An Adaptability-Enhanced Few-Shot Website Fingerprinting Attack Based on Collusion

Few-shot website fingerprinting (FSWF) attacks attempt to identify whether the users have access to specific websites based on a few training data. Existing FSWF attack methods focus on adapting to variable network conditions in real scenarios. They use various techniques to transfer the model to adapt to test data which has a different distribution from training data. However, recent methods ignore the impact of pre-training data diversity on adaptability. The poor data diversity caused by the user-specific data crawl limits representation ability, and further hinders rapid adaptation to new network conditions. Due to the extreme Non-IId between multiple attackers' datasets, it is not feasible to mix multiple datasets or perform traditional federated learning methods to improve representation ability. To address the issue, we propose a novel method based on a joint learning framework to achieve the collusion FSWF attacks. The proposed method fuses the feature spaces of multiple user-side attackers to enhance the representation ability of the local model, and constructs a virtual fusion center to mitigate the impact of Non-IID. It improves the adaptability under variable network conditions for the local attacker. This paper conducts comprehensive experiments to evaluate the performance of the proposed method in both closed-world and open-world settings. Compared with the state-of-the-art method, the proposed method improves the accuracy by up to 13.02% in the closed-world setting and the AUC by up to 0.085 in the open-world setting, respectively.