Identifying Incoherent Search Sessions: Search Click Fraud Remediation Under Real-World Constraints - Zhang, Runze; Sridhar, Ranjita Pai; Yao, Mingxuan; Yang, Zheng; Oygenblik, David; Xu, Haichuan; Dave, Vacha; Herley, Cormac; England, Paul; Saltaformaggio, Brendan (2025 IEEE Symposium on Security and Privacy (SP), 2025)
Search engines and advertisers continuously suffer substantial financial losses from click fraud, which poses challenges to existing detection algorithms. Even more concerning, despite ongoing advancements, our understanding of click fraud remains limited, leaving room for sophisticated fraudulent techniques to bypass existing detection measures. In this study, we pivot from examining individual search requests to analyzing search sessions, defined as sequences of consecutive search queries made by the same user. We found that benign users exhibit coherent behavior patterns within these sessions, which contrast clearly with those of fraudulent actors. Specifically, legitimate users tend to conduct searches focused on a single topic at a time. In contrast, fraudsters or automated bots often exhibit diverse, illogical, and incoherent search behaviors within a session. To address this behavioral distinction, we propose CoSeC, a system designed to quantify the “incoherence index” of search sessions. CoSeC integrates literal semantic, temporal, and ad-click behavioral features to evaluate sessions' coherence quantitatively. Our evaluation of CoSeC demonstrates high efficacy, achieving a precision of 95.79% and a recall of 92.40% in identifying incoherent sessions, highlighting CoSeC's substantial potential to enhance real-world click fraud detection.