Bit-FP: A Traffic Fingerprinting Approach for Bitcoin Hidden Service Detection

Zou, Kexin; Shi, Jinqiao; Gao, Yue; Wang, Xuebin; Wang, Meiqi; Li, Zeyu; Su, Majing

doi:10.1109/dsc53577.2021.00021

Cited by 1 publication

(1 citation statement)

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“…Hongcheng Zou et al [98] presented Probabilistic Fingerprinting (PF), a new WF attack based on kNN, using topic probability vectors of traffic instances as features. Kexin Zou et al [99] proposed a novel lightweight WF attack on Bitcoin hidden service, using a random decision forest classifier with features from TLS packet size and direction.…”

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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Bit-FP: A Traffic Fingerprinting Approach for Bitcoin Hidden Service Detection

Cited by 1 publication

References 11 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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