CMAES-WFD: Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy
Di Wang, Yuefei Zhu, Jinlong Fei*, Maohua Guo
School of Cyberspace Security, Information Engineering University, Zhengzhou, 450000, China
* Corresponding Author: Jinlong Fei. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.049504
Received 09 January 2024; Accepted 13 March 2024; Published online 26 April 2024
Abstract
Website fingerprinting, also known as WF, is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination, even when using the Tor anonymity network. While advanced attacks based on deep neural network (DNN) can perform feature engineering and attain accuracy rates of over 98%, research has demonstrated that DNN is vulnerable to adversarial samples. As a result, many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success. However, these methods suffer from high bandwidth overhead or require access to the target model, which is unrealistic. This paper proposes CMAES-WFD, a black-box WF defense based on adversarial samples. The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES) optimization algorithm. Perturbations are injected into the local parts of the original traffic to control bandwidth overhead. According to the experiment results, CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting (DF) and VarCnn to below 8.3% and the bandwidth overhead to a maximum of only 14.6% and 20.5%, respectively. Specially, for Automated Website Fingerprinting (AWF) with simple structure, CMAES-WFD reduced the classification accuracy to only 6.7% and the bandwidth overhead to less than 7.4%. Moreover, it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.
Keywords
Traffic analysis; deep neural network; adversarial sample; Tor; website fingerprinting
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