Few-shot Website Fingerprinting attack with Meta-Bias Learning

Highlights：

• We investigate the under-studied, more realistic, and more challenging few-shot website fingerprinting attackproblem. Crucially, we focus on the model learning scalability for knowledge transfer efficacy, and model optimization strategy for task adaptation capability.

• We propose a novel Meta-Bias Learning (MBL) method for solving few-shot WF attack. Specifically, we introduce a notion of parameter factorization, which avoids the need of meta-training the whole model. With this design, a majority fraction of parameters can be allocated to learn generic re-usable feature representations useful for all different tasks, whilst the remaining are used for more effective task adaptation.

• Extensive experiments show that our MBL outperforms significantly previous state-of-the-art methods in both closed-world and open-world few-shot WF attack scenarios, with and without defense.