Advertisement

Prediction of Unsolved Terrorist Attacks Using Group Detection Algorithms

  • Fatih Ozgul
  • Zeki Erdem
  • Chris Bowerman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5477)

Abstract

Detection of terrorist groups using crime data has few examples currently; this is because of lack of detailed crime data which contain terrorist groups’ attacks and activities. In this study, a novel prediction model; CPM is applied to a crime dataset which includes solved and unsolved terrorist events in Istanbul, Turkey between 2003 and 2005, aiming to predict perpetuators of terrorist events which are still remained unsolved. CPM initially learns similarities of crime incident attributes from all terrorist attacks and then puts them in appropriate clusters. After solved and unsolved attacks are gathered in the same “umbrella” clusters, CPM classifies unsolved cases to previously known terrorist groups. Recall and precision results and findings of CPM regarded as successful then a baseline system; TMODS.

Keywords

Terrorist groups crime data mining matching and predicting crimes clustering classification offender networks group detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Coffman, T., Greenblatt, S., Marcus, S.: Graph-based technologies for intelligence analysis. Communication of ACM 47(3), 45–47 (2004)CrossRefGoogle Scholar
  2. 2.
    Coffman, T.R., Marcus, S.E.: Pattern Classification in Social Network Analysis: A case study. In: 2004 IEEE Aerospace Conference, March 6-13 (2004)Google Scholar
  3. 3.
    Coffman, T.R., Marcus, S.E.: Dynamic Classification of Suspicious Groups using social network analysis and HMMs. In: 2004 IEEE Aerospace Conference, March 6-13 (2004)Google Scholar
  4. 4.
    Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons, New York (2003)zbMATHGoogle Scholar
  5. 5.
    Marcus, S., Coffman, T.: Terrorist Modus Operandi Discovery System 1.0: Functionality, Examples, and Value. In: 21st Century Technologies, Austin TX (2002)Google Scholar
  6. 6.
    Marcus, S.E., Moy, M., Coffman, T.: Social Network Analysis. In: Cook, D.J., Holder, L.B. (eds.) Mining Graph Data. John Wiley & Sons, Inc., Hoboken (2007)Google Scholar
  7. 7.
    Moy, M.: Using TMODS to run best friends group detection algorithm. In: 21st Century Technologies, Austin, TX (2005)Google Scholar
  8. 8.
    Ozgul, F., Bondy, J., Aksoy, H.: Mining for offender group detection and story of a police operation. In: Sixth Australasian Data Mining Conference (AusDM 2007). Australian Computer Society Conferences in Research and Practice in Information Technology (CRPIT), Gold Coast, Australia (2007)Google Scholar
  9. 9.
    Ozgul, F., Erdem, Z., Aksoy, H.: Comparing Two Models for Terrorist Group Detection: GDM or OGDM? In: Yang, C.C., Chen, H., Chau, M., Chang, K., Lang, S.-D., Chen, P.S., Hsieh, R., Zeng, D., Wang, F.-Y., Carley, K.M., Mao, W., Zhan, J. (eds.) ISI Workshops 2008. LNCS, vol. 5075, pp. 149–160. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fatih Ozgul
    • 1
  • Zeki Erdem
    • 2
  • Chris Bowerman
    • 1
  1. 1.Department of Computing &TechnologyUniversity of SunderlandSunderlandUnited Kingdom
  2. 2.Information Technologies InstituteTUBITAK- Marmara Research CentreGebzeTurkey

Personalised recommendations