AI-Driven APT Detection Platform
<System Architecture Diagram />
Overview
Design a professional, dark-themed cybersecurity portfolio section that presents an advanced AI-driven system for detecting Advanced Persistent Threats (APT) in enterprise networks. The project represents a realistic SOC-oriented solution, combining machine learning, behavioral analysis, and threat intelligence to detect stealthy, long-term cyberattacks that evade traditional signature-based systems.
Values & Objectives
- Detect low-and-slow APT attacks in real time
- Reduce false positives compared to classic IDS/IPS
- Provide actionable alerts for SOC analysts
- Support explainable AI (XAI) for security decisions
Target Audience
Designed for Security Operations Centers (SOC) in medium-to-large enterprises to assist analysts in identifying stealthy APT campaigns before damage occurs.
Technical Architecture
// AI & Tech Stack
- Anomaly Detection: Isolation Forest, Autoencoders (Unsupervised)
- Classifiers: Random Forest, XGBoost (Supervised)
- Data Ingestion: NetFlow, PCAP, System Logs (ELK Pipeline)
- Explainability: SHAP values for alert justification
Features Extracted
The system engineers features from raw traffic to identify malicious patterns:
- Temporal behavior patterns (beaconing)
- Privilege escalation indicators
- Command & Control (C2) anomalies
- Lateral movement detection via sequence patterns
Impact
This solution improves SOC efficiency by filtering noise and highlighting high-confidence threats with AI-assisted decision making, serving as a scalable and production-ready concept for modern defense.