Resisting DNN-Based Website Fingerprinting Attacks Enhanced by Adversarial Training.

Deep neural network (DNN) based website fingerprinting (WF) attacks pose a severe threat to the privacy of Tor users. To overcome this challenge, adversarial perturbation based WF defenses have been recently proposed to fool the classifiers of attackers, through purposefully perturbing the user's traffic traces. Unfortunately, these defenses significantly deteriorate once the WF attacks are enhanced with adversarial training (AT). AT endows the WF attacks with more powerful website recognition capability, through learning the perturbed traffic traces generated by attackers. To resist the WF attacks enhanced by AT, we develop a black-box WF defense, called Acup3. First, Acup3 leverages many-to- one website imitation to make the traffic traces associated with different websites look more like each other, increasing the difficulty of website classification. Second, Acup3 generates trace-agnostic perturbations without accessing traffic traces, making it suitable for practical deployment. Third, Acup3 employs perturbation variation to diversify the traffic traces of different users visiting the same website, making the knowledge learnt from AT less helpful for WF attacks. Therefore, Acup3 is more robust against AT. Experiments demonstrate Acup3 markedly surpasses four representative WF defenses (e.g., Mockingbird and AWA) in defense capability and bandwidth overhead. Facing the state-of-the-art (SOTA) attack Var-CNN enhanced with AT, Acup3 depresses its attack success rate (ASR) from 98% to 24.29% with only 13.95% bandwidth overhead. Compared to the SOTA defense AWA, Acup3 causes a 24.5% larger decrement in ASR of WF attacks, and achieves a more than 100 times faster speed of perturbation generation.