Be like a Chameleon: Protect Traffic Privacy with Mimicry
Zexiao Zou, Zhang Yan, Jin Chen, Jianyi Zhang, Zhiqiang Wang, Lei Ju, Ri Xu · 2023
By using traffic analysis attacks, attackers track users’ network traffic and analyze user behaviors, such as visited websites and online activities. Users’ personal privacy is at risk. Traffic obfuscation is a new way to protect privacy. Traffic obfuscation is commonly used in censory-circumvention systems to help Internet users bypass online censorship, but recent work has used it to disguise user traffic to evade adversary attacks. However, the current traffic obfuscation methods and rules are relatively simple, can not dynamically adapt to the network environment, and lack of uniform traffic feature similarity evaluation index. In this paper, we propose a generative adversarial network(GAN) model based on temporal convolutional network(TCN). TCN-GAN learns traffic features to achieve the purpose of disguising specific traffic as target traffic. At the same time, we provide the Measurement of Indiscernibility(MoI) for evaluating the difference between generated traffic features and real traffic features. Compared with the existing work, our experimental results show that in machine learning-based traffic analysis attacks, TCN-GAN has better performance on sample quality and mimicry effect, and MoI can be used as an effective index to evaluate the model generation effect.