Fingerprinting Attack on Tor Anonymity using Deep Learning

Tor is free software that enables anonymous communication. It defends users against traffic analysis and network surveillance. It is also useful for confidential business activities and state security. At the same time, anonymized protocols have been used to access criminal websites such as those dealing with illegal drugs. This paper proposes a new method for launching a fingerprinting attack to analyze Tor traffic in order to detect users who access illegal websites. Our new method is based on Stacked Denoising Autoencoder, a deep-learning technology. Our evaluation results show 0.88 accuracy in a closed-world test. In an open-world test, the true positive rate is 0.86 and the false positive rate is 0.02.