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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Analyst Notebook, i2 Analyst Notebook i2 Ltd. (2007), http://www.i2.co.uk/ (accessed July 31, 2007)
Chen, H., Atabakhsh, H., et al.: Visualisation in Law Enforcement. In: CHI 2005, Portland, Oregon, USA, April 2-7, ACM, New York (2005)
Chen, H., Xu, J.J.: Fighting Organised Crimes: using shortest-path algorithms to identify associations in criminal networks. Decision Support Systems 38(3), 473–487 (2003)
Chen, H., Chung, W., et al.: Crime data mining: a general framework and some examples. Computer 37(4), 50–56 (2004)
Chen, H., Schroeder, J., et al.: COPLINK Connect: information and knowledge management for law Enforcement. Decision Support Systems 34, 271–285 (2002)
Cook, D.J., Holder, L.B.: Graph-Based data mining. IEEE Intelligent Systems 15(2), 32–41 (2000)
Cook, D.J., Holder, L.B.: Graph Mining. Wiley-Interscience, John Wiley Sons, Inc., Hoboken, New Jersey (2007)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press and McGraw-Hill (2001)
Gabor Csardi igraph: IGraph class. R package version 0.1.1 (2005), http://cneurocvs.rmki.kfki.hu/igraph
Gentleman, R., Whalen, E., Huber, W., Falcon, S.: Graph: A package to handle graph data structures. R package version 1.10.6 (2006)
Getoor, L., Diehl, C.P.: Link Mining: A Survey. SIGKDD Explorations 7(2), 3–12 (2005)
Getoor, L., et al.: Link Mining: a new data mining challenge. SIGKDD Explorations 5(1), 84–89 (2004)
Guest, S.D., Moody, J., Kelly, L., Rulison, K.L.: Density or Distinction? The Roles of Data Structure and Group Detection Methods in Describing Adolescent Peer Groups. Journal of Social Structure 8(1), http://www.cmu.edu/joss/content/articles/volindex.html (viewed at July 28, 2007)
Gomez-Perez, A.: Ontological Engineering with examples from the areas of knowledge management, e-commerce and the semantic web. Springer, London (2005)
Frank, O.: Statistical estimation of co-offending youth networks. Social Networks 23, 203–214 (2001)
i2 Analyst‘s Notebook. Cambrige, UK, i2 Ltd. (2006)
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)
Xu, J., Chen, H.C.: CrimeNet Explorer: A Framework for Criminal Network Knowledge Discovery. ACM Transactions on Information Systems 23(2), 201–226 (2005)
Lapsley, M. and from October 2002 B.D. Ripley RODBC: ODBC database access. R package version 1.1-7 (2006)
McCue, C.: Data Mining and Predictive Analytics Intelligence Gathering and Crime Analysis. BH Press Elsevier Oxford, England (2007)
Marcus, S.M., Moy, M., Coffman, T.: Social Network Analysis In Mining Graph Data. Cook, D.J., Holder, L.B. (eds.), John Wiley &Sons, Inc., Chichester (2007)
Mena, J.: Investigative Data Mining for security and criminal detection, Butterworth Heinemann, US (2003)
Moy, M.: Using TMODS to run the best friends group detection algorithm. 21st Century Technologies Internal Publication (2005)
Nooy, W.d., Mrvar, A., et al.: Exploratory Social Network Analysis with Pajek. Cambridge University Press, New York (2005)
Olay Bursa Local Newspaper, Technological tracking to criminal groups, http://www2.olay.com.tr/blocks/haberoku.php?id=5990&cins=Spot%20Bursa (viewed on December 19, 2006)
Police News Portal: Polis Haber Operation ‘Cash’ By Police (2006), http://www.polis.web.tr/article_view.php?aid=3666 (viewed on July 31, 2007)
R Development Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2006), http://www.R-project.org ISBN 3-900051-07-0
Scott, J.: Social Network Analysis. SAGE Publications, London (2005)
Senator, T.E.: Link Mining Applications: Progress and Challenges. SIGKDD Explorations 7(2), 76–83 (2005)
Sentient Data Detective, Sentient Information Systems (2007), http://www.www.sentient.nl/ (Viewed at July 31, 2007)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, p. 266 (1994)
Zaman National Newspaper, Police tracked down 63 crime groups with new technology help on 9th of January 2007 (2006), http://www.zaman.com.tr/webap-tr/haber.do?haberno=437444 (Viewed at July 31, 2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ozgul, F., Erdem, Z., Aksoy, H. (2008). Comparing Two Models for Terrorist Group Detection: GDM or OGDM?. In: Yang, C.C., et al. Intelligence and Security Informatics. ISI 2008. Lecture Notes in Computer Science, vol 5075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69304-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-69304-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69136-5
Online ISBN: 978-3-540-69304-8
eBook Packages: Computer ScienceComputer Science (R0)