Realistically Fingerprinting Social Media Webpages in HTTPS Traffic

Abstract

In webpage fingerprinting (WPF), an adversary attempts to identify webpages in encrypted network traffic. Identifying social media webpages however is a challenging task, due to the similarity and dynamic nature of such pages. Existing webpage fingerprinting attacks often have unrealistic assumptions regarding the capability of government agencies or knowledge of the criminal’s environment, which renders these attacks ineffective when applied to social media platforms. In this paper, we unravel the current concerns in state of the art WPF attacks in a social network context for forensic analysis. To resolve the issues presented, we propose an enhanced version of the WPF attack ‘IUPTIS’ and introduce an intelligent observer that significantly improves upon previous works. Furthermore, our improvements are compared to related WPF attacks by conducting extensive experiments on two social platforms: Twitter and Instagram. Our examination shows that the improved IUPTIS attack defeats previous works in terms of realistic obstacles such as HTTP/2, caching and performance costs, thus making it feasible to identify social media webpages with minimal resources.