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In recent years, there have been several works that use websitefingerprinting techniques to enable a local adversary to determine whichwebsite a Tor user visits. While the current state-of-the-art attack, whichuses deep learning, outperforms prior art with medium to large amounts of data,it attains marginal to no accuracy improvements when both use small amounts oftraining data. In this work, we propose Var-CNN, a website fingerprintingattack that leverages deep learning techniques along with novel insightsspecific to packet sequence classification. In open-world settings with largeamounts of data, Var-CNN attains over $1\%$ higher true positive rate (TPR)than state-of-the-art attacks while achieving $4\times$ lower false positiverate (FPR). Var-CNN's improvements are especially notable in low-datascenarios, where it reduces the FPR of prior art by $3.12\%$ while increasingthe TPR by $13\%$. Overall, insights used to develop Var-CNN can be applied tofuture deep learning based attacks, and substantially reduce the amount oftraining data needed to perform a successful website fingerprinting attack.This shortens the time needed for data collection and lowers the likelihood ofhaving data staleness issues.

Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning