Know Your Victim: Tor Browser Setting Identification via Network Traffic Analysis

Chun Ming Chang, Hsu Chun Hsiao, Timothy Lynar, Tatsuya Mori

研究成果: Conference contribution

抄録

Network traffic analysis (NTA) is widely researched to fingerprint users' behavior by analyzing network traffic with machine learning algorithms. It has introduced new lines of de-anonymizing attacks [1] in the Tor network, inclusive of Website Fingerprinting (WF) and Hidden Service Fingerprinting (HSF). Previous work [4] observed that the Tor browser version may affect network traffic and claimed that having identical browsing settings between the users and adversaries is one of the challenges in WF and HSF. Based on this observation, we propose a NTA method to identify users' browser settings in the Tor network. We confirm that browser settings have notable impacts on network traffic and create a classifier to identify the browser settings. The classifier can establish over 99% accuracy under the closed-world assumption. The open-world assumption results indicate classification success except for one security setting option. Last, we provide our observations and insights through feature analysis and changelog inspection.

本文言語English
ホスト出版物のタイトルWWW 2022 - Companion Proceedings of the Web Conference 2022
出版社Association for Computing Machinery, Inc
ページ201-204
ページ数4
ISBN(電子版)9781450391306
DOI
出版ステータスPublished - 2022 4月 25
イベント31st ACM Web Conference, WWW 2022 - Virtual, Online, France
継続期間: 2022 4月 25 → …

出版物シリーズ

名前WWW 2022 - Companion Proceedings of the Web Conference 2022

Conference

Conference31st ACM Web Conference, WWW 2022
国/地域France
CityVirtual, Online
Period22/4/25 → …

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • ソフトウェア

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