Tor is currently the most widely used anonymity network, providing anonymity to both users and hidden services. However, a wide range of attacks proposed in the literature claim the capability of de-anonymizing its users. Among the others, Flow Correlation Attacks aiming at discovering the activity of a specific target user present impressive results in the literature. Specifically, recent attacks such as DeepCoFFEA (S&P'22), and SUMo (NDSS'24) show more then 90% precision in correlating users and their traffic. However, when investigating Tor's past efforts in mitigating this kind of attack, we formulated the following research question: how realistic and effective are these attacks on state-of-the-art Tor networks?
In this paper, we answer this question by evaluating state-of-the-art attacks and mitigations. We create a novel dataset of Tor traffic in five different scenarios, leveraging both the latest available Tor implementation and state-of-the-art defenses. We also proposed and evaluated a novel countermeasure. We then used our collected data to test two recent and prominent flow correlation attacks, i.e., SUMo (NDSS'24) and ESPRESSO (2024), a refinement of DeepCoFFEA (S&P'22). Our experimental results demonstrate that both ESPRESSO and SUMo are ineffective at correlating Tor traffic, achieving 8% accuracy and 12% accuracy, respectively. By investigating the motivations behind these results, we discuss how mitigation developed by Tor substantially reduced the risks associated with such attacks, thereby strengthening user anonymity.