Website fingerprinting using traffic analysis of dynamic webpages

This paper presents mechanisms for identification of web traffic masqueraded behind encrypted Virtual Private Network (VPN) tunnels. Website identification using Traffic Analysis (TA) has many administrative applications including preventing access to forbidden websites and site-specific Quality of Service (QoS) provisioning. Previous works in this area mainly looked at the problem of identifying traffic from relatively static websites, thus limiting the applicability of the technique for websites with dynamically changing contents. In this work, we attempt to generalize the mechanism for dynamic sites by the way of introducing a new classification feature traffic surge period, and adapting the first n Components of Haar Wavelet Transformation, which is commonly used in traditional signal processing applications. Our results from fingerprinting experiments carried out over an SSL VPN shows that the addition of these new features can indeed bridge the fingerprinting performance gap between static and dynamic websites.