Mydvthukran/AI-Cyber-Defense-Threat-Intelligence-Platform

# AI Cyber Defense Threat Intelligence Platform A simple SOC/SIEM-style cybersecurity project to capture traffic, detect threats, visualize attacks, and optionally respond automatically. ## Project Flow (Simple) ### 1) Network Traffic Comes In - Packet capture tools: **Wireshark**, **Suricata** - Packets are treated as small data messages moving across the network ### 2) Traffic Monitoring The system watches packets in real time and checks: - Source/destination IPs - Ports and protocols - Packet/request type - Request frequency Examples: - Normal browsing ✅ - 500 login attempts in 1 minute 🚨 - Port scanning behavior 🚨 ### 3) Threat Detection Engine Two detection methods work together: **A. Rule-Based Detection (Snort/Suricata rules)** - Example: if failed login attempts exceed threshold → trigger alert **B. AI/ML Detection** - Learns normal behavior and flags anomalies - Example: normal user ~20 requests/min vs attacker ~2000 requests/min ### 4) Alert Generation When suspicious activity is detected: - Dashboard shows warning - Admin gets notification - Logs are stored for investigation Common alerts: - Port scan detected - Brute-force attack detected - Suspicious IP activity - Malware-like traffic pattern ### 5) Visualization Dashboard Frontend displays live security status: - Attack graphs - Traffic charts - Attacker IP list - Threat severity - Real-time event logs Suggested stack: **React + Chart.js** ### 6) Auto Response (Advanced) Optional automated actions: - Block IPs - Rate-limit/stop abusive requests - Isolate suspicious traffic Example: too many requests from one IP → auto-blacklist. ### 7) Honeypot (Advanced) Deploy a fake vulnerable target to study attackers safely. - Suggested tool: **Cowrie** - Captures attacker behavior, commands, and patterns for threat intelligence ## Very Simple Architecture Network Traffic ↓ Packet Capture ↓ Threat Detection Engine ↓ AI Analysis + Security Rules ↓ Alerts + Logs ↓ Dashboard Visualization ↓ Auto Response / Blocking ## Roles (Short) - **SOC Analyst**: monitors dashboard, validates alerts, escalates incidents. - **Security Engineer**: writes/tunes detection rules, manages Suricata/Snort, response policies. - **ML Engineer**: builds and improves anomaly detection models. - **Backend Engineer**: builds ingestion APIs, detection orchestration, alert services. - **Frontend Engineer**: develops dashboard, charts, and live incident views. - **DevOps/Platform Engineer**: deployment, scaling, logging pipeline, reliability. ## Requirements (Short) ### Core Tools - Packet capture/IDS: Wireshark, Suricata (and/or Snort rules) - Honeypot (optional): Cowrie - Frontend: React, Chart.js - Backend/ML stack (recommended): Python + FastAPI + scikit-learn ### Infrastructure - Linux environment for network tooling - Local lab/test network for safe attack simulation - Database/log storage (e.g., PostgreSQL/Elasticsearch) for alerts and history ### Safety - Run attack simulations only in authorized lab environments. - Never test offensive techniques on public or unauthorized systems. ## Repository Structure AI-Cyber-Defense-Threat-Intelligence-Platform/ ├── backend/ ├── frontend/ └── README.md