TForm-RF: An Efficient Data Augmentation for Website Fingerprinting Attack
Yongxin Chen, Yongjun Wang, Lumming Yang, Yuchuan Luo, Mantun Chen · 2022
Website fingerprinting (WF) attacks have become a significant threat to users’ privacy, even against Tor, one of the most famous anonymous communication tools. However, some limitations have prevented them from applying to the real world. A severe limitation for traditional deep-learning-based WF attacks is that a large amount of training data is required to gain classification capabilities. In this paper, we considered a more practical setting where the attacker could only obtain a few traffic trace instances for each target website, called the few-shot WF problem. Unlike previous work using transfer learning and demanding additional pre-training data, we leveraged data augmentation to generate additional virtual samples from the training sample neighborhood. It can expand the support for the distribution of training data, thereby solving the data hunger problem of deep-learning-based WF attacks. Specifically, we proposed a new augmentation method called Trace-Form Based Refill (TForm-RF), which is tailored to the intrinsic properties of website traffic trace. From cell sequences, we extract TForms to figuratively represent traffic patterns and refill them randomly to generate meaningful new instances. We evaluate this idea with several reasonable experiments. We illustrated that TForm-RF is much more effective than prior HDA and has a competitive advantage over TF in both closed-world and open-world scenarios.