A Zero-Latency Website Identification for QUIC Traffic Based on Feature Alignment

With the deployment of the QUIC protocol, website fingerprinting attacks targeting QUIC traffic are becoming a growing concern. Since the deployment is incremental, attackers must continuously crawl the QUIC traffic of new QUIC-enabled websites to update their attack models. For the latency caused by data crawling and classifier training, existing few-shot website fingerprinting (FSWF) attacks rely on representation learning to mitigate data dependency. To further achieve zero-latency identification, TCP traffic can be applied to construct the attack model before QUIC deployment. However, the different protocol semantics of TCP and QUIC lead to differences in the latent features. As representation learning models cannot eliminate the website feature differences, classifiers trained on TCP-based features are difficult to adapt to QUIC traffic. To address the issue, we propose a novel cross-protocol FSWF attack method to fuse cross-protocol website features. The proposed method forces TCP features and QUIC features to be in the same feature space by sharing model parameters, and reduces cross-protocol website feature differences through inter-protocol adversarial representation learning. Meanwhile, it utilizes a non-linear classifier to fit the fused features. The proposed method enables zero-latency identification for QUIC traffic based on a few TCP traffic. We conducted comprehensive evaluation experiments on public datasets from both closed-world and open-world settings. The proposed method outperforms state-of-the-art methods in zero-latency identification.