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.

Identifying censorship circumvention network traffic has become an important task for preventing abuse of those tools. However, traditional flow-based methods have drawbacks in high false positive rate, and they fail to exploit useful hidden features. In this paper, we propose a novel feature extraction method for censorship circumvention activity identification, which extracts features from multi-granularity, and it uses a heuristic-combining approach to make the final decision. Moreover, unlike traditional approaches, which classify on an individual flow or a packet, the proposed method examines on a new granularity. We present an implementation based on the proposed method, and the results are presented to demonstrate the effectiveness of our method. In comparison to the traditional flow-based methods, the proposed strategy has a slightly lower overall accuracy rate than flow-based approaches; however, its average false positive rate is significantly lower than the traditional method. Copyright © 2016 John Wiley & Sons, Ltd.