Build a Real-time Intrusion Detection System Using AI

Traditional signature-based intrusion detection systems struggle to keep up with modern threats. AI-based systems can analyze behavior, detect unknown attacks, and adapt over time. Real-time intrusion detection helps prevent data breaches and system compromise proactively.

The system captures live network traffic, extracts features like packet size, source/destination, and protocol, then feeds this data into a trained ML model to classify it as normal or malicious. Detected threats trigger real-time alerts and can optionally block traffic.

The system captures packets and extracts structured data. A trained machine learning model continuously receives these records and classifies them. If the traffic is deemed anomalous or malicious, the system raises alerts and optionally executes predefined responses.

• Capture packets using sniffers like Scapy or pyshark.
• Extract meaningful features for ML processing.
• Run real-time classification using a pre-trained model (e.g., Random Forest, SVM).
• Display alerts and log malicious connections.
• Allow admin actions such as traffic blocking or IP blacklisting.

• NSL-KDD Dataset for Intrusion Detection
• Scapy Network Tool
• pyshark Packet Analysis
• Real-Time ML with scikit-learn
• Streamlit for Real-Time Dashboards