ClickMiner: Towards Forensic Reconstruction of User-Browser Interactions from Network Traces

Recent advances in network traffic capturing techniques have made it feasible to record full traffic traces, often for extended periods of time. Among the applications enabled by full traffic captures, being able to automatically reconstruct user-browser interactions from archived web traffic traces would be helpful in a number of scenarios, such as aiding the forensic analysis of network security incidents. Unfortunately, the modern web is becoming increasingly complex, serving highly dynamic pages that make heavy use of scripting languages, a variety of browser plugins, and asynchronous content requests. Consequently, the semantic gap between user-browser interactions and the network traces has grown significantly, making it challenging to analyze the web traffic produced by even a single user. In this paper, we propose ClickMiner, a novel system that aims to automatically reconstruct user-browser interactions from network traces. Through a user study involving 21 participants, we collected real user browsing traces to evaluate our approach. We show that, on average, ClickMiner can correctly reconstruct between 82% and 90% of user-browser interactions with false positives between 0.74% and 1.16%, and that it outperforms reconstruction algorithms based solely on referrer-based approaches. We also present a number of case studies that aim to demonstrate how ClickMiner can aid the forensic analysis of malware downloads triggered by social engineering attacks.