Logo image
Back
Measuring and Mitigating AS-level Adversaries Against Tor
Conference proceeding   Open access

Measuring and Mitigating AS-level Adversaries Against Tor

Rishab Nithyanand, Oleksii Starov, Adva Zair, Phillipa Gill and Michael Schapira
23RD ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2016)
01/01/2016
DOI: 10.14722/ndss.2016.23322
url
https://doi.org/10.14722/ndss.2016.23322View
Published (Version of record) Open Access

Abstract

The popularity of Tor as an anonymity system has made it a popular target for a variety of attacks. We focus on traffic correlation attacks, which are no longer solely in the realm of academic research with recent revelations about the NSA and GCHQ actively working to implement them in practice. Our first contribution is an empirical study that allows us to gain a high fidelity snapshot of the threat of traffic correlation attacks in the wild. We find that up to 40% of all circuits created by Tor are vulnerable to attacks by traffic correlation from Autonomous System (AS)-level adversaries, 42% from colluding AS-level adversaries, and 85% from state-level adversaries. In addition, we find that in some regions (notably, China and Iran) there exist many cases where over 95% of all possible circuits are vulnerable to correlation attacks, emphasizing the need for AS-aware relay-selection. To mitigate the threat of such attacks, we build Astoria-an AS-aware Tor client. Astoria leverages recent developments in network measurement to perform path-prediction and intelligent relay selection. Astoria reduces the number of vulnerable circuits to 2% against AS-level adversaries, under 5% against colluding AS-level adversaries, and 25% against state-level adversaries. In addition, Astoria load balances across the Tor network so as to not overload any set of relays.
 Computer Science Technology Computer Science, Information Systems Computer Science, Theory & Methods Science & Technology

Details

Metrics

46 Record Views
24 Times Cited - Web of Science
Logo image