Synopsis (AI-Generated)
Crypto-Cognitive Exploitation offers a catalog-style synthesis of how fraud in cryptocurrency environments emerges from the interplay of cognitive processes, social dynamics, and technological infrastructures. The volume examines how individuals’ attention, biases, risk perceptions, and decision-making interact with social influence, reputation signals, and information asymmetries to facilitate fraudulent actions. It also considers how technological elements—cryptographic protocols, blockchain immutability, smart contracts, and wallet ecosystems—provide both opportunities for legitimate use and pathways for exploitation. By applying telematics and informatics perspectives, the work integrates data flows from network telemetry, user interfaces, and transaction traces to illuminate patterns of abuse and defense. The catalog frames fraud as a systemic phenomenon that arises at the intersection of human cognition, social networks, and engineered systems, rather than as isolated incidents. The entry organizes its coverage into analytic strands and model types that support comparative analysis across crypto ecosystems. Key elements include cognitive drivers of deception, social propagation mechanisms, and technical vectors such as vulnerabilities in smart contracts, wallet security, and data leakage. It addresses stages of fraud—from initial deception through exploitation to concealment—and identifies typologies of fraud schemes without detailing specific cases. The informatics lens emphasizes data integration, anomaly detection, and visualization of relationships among actors, assets, and events. The telematics dimension highlights the role of real-time telemetry and remote sensing of user behavior and system performance to inform risk assessment and response. The catalog-oriented presentation aims to support researchers, practitioners, and policymakers in understanding exploitation pathways and in designing user-centered safeguards, policy frameworks, and technical countermeasures.
Identified Gaps (AI-Generated)
Explicit gaps include reliance on 204 DFPI reports and Chainalysis API limitations; absence of multi-source data; limited generalizability to non-US contexts; lack of longitudinal data; need empirical validation of CCEM; need to translate findings into cross-jurisdictional prevention and policy evaluations.
Methods (AI-Generated)
An integrated theoretical framework combining Cognitive Vulnerability Theory and the Social Engineering Approach analyzes 204 DFPI cryptocurrency scam reports to map scam dimensions. The study uses advanced language models for data coding, achieving interrater reliability (kappa = 0.78) and introduces the Crypto-Cognitive Exploitation Model (CCEM). It identifies seven scam dimensions and notes prevalent fraudulent trading platform scams often co-occur with pig butchering.
Limitations (AI-Generated)
Limitations include reliance on a single US-based data source (DFPI) and Chainalysis API restrictions, potential reporting bias, and narrative variability; limited generalizability to global contexts; no longitudinal tracking; potential measurement error from automated coding; need external validation of CCEM.
Future Work (AI-Generated)
Future research should broaden data sources beyond US DFPI reports to capture global scam typologies and cross-jurisdictional responses. Validate and extend the Crypto-Cognitive Exploitation Model (CCEM) using multi-source datasets (global Chainalysis data, consumer complaint portals) and longitudinal data to track scam evolution. Empirically test the effectiveness of recommended digital strategies and regulatory countermeasures across different regulatory regimes. Investigate victim demographics and psychosocial factors to tailor prevention messaging. Develop standardized coding schemes for scam reports to improve comparability. Explore real-time detection tools and interventions in DeFi and exchange ecosystems, and assess policy impact on scam incidence and recovery outcomes.
AI-Generated Content Notice
The synopsis and research notes on this page were generated with AI from available publication information and, when available, the uploaded paper text. They may contain errors, omissions, or interpretation issues. Readers should follow the DOI or source link, review the original publication, and make their own judgment about the content.