Engineering a Real-Time ML Network IDS Dashboard

Sentinel is a real-time network intrusion detection dashboard that bridges the gap between training a machine learning model and deploying it against live traffic. Built using a Random Forest classifier trained on the NSL-KDD dataset, it captures live packets, reconstructs the expected features, and displays attack verdicts instantly. The system achieved 99.68% validation accuracy and successfully flagged a simulated SYN flood attack while correctly ignoring normal ping traffic.