Inter-Flow Spatio-Temporal Correlation Analysis Based Website Fingerprinting Using Graph Neural Network

Website fingerprinting has emerged as a prominent topic in the area of network management. However, the proliferation of encrypted network traffic poses new challenges for website fingerprinting. In this paper, we analyze the behavior and correlations among the network flows generated by browsing a webpage and conclude that there exist specific spatio-temporal correlations among these network flows. Based on this finding, we propose the construction of an inter-flow spatio-temporal correlation graph (STCG) to model these correlations. In the STCG, each node represents a flow, with its features capturing the properties of the flow itself, and each edge with a weight vector represents the spatio-temporal correlation between two flows. Subsequently, we propose a graph neural network-based website fingerprinting method (STC-WF) by considering the inter-flow spatio-temporal correlations, in which the Graph Attention Network (GAT) and Self-Attention Graph Pooling (SAGPool) mechanisms are employed to acquire a comprehensive representation of the STCG. To evaluate the performance of STC-WF, we construct a real-world traffic dataset and conduct comprehensive evaluations. The experimental results demonstrate that STC-WF outperforms state-of-the-art methods in terms of accuracy and time consumption.