Build a Real-time Intrusion Detection System Using AI
Leverage AI and machine learning to build a real-time intrusion detection system that monitors network activity and flags anomalies — an advanced cybersecurity project for proactive defense.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.
Live Traffic Capture
Use packet sniffing tools to monitor network packets in real time from the system interface.
Feature Extraction from Packets
Extract relevant attributes like protocol type, byte count, TCP flags, and connection duration.
Machine Learning-based Classification
Train a model on a labeled dataset (e.g., NSL-KDD) and use it to classify real-time traffic.
Real-Time Alerting and Logging
Notify users or admins when intrusions are detected and log all events with timestamps.
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.
Traffic Capture Tools
Scapy, pyshark, or tshark for packet sniffing and protocol analysis.
ML Framework
scikit-learn or TensorFlow for training and deploying detection models.
Frontend & Dashboard
Streamlit or React.js for real-time threat visualization and logs.
Data Sources
NSL-KDD, CIC-IDS2017, or custom labeled datasets for model training.
1. Prepare Dataset and Train ML Model
Use labeled network traffic datasets and train a model to classify traffic types.
2. Build Packet Sniffer Module
Capture and parse live packets into structured records using tools like pyshark.
3. Integrate Model for Real-Time Prediction
Deploy the trained model into a backend API or directly in the app for prediction.
4. Create UI for Monitoring
Build a dashboard to show ongoing traffic, flagged threats, and system health.
5. Add Alert & Response System
Trigger email/SMS alerts or automate response actions like blocking IPs.
Detect Threats the Moment They Happen
Build a real-time intrusion detection system powered by AI and protect networks with proactive, intelligent monitoring.
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