Abstract
Recent website fingerprinting attacks have been shown to achieve very high performance against traffic through Tor. These attacks allow an adversary to deduce the website a Tor user has visited by simply eavesdropping on the encrypted communication. This has consequently motivated the development of many defense strategies that obfuscate traffic through the addition of dummy packets and/or delays. The efficacy and practicality of many of these recent proposals have yet to be scrutinized in detail. In this study, we re-evaluate nine recent defense proposals that claim to provide adequate security with low-overheads using the latest Deep Learning-based attacks. Furthermore, we assess the feasibility of implementing these defenses within the current confines of Tor. To this end, we additionally provide the first on-network implementation of the DynaFlow defense to better assess its real-world utility.
Original language | English (US) |
---|---|
Title of host publication | Proceedings - 44th IEEE Symposium on Security and Privacy, SP 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 969-986 |
Number of pages | 18 |
ISBN (Electronic) | 9781665493369 |
DOIs | |
State | Published - 2023 |
Event | 44th IEEE Symposium on Security and Privacy, SP 2023 - Hybrid, San Francisco, United States Duration: May 22 2023 → May 25 2023 |
Publication series
Name | Proceedings - IEEE Symposium on Security and Privacy |
---|---|
Volume | 2023-May |
ISSN (Print) | 1081-6011 |
Conference
Conference | 44th IEEE Symposium on Security and Privacy, SP 2023 |
---|---|
Country/Territory | United States |
City | Hybrid, San Francisco |
Period | 5/22/23 → 5/25/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- anonymous system
- deep learning
- defense
- privacy
- website fingerprinting