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Taha, K.; Yoo, Paul (2016)
Languages: English
Types: Article

Classified by OpenAIRE into

Members of a criminal organization, who hold central positions in the organization, are usually targeted by criminal investigators for removal or surveillance. This is because they play key and influential roles by acting as commanders, who issue instructions or serve as gatekeepers. Removing these central members (i.e., influential members) is most likely to disrupt the organization and put it out of business. Most often, criminal investigators are even more interested in knowing the portion of these influential members, who are the immediate leaders of lower level criminals. These lower level criminals are the ones who usually carry out the criminal works; therefore, they are easier to identify. The ultimate goal of investigators is to identify the immediate leaders of these lower level criminals in order to disrupt future crimes. We propose, in this paper, a forensic analysis system called SIIMCO that can identify the influential members of a criminal organization. Given a list of lower level criminals in a criminal organization, SIIMCO can also identify the immediate leaders of these criminals. SIIMCO first constructs a network representing a criminal organization from either mobile communication data that belongs to the organization or crime incident reports. It adopts the concept space approach to automatically construct a network from crime incident reports. In such a network, a vertex represents an individual criminal, and a link represents the relationship between two criminals. SIIMCO employs formulas that quantify the degree of influence/importance of each vertex in the network relative to all other vertices. We present these formulas through a series of refinements. All the formulas incorporate novelweighting schemes for the edges of networks. We evaluated the quality of SIIMCO by comparing it experimentally with two other systems. Results showed marked improvement.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] A. Wesolowski, N. Eagle, A. J. Tatem, D. L. Smith, A. M. Noor, R. W. Snow, and C. O. Buckee, “Quantifying the impact of human mobility on malaria,” Science, vol. 338, no. 6104, pp. 267-270, 2012.
    • [2] A. Milani Fard, M. Ester, Collaborative Mining in Multiple Social Networks Data for Criminal Group Discovery, IEEE International Conference on Social Computing (SocialCom), 2009.
    • [3] BREIGER, R. L. 2004. The analysis of social networks. In Handbook of Data Analysis, M. A. Hardy and A. Bryman, Eds. Sage Publications, London, U.K. 505-526.
    • [4] BREIGER, R. L., BOORMAN, S. A., AND ARABIE, P. 1975. An algorithm for clustering relational data, with applications to social network analysis and comparison with multidimensional scaling. J. Math. Psych. 12, 328-383.
    • [5] BAKER, W. E. AND FAULKNER R. R. 1993. The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry. Amer. Soc. Rev. 58, 837-860.
    • [6] Bell, S., McDiarmid, A., Irvine, J. Nodobo: Mobile Phone as a Software Sensor for Social Network Research. Proceedings of Context Awareness for Proactive Systems, May 2011.
    • [7] Bower, K. When to Use Fisher's Exact Test. American Society for Quality, Six Sigma Forum Magazine, Vol. 2, No. 4, 2003.
    • [8] Baldi, P. & Hatfield, W. (2002), DNA Microarrays and Gene Expression, Cambridge University Press, Cambridge, UK. [80].
    • [9] CHEN, H. AND LYNCH, K. J. 1992. Automatic construction of networks of concepts characterizing document databases. IEEE Trans. Syst. Man Cybernet. 22, 885-902.
    • [10] CHEN, H., ZENG, D., ATABAKHSH, H., WYZGA, W., AND SCHROEDER, J. 2003. Coplink: Managing law enforcement data and knowledge. Commun. ACM 46, 28-34.
    • [11] Catanese, S., Ferrara, E., & Fiumara, G. (2013). Forensic analysis of phone call networks. Social Network Analysis and Mining, 3(1), 15-33.
    • [12] DBLP bibliography, 2014. [Online]. Available: http://www.informatik.unitrier.de/ ley/db/
    • [13] Enron Corporation from Wikipedia. Available at: https://www.uwosh.edu/llce/conted/lir/course-listings/Enron%20Scandal.pdf.
    • [14] E. Ferrara, P. De Meo, S. Catanese, and G. Fiumara, “Detecting criminal organizations in mobile phone networks,” Expert Systems with Applications, vol. 41, no. 13, pp. 5733-5750, 2014.
    • [15] EVAN, W. M. 1972. An organization-set model of interorganizational relations. In Interorganizational Decision-Making, M. Tuite, R. Chisholm, and M. Radnor, Eds. Aldine Publishers, Chicago, IL, 181-200.
    • [16] Enron Email Dataset. Available at: http://www-2.cs.cmu.edu/~enron/.
    • [17] F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, and C. Ratti, “Realtime urban monitoring using cell phones: A case study in rome,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 1, pp. 141-151, 2011.
    • [18] Girvan, M., & Newman, M. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821.
    • [19] Hauck, R. V., Atabakhsh, H., Ongvasith, P., Gupta, H., Chen, H. 2002. Using Coplink to analyze criminal-justice data. IEEE Comput. 35, 30-37.
    • [20] H. Sarvari, E. Abozinadah, A. Mbaziira, and D. McCoy, “Constructing and analyzing criminal networks,” CA, USA, May 2014, pp. 84-91.
    • [21] H. Wang, C. K. Chang, H.-I. Yang, and Y. Chen, “Estimating the relative importance of nodes in social networks,” Journal of Information Processing, vol. 21, no. 3, pp. 414-422, 2013.
    • [22] J. J. Xu and H. Chen, “CrimeNet explorer: A framework for criminal network knowledge discovery,” ACM Trans. Inf. Syst., vol. 23, no. 2, pp. 201-226, Apr. 2005.
    • [23] J. Pattillo, N. Youssef, and S. Butenko, “Clique relaxation models in social network analysis,” in Handbook of Optimization in Complex Networks. Springer, 2012, pp. 143-162.
    • [24] Keila, P.S. and D.B. Skillicorn (2005), 'Structure in the Enron email dataset', Computational & Mathematical Organization Theory, 11(3), 183-99.
    • [25] Krebs, V. (2002). Mapping networks of terrorist cells. Connections, 24(3), 43- 52.
    • [26] Kleinberg, Jon. Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM) 46 (5), 604-632.
    • [27] Klerks P., “The Network Paradigm Applied to Criminal Organisations: Theoretical nitpicking or a relevant doctrine for investigators? Recent developments in the Netherlands”, Connections 24(3): 53-65, 2001, INSNA.
    • [28] LORRAIN, F. P. AND WHITE, H. C. 1971. Structural equivalence of individuals in social networks. J. Math. Soc. 1, 49-80.
    • [29] L. Cavique, A. B. Mendes, and J. M. Santos, “An algorithm to discover the kclique cover in networks,” in Progress in Artificial Intelligence. Springer, 2009, pp. 363-373.
    • [30] M. Akbas, R. Avula, M. Bassiouni, and D. Turgut, “Social network generation and friend ranking based on mobile phone data,” 2013, pp. 1444-1448.
    • [31] MCANDREW, D. 1999. The structural analysis of criminal networks. In The Social Psychology of Crime: Groups, Teams, and Networks. D. Canter and L. Alison, Eds. Dartmouth Publishing, Aldershot, UK, 53-94.
    • [32] Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos and Shiqiang Yang. CatchSync: Catching Synchronized Behavior in Large Directed Graphs. The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), New York City, USA, August 24 - August 27, 2014.
    • [33] Memon, Bisharat, Identifying Important Nodes in Weighted Covert Networks Using Generalized Centrality Measures. 2012 European Intelligence and Security Informatics Conference (EISIC 2012).
    • [34] Newman, M. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 69(6), 066133.
    • .
    • [42] Taha, K., Homouz, D., Al Muhairi, H., and Al Mahmoud, Z. "GRank: A Middleware Search Engine for Ranking Genes by [502] Relevance to Given Genes". BMC Bioinformatics 2013, 14:251, doi:10.1186/1471-2105-14-251.
    • [43] Taha, K. and Elmasri, R. "SPGProfile: Speak Group Profile." Information Systems (IS), 2010, Elsevier, Vol. 35, No. 7, pp. 774-790.
    • [44] Taha, K. and Elmasri, R. "BusSEngine: A Business Search Engine." Knowledge and Information Systems: An International Journal (KAIS), 2010, LNCS, Springer, Vol. 23, No. 2, pp. 153-197.
    • [45] Taha, K. and Elmasri, R. "CXLEngine: A Comprehensive XML Loosely Structured Search Engine." DataX'08 at EDBT'08 (Database technologies for handling XML information on the web), Nantes, France, March 2008.
    • [46] U. K. Wiil, J. Gniadek, N. Memon; Measuring Link Importance in Terrorist Networks. Social Network Analysis, International Conference On Advances in Social Networks Analysis and Mining, ASONAM 2010.
    • [47] Wellman, B. 1988. Structural analysis: From method and metaphor to theory and substance. In Social structures: A network approach, B. Wellman and S. D. Berkowitz, Eds. Cambridge University Press, Cambridge, UK, 19-61.
    • [48] Yang, L. Based on social network crime organization relation mining and central figure determining. 2012 IEEE International Conference on Computer Science and Automation Engineering, June 2012. Kamal Taha is an Assistant Professor in the Department of Electrical and Computer Engineering at Khalifa University, UAE, since 2010. He received his Ph.D. in Computer Science from the University of Texas at Arlington, USA, in March 2010. He has over 60 refereed publications that have appeared in prestigious top ranked journals, conference proceedings, and book chapters.
    • Fifteen of his publications have appeared (or are forthcoming) in IEEE Transactions journals. He was as an Instructor of Computer Science at the University of Texas at Arlington, USA, from August 2008 to August 2010. He worked as Engineering Specialist for Seagate Technology, USA, from 1996 to 2005 (Seagate is a leading computer disc drive manufacturer in the US). His research interests span Information Forensics & Security, bioinformatics, information retrieval, data mining, and databases, with an emphasis on making data retrieval and exploration in emerging applications more effective, efficient, and robust. He serves as a member of the Program Committee, editorial board, and review panel for a number of international conferences and journals, some of which are IEEE and ACM journals. He is a Senior Member of the IEEE. Paul D. Yoo received his PhD in Engineering and IT from the University of Sydney (USyd)
    • [35] Nodobo: Available at: http://nodobo.com/release.html in 2008. He was a Research Fellow in the
    • [36] P. I. Snchez, E. Mller, O. Irmler, and K. Bhm, “Local context selection for Centre for Distributed and High Performance outlier ranking in graphs with multiple numeric node attributes,” in Computing, at USyd from 2008 to 2009, and Proceedings of the 26th International Conference on Scientific and Statistical Database Management, New York, NY, USA, 2014, pp. 1-12. PHD Researcher (Quantitative Analysis) at the
    • [37] Sageman, M. (2004). Understanding terror networks. University of Capital Markets CRC, administered by the Pennsylvania Press. Australia Federal Dept. for Education, Science
    • [38] Stanford Tokenizer, Part-of-Speech Tagger, and Named Entity Recognizer. and Training, from 2004 to 2008. He was with the ATIC-Khalifa Downloaded from: http://nlp.stanford.edu/software/ Semiconductor Research Center, KUSTAR from 2009 to 2014 as
    • [39] Shang, X., Yuan, Y. Social Network Analysis in Multiple Social Networks Data an Assistant Professor in Data Science. He is currently a Lecturer for Criminal Group Discovery. 2012 International Conference on Cyber- at the Data Science Institute, Bournemouth University, U.K. Paul Enabled Distributed Computing and Knowledge Discovery (CyberC). also holds over 40 prestigious journal and conference publications
    • [40] Taha, K. "Determining the Semantic Similarities among Gene Ontology Terms". IEEE Journal of Biomedical and Health Informatics (IEEE J-BHI), and is currently actively involved in editorial board, technical 2013, Vol. 17, Issue 3, pp. 512 - 525. program committees, and review panels of the data science and
    • [41] Todd, M., & Nomani, A. (2011). The truth left behind: Inside the kidnapping analytics areas for top conference and journal publications such as and murder of Daniel Pearl. New York. IEEE, ACM and ISCB. He is a Senior Member of IEEE. All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
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