  Anonymity systems like Tor are vulnerable to Website Fingerprinting (WF)
attacks, where a local passive eavesdropper infers the victim's activity.
Current WF attacks based on deep learning classifiers have successfully
overcome numerous proposed defenses. While recent defenses leveraging
adversarial examples offer promise, these adversarial examples can only be
computed after the network session has concluded, thus offer users little
protection in practical settings.
  We propose Dolos, a system that modifies user network traffic in real time to
successfully evade WF attacks. Dolos injects dummy packets into traffic traces
by computing input-agnostic adversarial patches that disrupt deep learning
classifiers used in WF attacks. Patches are then applied to alter and protect
user traffic in real time. Importantly, these patches are parameterized by a
user-side secret, ensuring that attackers cannot use adversarial training to
defeat Dolos. We experimentally demonstrate that Dolos provides 94+% protection
against state-of-the-art WF attacks under a variety of settings. Against prior
defenses, Dolos outperforms in terms of higher protection performance and lower
information leakage and bandwidth overhead. Finally, we show that Dolos is
robust against a variety of adaptive countermeasures to detect or disrupt the
defense.
