Document Type

Article

Publication Date

12-1-2024

Publication Title

Computer Communications

Abstract

Website fingerprinting is a passive network traffic analysis technique that enables an adversary to identify the website visited by a user despite encryption and the use of privacy services such as Tor. Several website fingerprinting defenses built on top of Tor have been proposed to guarantee a user's privacy by concealing trace features that are important to classification. However, some of the best defenses incur a high bandwidth and/or latency overhead. To combat this, new defenses have sought to be both lightweight - i.e., introduce a small amount of bandwidth overhead - and zero-delay to real network traffic. This work introduces a novel zero-delay and lightweight website fingerprinting defense, called BRO, which conceals the feature-rich beginning of a trace while still enabling the obfuscation of features deeper into the trace without spreading the padding budget thin. BRO schedules padding with a randomized beta distribution that can skew to both the extreme left and right, keeping the applied padding clustered to a finite portion of a trace. This work specifically targets deep learning attacks, which continue to be among the most accurate website fingerprinting attacks. Results show that BRO outperforms other well-known website fingerprinting defenses, such as FRONT, with similar bandwidth overhead.

Comments

This work was supported in part by the U.S. National Science Foundation under Grant 2028397.

DOI

10.1016/j.comcom.2024.107976

Version

Publisher's PDF

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Volume

228

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