Detection of obfuscated Tor traffic based on bidirectional generative adversarial networks and vision transform

Abstract

The Onion Router (TOR) network is a decentralized system of volunteer-run servers that aims to protect the anonymity and privacy of users by routing their internet traffic through a series of nodes. Individuals who use the TOR network may employ obfuscated traffic to conceal their internet activity from network administrators or security systems attempting to block or monitor them. Furthermore, some may use obfuscated Tor traffic to hide illegal activities, such as buying and selling illegal goods or accessing illegal services on the dark web. Despite efforts to identify and block Tor traffic, challenges remain, such as a limited set of features for identification, leading to false positives and negatives. To address these challenges, this paper proposes a novel approach using Visual Transformation (ViT), and augmentation by Bidirectional Generative Adversarial Networks (BiGAN). The proposed approach demonstrates superior performance on the ISCX-Tor2016 dataset, achieving 99.59% accuracy, 99.83% recall, 99.72% precision, and 99.78% F-score, thereby outperforming current state-of-the-art techniques.