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Combined Detection Model for Criminal Network Detection

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Intelligence and Security Informatics (PAISI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 6122))

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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.

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References

  1. Brandes, U.: A Faster Algorithm for betweenness centrality. Journal of Mathematical Sociology 25(2), 163–177 (2001)

    MATH  Google Scholar 

  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. Hunter, A.: Leninist Cell Data Analysis. 21st Century Technologies Inc., Austin (2002)

    Google Scholar 

  4. Smith, M.N., King, P.J.H.: Incrementally Visualising Criminal Networks. In: Sixth International Conference on Information Visualisation (IV’02), IEEE, Los Alamitos (2002)

    Google Scholar 

  5. Wang, G.A., Xu, J.J., Chen, H.: Using Social Contextual Information to Match Criminal Identities. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences, vol. 04, pp. 81.2. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  6. 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 

  7. 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)

    Chapter  Google Scholar 

  8. Mcglorin, J.M., Sullivan, C.J., Piquero, A.R., Bacon, S.: Investigating the stability of co-offenders among a sample of youthful offenders. Criminology 46(1), 155–187 (2008)

    Article  Google Scholar 

  9. Canter, D.: A partial order scalogram analysis of criminal network structures. Behaviormetrika 31(2), 131–152 (2004)

    Article  MathSciNet  Google Scholar 

  10. Golbeck, J.: Trust and nuanced profile similarity in online social networks. ACM Trans. Web 3, 4, Article 12, 33 pages(2009)

    Google Scholar 

  11. Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback Effects between Similarity and Social Influence in Online Communities. In: KDD’08, Las Vegas, Nevada, USA, August 24-27. ACM, New York (2008)

    Google Scholar 

  12. Chau, M., Xu, J., Chen, H.: Extracting meaningful entities from police narrative reports. In: National Conference on Digital Government Research (2001)

    Google Scholar 

  13. Xu, J., Chen, H.C.: Fighting Organised Crimes: using shortest-path algorithms to identify associations in criminal networks. Decision Support Systems 38(3), 473–487 (2003)

    Article  Google Scholar 

  14. Xu, J., Chen, H.C.: CrimeNet Explorer: A Framework for Criminal Network Knowledge Discovery. ACM Transactions on Information Systems 23(2), 201–226 (2005)

    Article  Google Scholar 

  15. Oatley, G.C., Zeleznikov, J., Ewart, B.W.: Matching and predicting crimes. In: AI 2004-The 24th SGAI International Conference on Knowledge Based Systems and Applications of Artificial Intelligence (2004)

    Google Scholar 

  16. Goldberg, H.G., Wong, R.W.H.: Restructuring transactional data for link analysis in FinCEN AI System. In: AAAI Fall Symposium (1998)

    Google Scholar 

  17. Skillicorn, D.B.: Clusters within clusters: SVD and counterterrorism. In: Workshop on Data Mining for Counterterrorism and Security (2003)

    Google Scholar 

  18. Adderley, R., Badii, A., Wu, C.: The automatic identification and prioritization of criminal networks from police crime data. In: Ortiz-Arroyo, D., Larsen, H.L., Zeng, D.D., Hicks, D., Wagner, G. (eds.) EuroIsI 2008. LNCS, vol. 5376, pp. 5–14. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  19. Adderley, R., Mushgrove, P.B.: Data mining case study: Modeling the behavior of offenders who commit sexual assaults. In: ACM SIGKDD 2001 International Conference on Knowledge Discovery and Data Mining, New York, pp. 215–220 (2001)

    Google Scholar 

  20. Coffman, T., Greenblatt, S., Marcus, S.: Graph-based technologies for intelligence analysis. Communication of ACM 47(3), 45–47 (2004)

    Article  Google Scholar 

  21. Marcus, S., Coffman, T.: Terrorist Modus Operandi Discovery System 1.0: Functionality, Examples, and Value. 21st Century Technologies, Austin (2002)

    Google Scholar 

  22. 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 

  23. Moy, M.: Using TMODS to run best friends group detection algorithm. 21st Century Technologies, Austin (2005)

    Google Scholar 

  24. 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 

  25. Greenblatt, S., Coffman, T., Marcus, S.: Emerging Information Technologies and enabling policies for counter terrorism. In: Behaivoural Network Analysis for Terrorist Detection. Wiley-IEEE Press, Hoboken (2005)

    Google Scholar 

  26. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. (2001)

    Google Scholar 

  27. Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons, New York (2003)

    MATH  Google Scholar 

  28. Kaza, S., Hu, D., Atabakhsh, H., Chen, H.: Predicting criminal relationships using multivariate survival analysis. In: Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains, pp. 290–291. Digital Government Society of North America, Philadelphia (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Computing, Engineering &Technology, University of Sunderland, SR6 0DD, Sunderland, United Kingdom

    Fatih Ozgul & Chris Bowerman

  2. TUBITAK- UEKAE, Information Technologies Institute, 41470, Gebze, Kocaeli, Turkey

    Zeki Erdem

  3. School of Global Studies, Social Science & Planning, RMIT University, Melbourne, Australia

    Julian Bondy

Authors
  1. Fatih Ozgul
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  2. Zeki Erdem
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  3. Chris Bowerman
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  4. Julian Bondy
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Editor information

Editors and Affiliations

  1. The University of Arizona, Tucson, AZ, USA

    Hsinchun Chen

  2. The University of Hong Kong, Hong Kong, China

    Michael Chau

  3. National Taiwan University, Taipei, Taiwan, R.O.C.

    Shu-hsing Li

  4. International School of Information Management, University of Mysore, Mysore, India

    Shalini Urs

  5. International Institute of Information Technology, Bangalore, India

    Srinath Srinivasa

  6. Virginia Tech, Blacksburg, VA, USA

    G. Alan Wang

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© 2010 Springer-Verlag Berlin Heidelberg

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Ozgul, F., Erdem, Z., Bowerman, C., Bondy, J. (2010). Combined Detection Model for Criminal Network Detection . In: Chen, H., Chau, M., Li, Sh., Urs, S., Srinivasa, S., Wang, G.A. (eds) Intelligence and Security Informatics. PAISI 2010. Lecture Notes in Computer Science, vol 6122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13601-6_1

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  • DOI: https://doi.org/10.1007/978-3-642-13601-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13600-9

  • Online ISBN: 978-3-642-13601-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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