Realistic Website Fingerprinting By Augmenting Network Trace
Alireza Bahramali, Ardavan Bozorgi, Amir Houmansadr

TL;DR
This paper introduces NetAugment, a trace augmentation method that improves website fingerprinting accuracy in unobserved network conditions by training classifiers on augmented data, demonstrating superior performance over existing techniques.
Contribution
The paper presents NetAugment, a novel trace augmentation technique tailored for Tor, enhancing WF classifier robustness across diverse network conditions using semi- and self-supervised learning.
Findings
NetAugment improves WF attack accuracy in unobserved network conditions.
Self-supervised NetCLR achieves up to 80% accuracy with 5-shot learning.
Augmentation leads to better generalization compared to state-of-the-art methods.
Abstract
Website Fingerprinting (WF) is considered a major threat to the anonymity of Tor users (and other anonymity systems). While state-of-the-art WF techniques have claimed high attack accuracies, e.g., by leveraging Deep Neural Networks (DNN), several recent works have questioned the practicality of such WF attacks in the real world due to the assumptions made in the design and evaluation of these attacks. In this work, we argue that such impracticality issues are mainly due to the attacker's inability in collecting training data in comprehensive network conditions, e.g., a WF classifier may be trained only on samples collected on specific high-bandwidth network links but deployed on connections with different network conditions. We show that augmenting network traces can enhance the performance of WF classifiers in unobserved network conditions. Specifically, we introduce NetAugment, an…
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Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Hate Speech and Cyberbullying Detection · Adversarial Robustness in Machine Learning
