LOM: A Website Fingerprinting Defense Method based on Local Optimal Mutation

Website Fingerprinting (WF) is a traffic analysis attack. The potential local adversary can infer the user's web activity by extracting implicit pattern information from traffic traces. Leveraging the latest advances in Deep Learning, the adversary has achieved 98% detection accuracy in closed-world tests. This poses a new challenge to the traditional WF defense methods. In this paper, we propose a new defense method, the proposed dissimilarity measure and local optimal mutation strategy greatly reduce the computational cost. Experimental results show that the method achieves the generation of adversarial traffic trace with shorter preparation times and better defensive effects. Moreover, it also has great potential in the face of more powerful attacks that may emerge in the future.