An Extension of the String-to-String Correction Problem
Journal of the ACM (JACM)
Inferring the source of encrypted HTTP connections
Proceedings of the 13th ACM conference on Computer and communications security
Tor: the second-generation onion router
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
ConceptDoppler: a weather tracker for internet censorship
Proceedings of the 14th ACM conference on Computer and communications security
Proceedings of the 2009 ACM workshop on Cloud computing security
How unique is your web browser?
PETS'10 Proceedings of the 10th international conference on Privacy enhancing technologies
DefenestraTor: throwing out windows in Tor
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
Website fingerprinting in onion routing based anonymization networks
Proceedings of the 10th annual ACM workshop on Privacy in the electronic society
Peek-a-Boo, I Still See You: Why Efficient Traffic Analysis Countermeasures Fail
SP '12 Proceedings of the 2012 IEEE Symposium on Security and Privacy
Touching from a distance: website fingerprinting attacks and defenses
Proceedings of the 2012 ACM conference on Computer and communications security
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In this paper, we propose new website fingerprinting techniques that achieve a higher classification accuracy on Tor than previous works. We describe our novel methodology for gathering data on Tor; this methodology is essential for accurate classifier comparison and analysis. We offer new ways to interpret the data by using the more fundamental Tor cells as a unit of data rather than TCP/IP packets. We demonstrate an experimental method to remove Tor SENDMEs, which are control cells that provide no useful data, in order to improve accuracy. We also propose a new set of metrics to describe the similarity between two traffic instances; they are derived from observations on how a site is loaded. Using our new metrics we achieve a higher success rate than previous authors. We conduct a thorough analysis and comparison between our new algorithms and the previous best algorithm. To identify the potential power of website fingerprinting on Tor, we perform open-world experiments; we achieve a recall rate over 95% and a false positive rate under 0.2% for several potentially monitored sites, which far exceeds previous reported recall rates. In the closed-world experiments, our accuracy is 91%, as compared to 86-87% from the best previous classifier on the same data.