Effective Attacks and Provable Defenses for Website Fingerprinting

By Tao Wang, Xiang Cai, Rishab Nithyanand, Rob Johnson, Ian Goldberg. USENIX Security Symposium, 2014

507 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2e4b61ca5ff7af8743e4365edeb40cd87df15c5a

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Citations in the corpus

Circuit Fingerprinting Attacks: Passive Deanonymization of Tor Hidden Services (2015, background): “For WF attacks (Section 7), defenses proposed by previous works [35, 9] will be effective here as well.”
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples (2019, background): “…than membership inference attacks, many other attacks rely on machine learning classifiers, e.g., attribute inference attacks [11, 17, 28], website fingerprinting attacks [7, 22, 29, 46, 67], side-channel attacks [73], location attacks [5, 45, 52, 72], and author identification attacks [8, 41].”
Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning (2018, methodology): “Prior work in WF has shown that the most important features are derived from the lengths of traces in each direction [14, 38].”
Spying on the Smart Home: Privacy Attacks and Defenses on Encrypted IoT Traffic (2017, background): “Internet browsing habits are also subject to privacy breaches by website fingerprinting attacks using network traffic metadata [18, 21, 42].”
k-fingerprinting: A Robust Scalable Website Fingerprinting Technique (2015, methodology): “The total number of packets, along with the number of incoming and outgoing packets [12, 27, 39].”
Automated Website Fingerprinting through Deep Learning (2017, unknown): “Their results show that this attack achieves 90% to 95% accuracy on 100 websites [30].”
Accurate Decentralized Application Identification via Encrypted Traffic Analysis Using Graph Neural Networks (2021, methodology): “[28] used a large feature set and adopted a k-Nearest Neighbor”
Website Fingerprinting at Internet Scale (2016, methodology): “Recently, the authors further improved the recognition rates in larger open-world scenarios (> 5,000 URLs) using a novel k-Nearest Neighbor (k-NN) classifier, which also significantly reduces the time necessary for training compared to previous results [25].”