An ontological approach to classifying cybercrimes in an ICT4D context

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Abstract

While the phenomenon of cybercrime remains a challenge for governments worldwide, it is even more of a challenge for countries in an ICT4D context since they possess limited technical skills and resources to respond to, investigate and prosecute nefarious cyber activities. Despite the challenges, governments have responded by establishing legal frameworks and Computer Security Incident Response Teams. However, scholars argue that the cybercrime phenomenon is still not well understood; which is compounded by the lack of an accepted, uniform cybercrime classification scheme or ontology with which to classify cybercrimes. While few classification schemes have been published, same are limited in that they are not comprehensive; i.e., they are unable to account for the range of and ever changing types of cybercrimes and, the schemes are largely incompatible, focusing on different perspectives. This makes holistic and consistent classification improbable. To address these gaps we propose a formal cybercrime classification ontology, expressed in OWL Ontology Language. In designing our ontology we were guided by the steps of the design science research methodology. This paper contributes a formal ontology of a ‘shared conceptualization’ of cybercrimes by police practitioners and researchers. The ontology presented here is improved over prior works since it incorporates multiple perspectives and its design is better able to handle existing and future cybercrimes, a most salient feature given the dynamic nature of cybercrimes. We demonstrate the ontology by applying it to an actual cybercrime case. The designed ontology effectively classifies the cybercrime and has the potential to improve cybercrime classification in ICT4D and developed contexts.

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APA

Donalds, C., & Osei-Bryson, K. M. (2018). An ontological approach to classifying cybercrimes in an ICT4D context. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10844 LNCS, pp. 253–267). Springer Verlag. https://doi.org/10.1007/978-3-319-91800-6_17

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