Combined detection model for criminal network detection

Authors:
Fatih Ozgul;Zeki Erdem;Chris Bowerman;Julian Bondy
Affiliations:
Faculty of Computing, Engineering &Technology, University of Sunderland, Sunderland, United Kingdom;TUBITAK- UEKAE, Information Technologies Institute, Gebze, Kocaeli, Turkey;Faculty of Computing, Engineering &Technology, University of Sunderland, Sunderland, United Kingdom;School of Global Studies, Social Science & Planning, RMIT University, Melbourne, Australia
Venue:
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
Year:
2010

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Abstract

Detecting criminal networks from arrest data and offender demographics data made possible with our previous models such as GDM, OGDM, and SoDM and each of them proved successful on different types of criminal networks. To benefit from all features of police arrest data and offender demographics, a new combined model is developed and called as combined detection model (ComDM). ComDM uses crime location, date and modus operandi similarity as well as surname and hometown similarity to detect criminal networks in crime data. ComDM is tested on two datasets and performed better than other models.