DeepCoFFEA: Improved Flow Correlation Attacks on Tor via Metric Learning and Amplification
Author:
Affiliation:
1. Ewha Womans University
2. University of Minnesota
3. Rochester Institute of Technology
Funder
National Science Foundation
Ewha Womans University
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9833550/9833558/09833801.pdf?arnumber=9833801
Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. ESPRESSO: Advanced End-to-End Flow Correlation Attacks on Tor;Proceedings of the 8th Asia-Pacific Workshop on Networking;2024-08-03
2. A Lightweight and Easily Scalable Tor Network Simulation Method Based on K8s;Communications in Computer and Information Science;2024
3. A Systematic Survey on Security in Anonymity Networks: Vulnerabilities, Attacks, Defenses, and Formalization;IEEE Communications Surveys & Tutorials;2024
4. Enhancing the Unlinkability of Circuit-Based Anonymous Communications with k-Funnels;Proceedings of the ACM on Networking;2023-11-27
5. TorKameleon: Improving Tor’s Censorship Resistance with K-anonymization and Media-based Covert Channels;2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom);2023-11-01
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