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Understanding Social Engineering Based Scams

Unknown (2016)

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DOI: 10.1007/978-1-4939-6457-4 (opens in a new tab)

Type:   Journal Article
Country:   Global
Tags:   Craigslist, Email Scams, Honeypots, Persuasion, Romance Scams, Scam Detection, Scam Taxonomy, Social Engineering
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Synopsis

            
This publication provides a comprehensive, research-based look at social engineering–based scams and the countermeasures used to study and combat them. Its stated aim is to illuminate emergent threats and defenses by collecting and analyzing real-world scam activities, with an emphasis on understanding how scammers operate, how victims are persuaded, and where interventions may disrupt illicit activity. The work gathers insights from multiple disciplinary perspectives, including psychology, criminology, and computer security, and treats all source material as untrusted reference while focusing on evidence-supported conclusions. The book uses both descriptive and empirical approaches. It surveys scam categories and targeting strategies, traces the persuasive techniques that underlie different scams, and examines how filtering and analysis technologies attempt to separate legitimate messages from fraud. It features practical, measurement-driven chapters, such as automated honeypot campaigns on Craigslist to study sales, rental, and related scams, and it details how scammers cluster into groups, the geographic and infrastructural patterns they use, and the kinds of monetization schemes they employ. It also discusses brand abuse and storyline-based detection methods that aim to identify common narrative elements across scams, while highlighting the limitations and trade-offs of these methods. Principal findings indicate that social engineering scams are widespread and organized, with a notable concentration of activity in certain groups and regions, including a substantial share of Nigerian actors in some campaigns. The work highlights that conventional filtering often misses scam patterns that rely on plausible branding or personalized narratives, and it points to potential intervention points at the payment, regulatory, and enforcement levels. It also acknowledges limitations inherent in measurement studies, such as attribution challenges and the evolving nature of scam tactics.

          

        
                  

            
Identified Gaps

            
The book identifies a need for defenses against low-volume, targeted scams that evade conventional spam filters. It calls for automated detection of persuasion principles and reused scam text, particularly in romance scams. It also identifies gaps in attack attribution, timely intelligence collection, proactive study of attacker and victim behavior, and cross-sector collaboration.

          

        
                  

            
Methods

            
This edited research book combines several approaches: a taxonomy and SVM classification of 219,480 scam emails collected from reporting sites (2006–2014); analysis of FBI/IC3 reports; survey-based credibility measurement; content and persuasion analysis; and case studies. The romance-scam case study used Craigslist magnetic honeypot advertisements in 100 low-activity U.S. cities, manual labeling of 541 responses, and a simulated spam-filter response to collect click, IP-address, user-agent, and reply data.

          

        
                  

            
Limitations

            
The book explicitly states that it is not a comprehensive treatment of scams, email scams, countermeasures, or scam research. Its romance-scam case study is limited to responses to intentionally conspicuous men-seeking-women Craigslist honeypot advertisements in low-activity U.S. cities over several months. Findings about scam categories, origins, and tactics may therefore not generalize to all romance scams, platforms, victims, or countries.

          

        
                  

            
Future Work

            
Develop detection for low-volume targeted attacks using headers, content, and recipient reactions. Build text-reuse detection, especially for romance-scam scripts, and iteratively expand scam-text repositories. Integrate filters with honeypots and automated conversations to collect timely intelligence. Improve attack attribution and collaboration among service providers, technology organizations, academia, government, and data-holding organizations.

          

              

    
    

        
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