Improving Meek with Adversarial Techniques

Abstract

As the internet becomes increasingly crucial to distributing information, internet censorship has become more pervasive and advanced. Tor aims to circumvent censorship, but adversaries are capable of identifying and blocking access to Tor. Meek, a traffic obfuscation method, protects Tor users from censorship by hiding traffic to the Tor network inside an HTTPS connection to a permitted host. However, machine learning attacks using side-channel information against Meek pose a significant threat to its ability to obfuscate traffic. In this work, we develop a method to efficiently gather reproducible packet captures from both normal HTTPS and Meek traffic. We then aggregate statistical signatures from these packet captures. Finally, we train a generative adversarial network (GAN) to minimally modify statistical signatures in a way that hinders classification. Our GAN successfully decreases the efficacy of trained classifiers, increasing their mean false positive rate (FPR) from 0.183 to 0.834 and decreasing their mean area under the precision-recall curve (PR-AUC) from 0.990 to 0.414.