AdvTraffic: Obfuscating Encrypted Traffic with Adversarial Examples

Website fingerprinting can reveal which sensitive website a user visits over encrypted network traffic. Obfuscating encrypted traffic, e.g., adding dummy packets, is considered as a primary approach to defend against website fingerprinting. How-ever, existing defenses relying on traffic obfuscation are either ineffective or introduce significant overheads. As recent website fingerprinting attacks heavily rely on deep neural networks to achieve high accuracy, producing adversarial examples could be utilized as a new way to obfuscate encrypted traffic. Unfortunately, existing adversarial example algorithms are designed for images and do not consider unique challenges for network traffic.In this paper, we design a new method, named AdvTraffic, which can customize perturbations produced by any existing adversarial example algorithm on images and derive adversarial examples over encrypted traffic. Our experimental results show that the integration of AdvTraffic, particularly with Generative Adversarial Networks, can effectively mitigate the accuracy of website fingerprinting from 95.0% to 10.2%, even if an attacker retrains a classifier with defended traffic. Compared to other defenses, our method outperforms most of them in mitigating attack accuracy and offers the lowest bandwidth overhead.