Network Traffic Anomaly Detection

Project Overview

This project focuses on implementing unsupervised learning techniques to detect anomalies in network traffic patterns. Using techniques like Isolation Forest, we'll learn to identify unusual network behavior that could indicate security threats or performance issues.

Key Learning Objectives

• Understanding network traffic data structures and features
• Implementing Isolation Forest for anomaly detection
• Feature engineering for network traffic analysis
• Evaluating unsupervised learning models
• Visualizing and interpreting anomaly detection results

Technologies Used

• Python
• scikit-learn (Isolation Forest)
• pandas for data manipulation
• matplotlib and seaborn for visualization

Additional Resources

For detailed implementation guide, code examples, and step-by-step instructions, please visit the GitHub Project Repository.