The Anatomy of Scam Scenarios: Large-Scale Characterization and Conversation-Aware Detection

Computer Science > Cryptography and Security [Submitted on 14 Jun 2026] The Anatomy of Scam Scenarios: Large-Scale Characterization and Conversation-Aware Detection Shang Ma, Chen Yanai, Avichai Ben, Zichen Liu, Yanfang Ye, Xusheng Xiao Online scams have become a pervasive global threat, causing substantial financial, psychological, and operational harm. Scammers embed psychological techniques (PTs) within reusable operational schemes to scale scam campaigns with minimal adaptation. However, existing studies often analyze PTs as isolated features, overlooking the recurring scam scenarios in which they are systematically deployed. To address this gap, we first conduct a large-scale empirical study to jointly characterize scam scenarios and their associated PTs. Specifically, we develop a data-driven pipeline to derive a hierarchical taxonomy of scam scenarios, consisting of 18 fine-grained scenarios grouped into 6 high-level tactics based on their PT profiles. Furthermore, to transfer this scenario-level knowledge to practical defense, we design a conversation-aware scam scenario detection approach for financial-institution customer interactions, enabling timely warning and intervention. Our study on 102,054 real-world scam incident reports, spanning 2024-02-01 to 2025-10-31, reveals that PT usage is significantly associated with scam scenarios. We further show that scammers organize scenarios around different operational goals, such as broad victim exposure, high victim conversion, and high-value extraction, and reuse infrastructure, including IP addresses, domains, email addresses, and phone numbers, to launch coordinated campaigns at scale. Evaluation on Subjects: Cryptography and Security (cs.CR); Computers and Society (cs.CY) Cite as: arXiv:2606.16052 [cs.CR]   (or arXiv:2606.16052v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.16052 Focus to learn more Submission history From: Shang Ma [view email] [v1] Sun, 14 Jun 2026 22:54:42 UTC (652 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)