Subverting Website Fingerprinting Defenses with Robust Traffic Representation

Meng Shen, Kexin Ji, Zhenbo Gao, Qi Li, Liehuang Zhu, Ke Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Citations (Scopus)

Abstract

Anonymity networks, e.g., Tor, are vulnerable to various website fingerprinting (WF) attacks, which allows attackers to perceive user privacy on these networks. However, the defenses developed recently can effectively interfere with WF attacks, e.g., by simply injecting dummy packets. In this paper, we propose a novel WF attack called Robust Fingerprinting (RF), which enables an attacker to fingerprint the Tor traffic under various defenses. Specifically, we develop a robust traffic representation method that generates Traffic Aggregation Matrix (TAM) to fully capture key informative features leaked from Tor traces. By utilizing TAM, an attacker can train a CNN-based classifier that learns common high-level traffic features uncovered by different defenses. We conduct extensive experiments with public real-world datasets to compare RF with state-of-the-art (SOTA) WF attacks. The closed- and open-world evaluation results demonstrate that RF significantly outperforms the SOTA attacks. In particular, RF can effectively fingerprint Tor traffic under the SOTA defenses with an average accuracy improvement of 8.9% over the best existing attack (i.e., Tik-Tok).

Original languageEnglish
Title of host publication32nd USENIX Security Symposium, USENIX Security 2023
PublisherUSENIX Association
Pages607-624
Number of pages18
ISBN (Electronic)9781713879497
Publication statusPublished - 2023
Event32nd USENIX Security Symposium, USENIX Security 2023 - Anaheim, United States
Duration: 9 Aug 202311 Aug 2023

Publication series

Name32nd USENIX Security Symposium, USENIX Security 2023
Volume1

Conference

Conference32nd USENIX Security Symposium, USENIX Security 2023
Country/TerritoryUnited States
CityAnaheim
Period9/08/2311/08/23

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