Real-time Anomaly Detection for Network Intrusion using Explainable AI

Network security remains a critical challenge in today's world, with the ever-evolving landscape of cyber threats demanding new and adaptable solutions.

Traditional rule-based methods for anomaly detection in network traffic struggle to keep pace with the sophistication of attackers, often requiring manual intervention, and lacking the flexibility to adapt to novel attack vectors.

This project seeks to address these limitations by leveraging the power of advanced statistical learning, specifically focusing on the field of Explainable Artificial Intelligence (XAI), to develop a real-time anomaly detection system for network Intrusion Detection Systems (IDS).