GPU-Accelerated Deep Learning-Based Correlation Attack on Tor Networks

Abstract

The Tor network, renowned for its provision of online privacy and anonymity, faces the constant threat of correlation attacks that aim to compromise user identities. For almost two decades, these correlation attacks were based on statistical methods. However, in recent years, deep learning-based correlation attacks have been introduced to make them more accurate. Nevertheless, in addition to being accurate, fast correlation attacks on Tor are crucial for assessing the real-world viability of such attacks because reduced correlation time aids in estimating its practical implications. Moreover, a reduction in correlation time also helps improve efficiency and ensures practical relevance of the attack. The existing state-of-the-art implementation of a correlation attack on Tor suffers from slow performance and large memory requirements. For instance, training the model required 133 GB of memory, and correlating 10,000 flows takes about 976 seconds. In this paper, we present a novel GPU-based correlation strategy and a fast traffic flow loading technique to reduce time complexity by 7.12x compared to existing methods. Our computational approach, reliant on PyCUDA, facilitates the parallelization of operations used in the attack, thereby enabling efficient execution through the utilization of GPU architecture. Leveraging these two approaches, we introduced an improved correlation attack, which shows high accuracy and fast performance compared to state-of-the-art methods. Moreover, we address resource limitation issues by reducing memory consumption by 47.37% during the training phase, which allows the model to be trained with much fewer resources.