Build a Network Traffic Monitoring and Anomaly Detection System
Monitor packets and flow data across networks to detect abnormal behavior using real-time analysis and ML-based classification — a powerful cybersecurity project for network defense.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.
Packet Capture and Flow Logging
Use tools like Scapy or tcpdump to capture real-time packets and log connection flows.
Traffic Feature Extraction
Extract metadata such as protocol, IPs, ports, connection duration, and packet sizes.
Anomaly Detection
Use unsupervised ML models like Isolation Forest or Autoencoders to identify outliers.
Live Traffic Dashboard
Display network traffic summaries, flagged anomalies, and alerts in a real-time web UI.
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.
Traffic Capture
Scapy, tshark, or pyshark for real-time packet analysis.
Feature Engineering
Python (pandas, NumPy) to extract flow-level statistics and prepare datasets.
Anomaly Detection Models
scikit-learn (Isolation Forest, One-Class SVM) or TensorFlow (Autoencoders).
Visualization
Streamlit, Flask + Chart.js, or React.js for dashboards and alerts.
1. Capture and Log Packets
Use Scapy or pyshark to sniff packets and store logs with relevant connection data.
2. Extract Features from Traffic
Process data to extract fields like flow duration, average packet size, port entropy, etc.
3. Train Anomaly Detection Model
Use clean data to train a model that detects deviation from normal traffic behavior.
4. Build Alerting System
Notify admins when suspicious activity is detected in real time.
5. Add Dashboard and Historical Logs
Create a dashboard showing traffic summaries, live anomalies, and past logs for review.
Identify Threats from Network Behavior
Build a real-time anomaly detection engine that protects your network from subtle and advanced threats — powered by live traffic analysis and machine learning.