Towards an Efficient Defense against Deep Learning based Website Fingerprinting

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Ling:2022:Communications,

• author = "Zhen Ling and Gui Xiao and Wenjia Wu and Xiaodan Gu and Ming Yang and Xinwen Fu",
• booktitle = "IEEE INFOCOM 2022 - IEEE Conference on Computer Communications",
• title = "Towards an Efficient Defense against Deep Learning based Website Fingerprinting",
• year = "2022",
• pages = "310--319",
• abstract = "Website fingerprinting (WF) attacks allow an attacker to eavesdrop on the encrypted network traffic between a victim and an anonymous communication system so as to infer the real destination websites visited by a victim. Recently, the deep learning (DL) based WF attacks are proposed to extract high level features by DL algorithms to achieve better performance than that of the traditional WF attacks and defeat the existing defense techniques. To mitigate this issue, we propose a-genetic-programming-based variant cover traffic search technique to generate defense strategies for effectively injecting dummy Tor cells into the raw Tor traffic. We randomly perform mutation operations on labeled original traffic traces by injecting dummy Tor cells into the traces to derive variant cover traffic. A high level feature distance based fitness function is designed to improve the mutation rate to discover successful variant traffic traces that can fool the DL-based WF classifiers. Then the dummy Tor cell injection patterns in the successful variant traces are extracted as defense strategies that can be applied to the Tor traffic. Extensive experiments demonstrate that we can introduce 8.percent of bandwidth overhead to significantly decrease the accuracy rate below 0.percent in the realistic open-world setting.",
• keywords = "genetic algorithms, genetic programming, Deep learning, Privacy, Computational modeling, Sociology, Bandwidth, Telecommunication traffic, Fingerprint recognition, Anonymous communication systems, website fingerprinting, cover traffic",
• DOI = "doi:10.1109/INFOCOM48880.2022.9796685",
• ISSN = "2641-9874",
• month = may,
• notes = "Also known as \cite{9796685}",

}

Genetic Programming entries for Zhen Ling Gui Xiao Wenjia Wu Xiaodan Gu Ming Yang Xinwen Fu

Citations