Poster

匿名 计算机科学 计算机安全 可扩展性 混淆 互联网 对手 假阳性悖论 流量分析 管道(软件) 互联网隐私 计算机网络 万维网 人工智能 操作系统
作者
Daniela Lopes,Pedro Medeiros,Jin-Dong Dong,Diogo Barradas,Bernardo Portela,João Vinagre,Bernardo Ferreira,Nicolas Christin,Nuno Santos
标识
DOI:10.1145/3548606.3563520
摘要

Tor is the most popular anonymity network in the world. It relies on advanced security and obfuscation techniques to ensure the privacy of its users and free access to the Internet. However, the investigation of traffic correlation attacks against Tor Onion Services (OSes) has been relatively overlooked in the literature. In particular, determining whether it is possible to emulate a global passive adversary capable of deanonymizing the IP addresses of both the Tor OSes and of the clients accessing them has remained, so far, an open question. In this paper, we present ongoing work toward addressing this question and reveal some preliminary results on a scalable traffic correlation attack that can potentially be used to deanonymize Tor OS sessions. Our attack is based on a distributed architecture involving a group of colluding ISPs from across the world. After collecting Tor traffic samples at multiple vantage points, ISPs can run them through a pipeline where several stages of traffic classifiers employ complementary techniques that result in the deanonymization of OS sessions with high confidence (i.e., low false positives). We have responsibly disclosed our early results with the Tor Project team and are currently working not only on improving the effectiveness of our attack but also on developing countermeasures to preserve Tor users' privacy.
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