Detection of Virtual Private Networks Encrypted Traffic using Machine and Ensemble Learning Algorithms

Abstract: The widespread adoption of Virtual Private Networks (VPNs) for secure communication over public networks has highlighted the need for accurate detection of encrypted network traffic. This study proposes a comprehensive approach utilizing machine and ensemble learning algorithms to address this detection challenge. Leveraging the ISCX VPN dataset, the project aims to develop a robust system capable of distinguishing between VPN and non-VPN traffic with high accuracy. Key stages include data acquisition, preprocessing, feature extraction, and model training using Decision Tree, Naive Bayes, Random Forest, and Adaboost algorithms. Through rigorous experimentation, the most effective algorithmic approach is identified. The project also offers insights into the diverse nature of encrypted network communication and its implications for network security. The anticipated outcomes include improved understanding and management of VPN traffic, enhancing overall network security and performance. This research contributes to advancing network security practices by offering practical solutions for encrypted traffic detection in real-world settings.