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Avichai Ben

Publications and source records attributed to Avichai Ben.

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

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

cs.CR

The Human Vulnerabilities & Exploits (HVE) Framework

The cybersecurity community has invested over two decades in building standardized frameworks, the Common Vulnerabilities and Exposures (CVE) system, the Common Vulnerability Scoring System (CVSS), and the Common Weakness Enumeration (CWE) to identify, classify, and remediate threats to digital infrastructure. However, an emerging body of research reveals that a vast majority of successful cyberattacks exploit not software flaws, but human behavioral and psychological vulnerabilities. Social engineering, fraud, and scam attacks, which manipulate human cognition, emotion, and trust, do not have an equivalent standardized framework. Meanwhile, behavioral science and psychology research has established robust theoretical foundations, such as dual-process theory, prospect theory, social influence frameworks, and visceral state models, which explain precisely why and how these attacks succeed. This paper introduces the Human Vulnerabilities & Exploits (HVE) Framework, a structured approach for identifying, classifying, and mitigating the behavioral and psychological vulnerabilities exploited in scams, social engineering, and other human-centric fraud and attacks, analogous in concept to how CVE helps classify software vulnerabilities: it provides a shared, machine-readable taxonomy with structured identifiers, multi-dimensional severity scoring via the Human Vulnerability Severity Score (HVSS), and actionable remediation guidance through Human Vulnerability Patches (HVPs). This introduction synthesizes the relevant literature across cybersecurity standardization, behavioral science, and fraud defense to establish the theoretical and practical foundations for the HVE framework, whose architecture and technical specifications are detailed in subsequent sections.

cs.CR