Machine Learning Approach for Classifying Encrypted Tor Traffic Payloads Using XGBoost Classifier - Kola Sivamani; GVS Ananthnath (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
The rapid evolution of internet technologies has necessitated advanced methodologies for monitoring and classifying encrypted network traffic. This study introduces a robust framework utilizing Machine Learning (ML) to classify Tor traffic encrypted payloads, an essential step for enhancing cybersecurity measures. Utilizing a dataset featuring columns such as Source Port, Destination Port, Protocol, Flow Duration, various Inter-Arrival Times (IAT), and others, we apply the XGBoost model. Our objective is to accurately predict the nature of traffic ('label' as the target column), thereby distinguishing between benign and potentially malicious activities. The effectiveness of the model is evaluated based on its predictive accuracy and computational efficiency, offering insights into the optimal approach for real-time encrypted traffic analysis. This research contributes to the development of more secure network environments by leveraging advanced data analytics in the realm of cybersecurity.
Keywords: Network traffic; machine learning; source port; destination port; protocol; flow duration; XGBoost classifier; cybersecurity