This project is a machine learning-based system that analyzes network traffic and identifies whether a connection is using a VPN or not. It is designed to help in understanding traffic behavior, enhancing network security, and detecting anonymity-based usage in monitored networks.
The system works by analyzing various features of network packets—like protocol type, packet lengths, and IP behavior—to classify them into VPN or Non-VPN traffic. It includes data preprocessing, feature engineering, model training using Random Forest, and evaluation with accuracy metrics and visualizations.
network-traffic-analyzer-vpn/ │ ├── src/ │ └── network_traffic_analyzer_vpn.ipynb # Main Jupyter notebook │ ├── data/ # Dataset files (add here) ├── requirements.txt # Required Python packages ├── .gitignore # Files/folders to ignore └── README.md # Project documentation
- 🔍 Analyzes real-world network traffic data
- 🧠 Detects VPN usage using Random Forest Classifier
- 📊 Data preprocessing and visualization using Pandas, Seaborn, Matplotlib
- ✅ Displays accuracy and classification results with confusion matrix
- Language: Python
- Notebook: Jupyter Notebook (
.ipynb) - Libraries:
pandas,numpy– Data handlingmatplotlib,seaborn– Visualizationscikit-learn– ML modeling and evaluation
- Clone the repository:
git clone https://github.com/your-username/network-traffic-analyzer-vpn.git cd network-traffic-analyzer-vpn