dashboard for demonstration purposes.
- Displays case statistics
- Does not modify data
Run:
cd web_dashboard
python app.pypython main.pyThis runs:
- Log ingestion
- Detections
- Correlation
- DFIR timeline building
- Case creation
- Automation
- Dashboard output
SOC-in-a-Box/
├── detections/
├── correlation/
├── mitre/
├── case_management/
├── dashboard/
├── web_dashboard/
├── logs/
├── main.py
└── README.md
The following components are intentionally designed but not fully implemented yet:
- Risk scoring engine
- Automated response actions
- External threat intelligence ingestion
- Hosted web dashboard (Render-ready)
These represent realistic SOC platform growth phases.
- SOC analyst workflows
- Detection engineering mindset
- Incident response and DFIR thinking
- Alert correlation and noise reduction
- Case lifecycle management
This project is for educational and portfolio demonstration purposes only. It is not intended for production use.