ESPRESSO: Advanced End-to-End Flow Correlation Attacks on Tor

Anonymous communication networks such as Tor are vulnerable to end-to-end flow correlation, which allows an eavesdropper to link a client with their destination by identifying pairs for network flows that belong to the same circuit. However, existing approaches, while having high accuracy, suffer from a high false positive rate and computational complexity as well as low sensitivity. In this paper, we propose ESPRESSO, a new method designed for Tor traffic correlation attacks that build upon the state-of-the-art DeepCoFFEA. We utilize an aggregated feature representation and we employ Transformers for global processing to capture long-range dependencies. Furthermore, we use an improved window-based amplification strategy to improve performance further. Our preliminary results show significant performance gains over the prior state-of-the-art DeepCoFFEA.