CPWF: Cross-Platform Website Fingerprinting Based on Multi-Similarity Loss

Website Fingerprinting (WF) attacks aim to identify the website label of anonymous website traffic trace. WF attacks are suitable for anonymous network scenario because it only needs to analyze the information contained in the packet header without analyzing the content of the packet payload. WF attacks are important means to combat anonymous cyber-crimes. However, with anonymous networks are widely deployed on different devices such as PC and mobile devices, the sources of anonymous website traffic have become more diverse, and differences in devices will cause differences in anonymous website traffic. Existing WF attacks only use anonymous website traffic collected from the same device to train the classifier, and the performance will be significantly reduced while it is trying to identify the mixed anonymous website traffic generated by different devices. In this study, we propose cross-platform website fingerprinting (CPWF) attack based on multi-similarity loss. First, we use the multi-similarity loss to train a deep learning-based website fingerprint extraction model. The model is able to extract a feature set for anonymous website traffic classification, ignoring the differences caused by different devices, so that attackers can use the anonymous website traffic collected from the single terminal device to train the classifier, the classifier capable of identifying anonymous website traffic collected from all devices effectively.