Website Fingerprinting on Access network and Core Gateway

Mei, Hantao; Cheng, Guang; Gao, Wei; Chen, Junqiang

doi:10.1109/msn53354.2021.00101

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…Kailong Wang et al [94] proposed the novel intra-domain WF attack that aimed to differentiate the webpages within the same social media website, which employed RF classifier and utilized temporal and volumetric features including CDN bursts. Mei et al [95] evaluated WF attacks on Tor using statistical features or package sequence features, based on C4.5, RF, kNN, or Quadratic Discriminant Analysis (QDA) classifiers, separately. Okazaki et al [96] proposed a WF attack using SVM classifier, and Virtual Set Size fluctuation of a specific process regarding website browsing as a feature.…”

Section: A Approachesmentioning

confidence: 99%

A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

Liu

2023

IEEE Access

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The attacks and defenses on the information of which website pages are visited by users are important research subjects in the field of privacy enhancing technologies, they are termed as website fingerprinting (WF) attacks and defenses. Nowadays, deep learning is an important tool in many research areas, including WF attacks and defenses. In this paper, we offer a comprehensive survey on deep learning for WF attacks and defenses. After a brief introduction, we first summarize deep learning, WF attacks, and WF defenses. For deep learning, we review the common paradigms, architectures, and performance metrics. For WF attacks, we review the approaches, challenges and solutions. The approaches include deep learning, traditional machine learning, and other methods. Challenges and solutions cover multi-tab browsing, concept drift, and the base rate fallacy. For WF defenses, we review the strategies and approaches. Then, we survey deep learning for WF attacks, and deep learning for WF defenses. In deep learning for WF attacks, we survey in detail the deep learning paradigms, architectures of WF attack models, and the performance of several representative WF attack models, and look into the future. In deep learning for WF defenses, we survey the architecture, efficacy and overhead of deep learning models in WF defenses, and look into the future. In the end, we summarize this paper.

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Section: A Approachesmentioning

confidence: 99%

A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

Liu

2023

IEEE Access

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Website Fingerprinting on Access network and Core Gateway

Cited by 1 publication

References 22 publications

A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

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