Website fingerprinting attacks can reveal the receiver in anonymous networks and cause a potential threat to users' privacy. Previous studies focus more on identifying individual webpages. They also neglect the hyperlink transition information, because it induces extra 'noise' to classify the original webpage. However, it is a common scenario that the users surf a website by clicking hyperlinks on the webpage. In this paper, we propose a website modeling method based on profile hidden Markov model (PHMM) which is widely used in bioinformatics for DNA sequencing analysis. Our technique explicitly accounts for possible hyperlink transitions made by users when fingerprinting a target website, and therefore can work in a more realistic environment than existing methods. Using SSH and Shadowsocks, we collect various data sets and conduct extensive evaluations. We also show that our approach could work both in webpage and website identification in a closed world setting. The experimental results demonstrate that our website fingerprinting is more accurate and robust than existing methods.
|Original language||English (US)|
|Number of pages||15|
|Journal||IEEE Transactions on Information Forensics and Security|
|State||Published - May 2018|
Bibliographical noteFunding Information:
Manuscript received February 26, 2017; revised June 21, 2017 and September 22, 2017; accepted September 29, 2017. Date of publication October 13, 2017; date of current version January 29, 2018. This work was supported by the National Natural Science Foundation of China under Grant 61402080, Grant 61502086, and Grant 61572115. The work of Z. Zhuo was supported by the China Scholarship Council for his study in University of Minnesota, Twin Cities. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Tomas Pevny. (Corresponding author: Xiaosong Zhang.) Z. Zhuo, X. Zhang, and J. Zhang are with the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: firstname.lastname@example.org).
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- Anonymous network
- profile hidden Markov model
- website fingerprinting attacks