EnhanCorr: Stable and Enhanced Flow Correlation under Bursty Traffic

With the proliferation of anonymous networks, traffic correlation attacks have become a common means of deanonymization. However, existing methods often rely on regular traffic patterns and exhibit limited effectiveness under bursty traffic conditions typical of web interactions—a critical and unresolved challenge. To overcome this limitation, we propose EnhanCorr, an enhanced flow correlation method based on multimodal convolution, designed to improve de-anonymization performance in bursty traffic environments. Our approach introduces a hybrid input matrix and a window-based weighting mechanism to enhance correlation stability, alongside pre-matching and window-sampling strategies to accelerate training and boost inference efficiency. Experimental results demonstrate that EnhanCorr achieves superior correlation accuracy and computational efficiency in bursty traffic scenarios, confirming its practical value and applicability in real-world anonymous communication systems.