Build a Network Traffic Monitoring and Anomaly Detection System

Attackers often exploit network-level weaknesses long before system-level compromises. Monitoring traffic flow helps identify patterns of DDoS, malware propagation, lateral movement, and data exfiltration. Early anomaly detection strengthens your overall defense posture.

The system captures real-time traffic from a network interface, extracts flow statistics like protocol usage, source/destination IPs, ports, and byte counts. It then applies rule-based filters or machine learning models to identify suspicious patterns.

Traffic is captured using a sniffer tool and parsed into flows. Features like packet count, source ports, connection time, and packet size variation are used to classify normal vs. anomalous behavior. Alerts are raised for traffic that deviates from learned baselines.

• Interface starts in monitor mode and listens to network traffic.
• Data is processed into structured logs and features are extracted per connection or time interval.
• ML model classifies incoming traffic as normal or anomalous in real-time.
• Flagged flows are logged, visualized, and optionally blocked or reported to admins.
• Historical data is stored for threat intelligence and trend analysis.

• Scapy Documentation
• PyShark – Wireshark Python API
• Anomaly Detection in scikit-learn
• NSL-KDD or CICIDS2017 Dataset
• Streamlit for ML Dashboards