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Analyzing Modern Scam Typologies: From Pig-Butchering and Nigerian Advance-Fee Fraud to Crypto Airdrop Schemes

Katalin Parti ; Sinyong Choi ; Thomas Dearden (2026) — International Journal of Cybersecurity Intelligence & Cybercrime

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DOI: 10.52306/2578-3289.1248 (opens in a new tab)

Type:   Journal Article
Country:   Nigeria
Tags:   AI-Enabled Fraud, Blockchain Forensics, Cross Cultural, Cryptocurrency Scams, Cyber Fraud, Fraud Attribution, Generative AI, Hids Alert Triage, Money Mules, Pig Butchering, Threat Intelligence, Victim Experience
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Synopsis

            
This publication provides a neutral synthesis of four research papers in the 2026 issue of the International Journal of Cybersecurity Intelligence and Cybercrime, focusing on contemporary cyber scam typologies and defense considerations. The authors frame modern cybercrime as shaped by decentralized technologies, organizational routines, and automated threat intelligence, and they advocate for interdisciplinary approaches that integrate technical forensics, qualitative analysis, and machine-learning aided triage. The first study examines a sanctions-linked pig-butchering network using on-chain tracing to reveal a three-tier transaction architecture with hub, intermediary, and holding layers. It emphasizes that the hub initiates centralized intake and that downstream funds flow primarily through internal transfers, suggesting forensic indicators for tracing illicit funds. The second study analyzes how the label “Nigerian scam” persists in discourse, employing qualitative analysis across academic, media, institutional, and enforcement texts to argue that attribution is shaped by representational repetition and institutional utility rather than strict authorship, with AI tools further destabilizing traditional cues. The third study analyzes 112 validated crypto airdrop scam cases to model how transaction-level mechanisms and user authorization influence losses, finding that authorization moments and routing via decentralized exchanges strongly affect monetary harm. The fourth study evaluates an automated threat intelligence pipeline that maps host intrusion system alerts to MITRE ATT&CK techniques, showing that domain adaptation and a metadata-aware re-ranking improve triage efficiency and cross-case comparability. Together, the issue positions a holistic defense framework that couples blockchain forensics, discourse-informed attribution, and automated threat enrichment to mitigate modern cyber threats.

          

        
                  

            
Identified Gaps

            
The editorial indicates a need for integrated analysis across technical forensics, fraud discourse, and automated detection because fragmented interventions are insufficient. It provides no direct evidence on victim experiences, relationship-grooming processes, reporting, recovery, or the comparative effectiveness of specific interventions in pig-butchering cases.

          

        
                  

            
Methods

            
This editorial synthesizes four studies in a journal issue. The summarized methods include multi-platform blockchain tracing, exposure screening, thematic flow analysis, qualitative discourse analysis, forensic tracing with Gamma generalized linear modeling of 112 airdrop cases, and transformer-based evaluation of HIDS-alert enrichment across controlled alerts and Windows event logs.

          

        
                  

            
Limitations

            
This publication is an editorial overview rather than a report of original, integrated empirical research. Its claims summarize four separate studies, with limited methodological detail, no full underlying datasets, and no direct victim-level evidence. Findings about pig-butchering are confined to sanctioned Bitcoin addresses and financial-network architecture.

          

        
                  

            
Future Work

            
Develop and evaluate interdisciplinary safeguards that combine blockchain forensics, qualitative analysis of fraud attribution, and automated threat triage. Research should assess whether coordinated technical, institutional, and policy responses improve proactive detection and disruption of evolving cyber scams.

          

              

    
    

        
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