GL‐WF: A lightweight graph learning model for website fingerprinting attacks

Abstract Website fingerprinting attacks that analyze clients' browsing preferences are an important information source for maintaining cybersecurity and improving big data utilization in terms of service quality. Many scholars have studied graph learning because of its powerful learning and inferential capabilities on unstructured network traffic. However, the extensive computing resource requirements of graph neural networks limit their application to large‐scale and automated scenarios. In this paper, we propose a lightweight graph learning method for website fingerprinting attacks called GL‐WF. We designed a four‐level heterogeneous spatiotemporal graph using a multimodal representation of website browsing traffic. We then utilized a graph sampling algorithm to refine the structure of the graph. Finally, a well‐designed graph isomorphism network was used to extract the graph topology for the traffic classifiers. The proposed GL‐WF model, which was evaluated on over 3.6 million website flows, was found to provide high accuracy and efficiency.