  Website Fingerprinting (WF) is a type of traffic analysis attack that enables
a local passive eavesdropper to infer the victim's activity, even when the
traffic is protected by a VPN or an anonymity system like Tor. Leveraging a
deep-learning classifier, a WF attacker can gain over 98% accuracy on Tor
traffic. In this paper, we explore a novel defense, Mockingbird, based on the
idea of adversarial examples that have been shown to undermine machine-learning
classifiers in other domains. Since the attacker gets to design and train his
attack classifier based on the defense, we first demonstrate that at a
straightforward technique for generating adversarial-example based traces fails
to protect against an attacker using adversarial training for robust
classification. We then propose Mockingbird, a technique for generating traces
that resists adversarial training by moving randomly in the space of viable
traces and not following more predictable gradients. The technique drops the
accuracy of the state-of-the-art attack hardened with adversarial training from
98% to 42-58% while incurring only 58% bandwidth overhead. The attack accuracy
is generally lower than state-of-the-art defenses, and much lower when
considering Top-2 accuracy, while incurring lower bandwidth overheads.
