Machine Learning based Network Intrusion Detection System

This research develops an AI-driven Intrusion Detection System utilizing machine learning to classify contemporary network traffic. By analyzing high-fidelity datasets containing diverse adversarial vectors, the model identifies sophisticated threats in real-time. Integrating an automated SOC-style dashboard, the system minimizes detection latency and human intervention, providing superior accuracy compared to traditional signature-based mechanisms.