Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning

Website fingerprinting enables a local eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting attacks have been shown to be effective even against Tor. Recently, lightweight website fingerprinting defenses for Tor have been proposed that substantially degrade existing attacks: WTF-PAD and Walkie-Talkie. In this work, we propose a new website fingerprinting attack against Tor that leverages a type of deep learning called convolution neural networks (CNN), and we evaluate this attack against WTF-PAD and Walkie-Talkie. The CNN attack attains over 98% accuracy on Tor traffic without defenses, better than all prior attacks, and it is also effective against WTF-PAD with over 90% accuracy. Walkie-Talkie remains effective, holding the attack to just 49.7% accuracy. In a realistic open-world setting, our attack remains effective, with 0.99 precision and 0.94 recall on undefended traffic. Against traffic defended with WTF-PAD in this setting, the attack still can get 0.95 precision and 0.70 recall. These findings highlight the need for effective defenses that defeat deep-learning attacks and that could be deployed in Tor.