Prism: Real-Time Privacy Protection Against Temporal Network Traffic Analyzers

IEEE Transactions on Information Forensics and Security. (TIFS) 2023.04.13,

Wenhao Li, Xiao-Yu Zhang, Huaifeng Bao, Binbin Yang, Zhaoxuan Li, Haichao Shi, Qiang Wang.


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 Prism, to protect the live connection privacy against attacks of temporal network traffic analyzers. Specifically, Prism 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, Prism defends against the analyzers with online traffic perturbation. Since the Prism 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.