Prism: Real-Time Privacy Protection Against Temporal Network Traffic Analyzers
Wenhao Li, Xiaoyu Zhang, Huaifeng Bao, Binbin Yang, Zhaoxuan Li, Haichao Shi, Qiang Wang
Abstract
Traffic analysis is widely used in network monitoring. However, the attackers can sometimes infer sensitive information from the patterns of the encrypted network traffic, which poses a threat to network security. Most existing countermeasures are proposed to obfuscate traffic flows using adversarial examples. However, there are two challenges when adding perturbations to live network traffic. Firstly, the perturbations imposed on the feature space cannot be conveniently projected to original traffic flows in feature-space based methods. Secondly, it is laborious and impractical to apply symmetrical framework to encode/decode the adversarial traffic in traffic-space based approaches. To address the above issues, in this paper, we propose an asymmetric defending scheme, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prism</i> , to protect the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">live</i> connection privacy against attacks of temporal network traffic analyzers. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prism</i> first extracts standardized temporal features via Power-Law Division (PLD) algorithm, and then employs Time-stacked State Transition Model (TSTM) to obtain the fingerprint of each application. Finally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prism</i> defends against the analyzers with online traffic perturbation. Since the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prism</i> is designed as a traffic-space based defender with asymmetric defending structure, the deployment is lightweight and efficient. Experimental results on two real-world datasets demonstrate the effectiveness and generalization of our adversarial perturbations. In particular, it is encouraging to see that our proposed defending scheme outperforms the advanced countermeasures, such as adversarial training and traffic filter.