Document Type
Article
Publication Date
4-29-2019
Publication Title
EAI Transactions on Endorsed Transactions on Security and Safety
Volume
19
Issue
20
Publisher Name
European Alliance for Innovation
Abstract
Website fingerprinting attacks have been shown to be able to predict the website visited even if the network connection is encrypted and anonymized. These attacks have achieved accuracies as high as 92%. Mitigations to these attacks are using cover/decoy network traffic to add noise, padding to ensure all the network packets are the same size, and introducing network delays to confuse an adversary. Although these mitigations have been shown to be effective, reducing the accuracy to 10%, the overhead is high. The latency overhead is above 100% and the bandwidth overhead is at least 30%. We introduce a new realistic cover traffic algorithm, based on a user’s previous network traffic, to mitigate website fingerprinting attacks. In simulations, our algorithm reduces the accuracy of attacks to 14% with zero latency overhead and about 20% bandwidth overhead. In real-world experiments, our algorithms reduces the accuracy of attacks to 16% with only 20% bandwidth overhead.
Recommended Citation
Cui, Weiqi; Yu, Jiangmin; Gong, Yanmin; and Chan-Tin, David. Efficient, Effective, and Realistic Website Fingerprinting Mitigation. EAI Transactions on Endorsed Transactions on Security and Safety, 19, 20: , 2019. Retrieved from Loyola eCommons, Computer Science: Faculty Publications and Other Works, http://dx.doi.org/10.4108/eai.29-1-2019.161977
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Copyright Statement
© 2019 Weiqi Cui et al., licensed to EAI.
Comments
Author Posting © 2019 Weiqi Cui et al., licensed to EAI. This article is posted here by permission of Weiqi Cui et al., licensed to EAI. for personal use, not for redistribution. The article was published in EAI Transactions on Endorsed Transactions on Security and Safety, Volume 19, Issue 20, April 2019, http://dx.doi.org/10.4108/eai.29-1-2019.161977