Seamless Website Fingerprinting in Multiple Environments
Chuxu Song, Zining Fan, Hao Wang, Richard Martin

TL;DR
This paper introduces a practical, site-level website fingerprinting attack using CNNs that achieves over 90% accuracy across diverse environments, challenging assumptions about the attack's feasibility and evaluating defenses.
Contribution
It presents a novel site-level classification method with a CNN that requires only packet jitter and size, improving realism and robustness of WF attacks.
Findings
Over 90% accuracy in website identification
Training data variability is crucial for effectiveness
Domain adaptation improves attack robustness
Abstract
Website fingerprinting (WF) attacks identify the websites visited over anonymized connections by analyzing patterns in network traffic flows, such as packet sizes, directions, or interval times using a machine learning classifier. Previous studies showed WF attacks achieve high classification accuracy. However, several issues call into question whether existing WF approaches are realizable in practice and thus motivate a re-exploration. Due to Tor's performance issues and resulting poor browsing experience, the vast majority of users opt for Virtual Private Networking (VPN) despite VPNs weaker privacy protections. Many other past assumptions are increasingly unrealistic as web technology advances. Our work addresses several key limitations of prior art. First, we introduce a new approach that classifies entire websites rather than individual web pages. Site-level classification uses…
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Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Hate Speech and Cyberbullying Detection · Authorship Attribution and Profiling
