Short Paper - Taming the Shape Shifter: Detecting Anti-fingerprinting Browsers

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Abstract

When it comes to leaked credentials and credit card information, we observe the development and use of anti-fingerprinting browsers by malicious actors. These tools are carefully designed to evade detection, often by mimicking the browsing environment of the victim whose credentials were stolen. Even though these tools are popular in the underground markets, they have not received enough attention by researchers. In this paper, we report on the first evaluation of four underground, commercial, and research anti-fingerprinting browsers and highlight their high success rate in bypassing browser fingerprinting. Despite their success against well-known fingerprinting methods and libraries, we show that even slightest variation in the simulated fingerprint compared to the real ones can give away the presence of anti-fingerprinting tools. As a result, we provide techniques and fingerprint-based signatures that can be used to detect the current generation of anti-fingerprinting browsers.

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CITATION STYLE

APA

Amin Azad, B., Starov, O., Laperdrix, P., & Nikiforakis, N. (2020). Short Paper - Taming the Shape Shifter: Detecting Anti-fingerprinting Browsers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12223 LNCS, pp. 160–170). Springer. https://doi.org/10.1007/978-3-030-52683-2_8

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