Abstract
Information leakage by side channels in web applications is a major security threat, even when web traffic is encrypted. In this paper, we describe an automated system to analyse leakages of user privacy. Previous works focus on communications directly interacting with sensitive information. We show how, in real-world web applications, user information can be leaked through non-intuitive communications, which do not contain direct interactions with sensitive information. Unexpectedly, we found that user privacy can be leaked more from this kind of non-intuitive communication than direct interactions. This work also discloses user identities can be inferred by traffic analysis. In this work, we combine machine learning to recognize traffic pattern, i.e., Hidden Markov model is used to fingerprint web traffic of user privacy.
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