Document Type

Article

Publication Date

6-30-2023

Publication Title

2023 Silicon Valley Cybersecurity Conference (SVCC)

Pages

1-8

Publisher Name

IEEE

Abstract

With increased privacy concerns, anonymity tools such as VPNs and Tor have become popular. However, the packet metadata such as the packet size and number of packets can still be observed by an adversary. This is commonly known as fingerprinting and website fingerprinting attacks have received a lot of attention recently as a known victim’s website visits can be accurately predicted, deanonymizing that victim’s web usage. Most of the previous work have been performed in laboratory settings and have made two assumptions: 1) a victim visits one website at a time, and 2) the whole website visit with all the network packets can be observed. To validate these assumptions, a new private webbrowser extension called WebTracker is deployed with real users. WebTracker records the websites visited, when the website loading starts, and when the website loading finishes. Results show that users’ browsing patterns are different than what was previously assumed. Users may browse the web in a way that acts as a countermeasure against website fingerprinting due to multiple websites overlapping and downloading at the same time. Over 15% of websites overlap with at least one other website and each overlap was 66 seconds. Moreover, each overlap happens roughly 9 seconds after the first website download has started. Thus, this reinforces some previous work that the beginning of a website is more important than the end for a website fingerprinting attack.

Comments

Author Posting © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The definitive version of this work was published in 2023 Silicon Valley Cybersecurity Conference (SVCC), (June 30, 2023), https://doi.org/10.1109/SVCC56964.2023.10164930.

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