An Enhanced Website Fingerprinting Attacks Based on Attention Mechanism and Spatial-Temporal Features

Website fingerprinting (WF) attacks leverage deep learning to analyze encrypted traffic and identify the websites accessed by Tor users, posing a critical threat to online anonymity. However, existing detection models often suffer from low accuracy and weak robustness in open-world scenarios. To address these issues, we propose a novel spatiotemporal WF framework integrating a lightweight attention mechanism and parallel multi-size feature extraction with dilated convolution. This design significantly enhances detection performance while maintaining high efficiency. Experimental results show that our approach outperforms CNN and LSTM models by 3–5% in accuracy and surpasses the DF model by about 1%, while achieving a 50% increase in efficiency. Furthermore, it demonstrates robust performance as the number of website categories grows and remains effective against defenses such as WTF-PAD and Walkie-Talkie. Notably, our framework also mitigates the impact of concept drift, exhibiting minimal performance degradation when traffic distributions evolve over time. These findings not only underscore the unique potential of our model to advance WF attacks, but also highlight the urgent need for stronger privacy-preserving measures to protect Tor users in real-world environments.