- Title
- The seven scam types: Mapping the terrain of cybercrime
- Creator
- Stabek, Amber; Watters, Paul; Layton, Robert
- Date
- 2010
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/34734
- Identifier
- vital:3844
- Abstract
- Threat of cybercrime is a growing danger to the economy. Industries and businesses are targeted by cyber-criminals along with members of the general public. Since cybercrime is often a symptom of more complex criminological regimes such as laundering, trafficking and terrorism, the true damage caused to society is unknown. Dissimilarities in reporting procedures and non-uniform cybercrime classifications lead international reporting bodies to produce incompatible results which cause difficulties in making valid comparisons. A cybercrime classification framework has been identified as necessary for the development of an inter-jurisdictional, transnational, and global approach to identify, intercept, and prosecute cyber-criminals. Outlined in this paper is a cybercrime classification framework which has been applied to the incidence of scams. Content analysis was performed on over 250 scam descriptions stemming from in excess of 35 scamming categories and over 80 static features derived. Using hierarchical cluster and discriminant function analysis, the sample was reduced from over 35 ambiguous categories into 7 scam types and the top four scamming functions - identified as scamming business processes, revealed. The results of this research bear significant ramifications to the current state of scam and cybercrime classification, research and analysis, as well as offer significant insight into the business processes and applications adopted by scammers and cyber-criminals. © 2010 IEEE.
- Publisher
- Ballarat, VIC
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Agglomerative; Business process; Classification; Cybercrime; Cyberscam; Divisive; Framework; Static feature; Business Process; Static features; Crime; Discriminant analysis; Hierarchical systems; Computer crime
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