Kwon, Doy;
Borrion, Herve;
Wortley, Richard;
(2024)
Measuring Cybercrime in Calls for Police Service.
Asian Journal of Criminology
, 19
(3)
pp. 329-351.
10.1007/s11417-024-09432-2.
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Abstract
Conventional police databases contain much information on cybercrime, but extracting it remains a practical challenge. This is because these databases rarely contain labels that could be used to automatically retrieve all cybercrime incidents. In this article, we present a supervised machine learning method for extracting cybercrime incidents in calls for police service datasets. Data from the Korean National Police (2020, 9 months, N = 15 million call logs) is used for the demonstration. We combined methods of keyword query selection, minority oversampling, and majority voting techniques to develop a classifier. Three classification techniques, including Naïve Bayes, linear SVM, and kernel SVM, were tested, and the kernel model was chosen to build the final model (accuracy, 93.4%; F1-score, 92.4). We estimate that cybercrime only represents 4.6% of the cases in the selected dataset (excluding traffic-related incidents), but that it can be prevalent with some crime types. We found, for example, that about three quarters (76%) of all fraud incidents have a cyber dimension. We conclude that the cybercrime classification method proposed in this study can support further research on cybercrime and that it offers considerable advantages over manual or keyword-based approaches.
| Type: | Article |
|---|---|
| Title: | Measuring Cybercrime in Calls for Police Service |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s11417-024-09432-2 |
| Publisher version: | https://doi.org/10.1007/s11417-024-09432-2 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Social Sciences, Criminology & Penology, Calls for service, Cyber-enabled crime, Cybercrime, Machine learning, Emergency call, Text classification |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10210411 |
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