Applying Semantic Technologies to Fight Online Banking Fraud
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Abstract
Cybercrime tackling is a major challenge for Law Enforcement Agencies (LEAs). Traditional digital forensics and investigation procedures are not coping with the sheer amount of data to analyse, which is stored in multiple devices seized from distinct, possibly-related cases. Moreover, inefficient information representation and exchange hampers evidence recovery and relationship discovery. Aiming at a better balance between human reasoning skills and computer processing capabilities, this paper discusses how semantic technologies could make cybercrime investigation more efficient. It takes the example of online banking fraud to propose an ontology aimed at mapping criminal organisations and identifying malware developers. Although still on early stage of development, it reviews concepts to extend from well-established ontologies and proposes novel abstractions that could enhance relationship discovery. Finally, it suggests inference rules based on empirical knowledge which could better address the needs of the human analyst.
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