Online Website Fingerprinting: Evaluating Website Fingerprinting Attacks on Tor in the Real World


Giovanni Cherubin, Alan Turing Institute; Rob Jansen, U.S. Naval Research Laboratory; Carmela Troncoso, EPFL SPRING Lab

Distinguished Paper Award Winner and Second Prize Winner (tie) of the 2022 Internet Defense Prize


Website fingerprinting (WF) attacks on Tor allow an adversary who can observe the traffic patterns between a victim and the Tor network to predict the website visited by the victim. Existing WF attacks yield extremely high accuracy. However, the conditions under which these attacks are evaluated raises questions about their effectiveness in the real world. We conduct the first evaluation of website fingerprinting using genuine Tor traffic as ground truth and evaluated under a true open world. We achieve this by adapting the state-of-the-art Triplet Fingerprinting attack to an online setting and training the WF models on data safely collected on a Tor exit relay—a setup an adversary can easily deploy in practice. By studying WF under realistic conditions, we demonstrate that an adversary can achieve a WF classification accuracy of above 95% when monitoring a small set of 5 popular websites, but that accuracy quickly degrades to less than 80% when monitoring as few as 25 websites. We conclude that, although WF attacks may be possible, it is likely infeasible to carry them out in the real world while monitoring more than a small set of websites.

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@inproceedings {277132,
author = {Giovanni Cherubin and Rob Jansen and Carmela Troncoso},
title = {Online Website Fingerprinting: Evaluating Website Fingerprinting Attacks on Tor in the Real World},
booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
year = {2022},
isbn = {978-1-939133-31-1},
address = {Boston, MA},
pages = {753--770},
url = {},
publisher = {USENIX Association},
month = aug,