Tripod: Use Data Augmentation to Enhance Website Fingerprinting

Abstract

Website Fingerprinting (WF) enables a passive adversary to identify the website a user is visiting, even when the web access adopts security or privacy technologies. WF attacks based on deep learning are highly effective when feeding sufficient training traces, for example, hundreds of traffic traces of accessing each website. However, collecting extensive traffic consumes much time and resources, degenerating WF attacks' timeliness and invisibility. Nevertheless, decreasing training traces dramatically drops the WF accuracy. This paper proposes Tripod, a novel data augmentation method to enhance WF attacks, making them effective with a small training set. It applies three packet manipulations (Injecting, Removing, and Losing) on one collected traffic trace to generate several augmented traces. WF attacks then use the website classifier trained by the augmented set of all traces. In the closed-world scenario, the Var-CNN attack with 20 training traces per website only correctly identifies 56.1% of websites, while Tripod significantly increases this accuracy to 95.9%. Furthermore, Tripod increases the true positive rate of Var-CNN from 26.9% to 91.4% in the more realistic open-world scenario.