Comparing Two Models for Terrorist Group Detection: GDM or OGDM?

Authors:
Fatih Ozgul;Zeki Erdem;Hakan Aksoy
Affiliations:
School of Computing & Technology, University of Sunderland, Sunderland, United Kingdom SR6 0DD;TUBITAK- MAM Research Centre, Information Technologies Institute, , Gebze, Turkey 41470;Bursa Police Department, Information Processing Unit, Bursa, Turkey 16050
Venue:
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Year:
2008

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Abstract

Since discovery of organization structure of offender groups leads the investigation to terrorist cells or organized crime groups, detecting covert networks from crime data are important to crime investigation. Two models, GDM and OGDM, which are based on another representation model - OGRM are developed and tested on eighty seven known offender groups where nine of them were terrorist cells. GDM, which is basically depending on police arrest data and "caught together" information, performed well on terrorist groups, whereas OGDM, which uses a feature matching on year-wise offender components from arrest and demographics data, performed better on non-terrorist groups. OGDM uses a terror crime modus operandi ontology which enabled matching of similar crimes.