Dark Web Traffic Detection Using Supervised Machine Learning

This paper presents a study on the application of supervised machine learning algorithms for the purpose of distinguishing and categorizing Virtual Private Network (VPN) and The Onion Router (TOR) traffic on the dark web. The dark web, characterized by its anonymity and inaccessibility, has become a popular platform for illicit activities such as drug trafficking, money laundering, and cybercrime. While VPNs and TOR can be used for legitimate purposes such as privacy protection and bypassing internet censorship, they can also be exploited by cybercriminals. The CIC-Darknet2020 dataset, which includes a comprehensive collection of network traffic captures from the dark web incorporating traffic features from both VPN and TOR technologies, is used for this study. We employ classification algorithms such as Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree classifiers to construct our model. The performance of the model is evaluated using parameters such as execution time, accuracy, precision, F-measure, and recall, utilizing five-fold and ten-fold cross-validation and 66/34 and 80/20 percentage splits. Our results show that the Decision Tree (J48) classifier outperforms other classifiers, achieving 99.6% accuracy with an execution time of 15 seconds for ten-fold cross-validation. The findings of this study have implications for enhancing cybersecurity measures in identifying and mitigating threats associated with VPN and TOR traffic on the dark web.