Behavioral analytics for nation-state and negligent insider threat detection
Built by Koketso Raphasha — Practical AI for Africa
Advanced insider threat detection simulator that models nation-state actors, malicious insiders, and negligent employees. Uses behavioral analytics, risk scoring, and scenario-based simulation for security team training.
- Nation-State Scenarios — Advanced persistent threat simulation
- Negligent Insider Models — Accidental data exposure scenarios
- Behavioral Analytics — User and entity behavior analysis (UEBA)
- Risk Scoring — Weighted multi-factor risk assessment
- Alert Generation — Real-time alerting with playbook recommendations
- Reporting Dashboard — Executive summaries and detailed logs
graph LR
USER[User] --> API[FastAPI]
API --> PROC[Processor]
PROC --> DB[(Database)]
API --> AUTH[Auth Layer]
PROC --> AI[AI/ML Engine]
Microservices-based architecture with API Gateway, authentication layer, PostgreSQL persistence, and event-driven communication.
git clone https://github.com/Raphasha27/Insider-Threat-Detector.git
cd Insider-Threat-Detector
pip install -r requirements.txt
python detect.py| Project | Description |
|---|---|
| DDOS-Detection-Simulator | Traffic analysis and DDoS alert generation |
| Phishing-Awareness-Game | Educational security awareness training |
| Network-Port-Scanner | Multi-threaded network scanning and banner grabbing |
GitHub (this repo)
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Portfolio → https://raphasha27.github.io/raphasha-dev-portfolio
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Contact → https://github.com/Raphasha27
MIT — see LICENSE