Objective
To detect malware and botnet communications by analyzing network flow patterns using machine learning. This project focuses on identifying malicious traffic signatures in NetFlow/sFlow data without deep packet inspection, enabling scalable threat detection across large networks.
Business Value
- Threat Detection: Identify malware communications and botnet activities in real-time
- Network Security: Protect against data exfiltration and command-and-control traffic
- Scalable Analysis: Analyze network flows without performance-impacting deep packet inspection
- Incident Response: Enable rapid identification and containment of infected devices
- Compliance: Meet security monitoring requirements for regulated industries
Core Libraries
- pandas: Flow data manipulation and feature engineering
- scikit-learn: Classification algorithms and model evaluation
- numpy: Numerical analysis of flow statistics
- matplotlib & seaborn: Threat visualization and pattern analysis
- kaggle: Access to malware flow datasets
Technical Approach
Model: Random Forest or XGBoost for robust classification- Features: Flow duration, packet sizes, timing patterns, protocol distributions
- Target: Binary classification (Benign vs Malicious) or multi-class (specific malware families)
- Evaluation: Focus on high recall to minimize missed threats
Key Features
- Flow-based feature engineering
- Botnet family classification
- Real-time threat scoring
- Network flow pattern analysis
- Integration with SIEM systems
Dataset
Malware/Botnet network flow datasets from security research organizations, focusing on C&C communications and data exfiltration patterns.
Files Structure
008_Malware_Botnet_Detection_Flow_Data/
├── README.md # This guide
├── notebook.ipynb # Complete implementation
├── requirements.txt # Dependencies
└── flow_features.py # Feature engineering utilities