Abstract
Website fingerprinting (WF) attack stands opposite against privacy protection in using the Internet, even when the content details are encrypted, such as Tor networks. Whilst existing difficulty in the preparation of many training samples, we study a more realistic problem — few-shot website fingerprinting attack where only a few training samples per website are available. We introduce a novel Transfer Learning Fingerprinting Attack (TLFA) that can transfer knowledge from the labeled training data of websites disjoint and independent to the target websites. Specifically, TLFA trains a stronger embedding model with the training data collected from non-target websites, which is then leveraged in a task-agnostic manner with a task-specific classifier model fine-tuned on a small set of labeled training data from target websites. We conduct expensive experiments to validate the superiority of our TLFA over the state-of-the-art methods in both closed-world and open-world attacking scenarios, at the absence and presence of strong defense.
•We study a realistic and difficult few-shot website fingerprinting attack problem.•We propose a novel Transfer Learning Fingerprinting Attack(TLFA) method.•Experiments show TLFA outperforms significantly previous state-of-the-art methods.