SoK: A Critical Evaluation of Efficient Website Fingerprinting Defenses

Nate Mathews, James K. Holland, Se Eun Oh, Mohammad Saidur Rahman, Nicholas Hopper, Matthew Wright

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Recent website fingerprinting attacks have been shown to achieve very high performance against traffic through Tor. These attacks allow an adversary to deduce the website a Tor user has visited by simply eavesdropping on the encrypted communication. This has consequently motivated the development of many defense strategies that obfuscate traffic through the addition of dummy packets and/or delays. The efficacy and practicality of many of these recent proposals have yet to be scrutinized in detail. In this study, we re-evaluate nine recent defense proposals that claim to provide adequate security with low-overheads using the latest Deep Learning-based attacks. Furthermore, we assess the feasibility of implementing these defenses within the current confines of Tor. To this end, we additionally provide the first on-network implementation of the DynaFlow defense to better assess its real-world utility.

Original languageEnglish (US)
Title of host publicationProceedings - 44th IEEE Symposium on Security and Privacy, SP 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages969-986
Number of pages18
ISBN (Electronic)9781665493369
DOIs
StatePublished - 2023
Event44th IEEE Symposium on Security and Privacy, SP 2023 - Hybrid, San Francisco, United States
Duration: May 22 2023May 25 2023

Publication series

NameProceedings - IEEE Symposium on Security and Privacy
Volume2023-May
ISSN (Print)1081-6011

Conference

Conference44th IEEE Symposium on Security and Privacy, SP 2023
Country/TerritoryUnited States
CityHybrid, San Francisco
Period5/22/235/25/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • anonymous system
  • deep learning
  • defense
  • privacy
  • website fingerprinting

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