ABSTRACT
Telecommunication fraud is one of the most prevalent crimes nowadays, and causes most property loss of victims. The criminals of telecommunication fraud are highly organized, concealed and transnational, making investigators difficult to track and capture the suspects. In this paper, we propose a Telecom Fraud Analysis Model (TFAM) which can unveil the underlying structure of fraud groups and identify the roles of the fraudsters. The links between suspects are built using flight information, and co-offending records. Social network analysis techniques are applied to analyze group structures as well as influences of each member. We collect a real telecom fraud dataset with 113 fraudsters whose fraudulent activities spread across four countries and 17 cities. Experimental results demonstrate that our method can successfully identify the key roles and discover the hidden structure of the fraud groups.
- Communications Fraud Control Association, "CFCA 2015 Global fraud loss survey," September 2015.Google Scholar
- S. H. Li, et al., "Identifying the signs of fraudulent accounts using data mining techniques," Computers in Human Behavior, 2012.Google Scholar
- E. Otte and R. Rousseau, "Social network analysis: a powerful strategy, also for the information science," Journal of information Science, 28(6):, pp. 441--453, 2002. Google ScholarCross Ref
- J. Xu and H. Chen, "CrimeNet explorer: a framework for criminal network knowledge discovery," ACM Transactions on Information Systems, vol. 23, no. 2, pp. 201--226, April 2005. Google ScholarDigital Library
- K. Taha and P. D. Yoo, "A system for analyzing criminal social networks," ASONAM, 2015. Google ScholarDigital Library
- F. Ozgul and Z. Erdem, "Which crime features are important for criminal network members," ASONAM, 2013.Google Scholar
- H. Chen et al., "COPLINK: Managing law enforcement data and knowledge," Commun. ACM, January 2003.Google Scholar
- U. K. Wiil, J. Gniadek, and N. Memon. "Measuring link importance in terrorist networks," ASONAM, 2010. Google ScholarDigital Library
- D. Xuan, H. Yu, and J. Wang, "A novel method of centrality in terrorist network," ISCID, 2014. Google ScholarDigital Library
- S. Ressler, "Social network analysis as an approach to combat terrorism: Past, present, and future research," Homeland Security Affairs, 2006.Google Scholar
- T. Moore, R. Clayton, and R. Anderson, "The economics of online crime," The Journal of Economic Perspectives, vol. 23, no. 3, pp. 3--20, 2009. Google ScholarCross Ref
- M. Aston et al., "A preliminary profiling of Internet money mules: An Australian perspective," UIC-ATC, 2009.Google Scholar
- P. Klerks, "The network paradigm applied to criminal organizations: Theoretical nitpicking or a relevant doctrine for investigators? Recent developments in the Netherlands," Connections, 24(3), pp. 53--65, 2001.Google Scholar
- IBM, "i2 Analyst's Notebook", May 2015.Google Scholar
- Duijn, Paul AC, Victor Kashirin, and Peter MA Sloot. "The relative ineffectiveness of criminal network disruption." Scientific reports, 2014.Google Scholar
- J. Xu and H. Chen, "Criminal network analysis and visualization," Communications of the ACM, vol. 48, no. 6, pp. 100--107, June 2005. Google ScholarDigital Library
- J. Xu, et al.," Analyzing and visualizing criminal network dynamics: a case study," Intelligence and Security Informatics (ISI), 2004.Google Scholar
- Z. Xia, and Z. Bu, "Community detection based on a semantic network," Knowledge-Based Systems, 26: pp. 30--39, 2012. Google ScholarDigital Library
- Liu, Xiaodong, et al. "Criminal networks: Who is the key player?," 2012.Google Scholar
- Tayebi, Mohammad A., et al. "Locating central actors in co-offending networks." ASONAM, 2011. Google ScholarDigital Library
- F. Ozgul and Z. Erdem, "Which crime features are important for criminal network members," ASONAM, 2013. Google ScholarDigital Library
- S. H. Li et al., "Identifying the signs of fraudulent accounts using data mining techniques," Computers in Human Behavior, 2012. Google ScholarDigital Library
- L. K. Branting et al., " Graph analytics for healthcare fraud risk estimation," ASONAM, 2016. Google ScholarCross Ref
- R. S. Burt, "Structural holes: The social structure of competition," Harvard university press, 2009.Google Scholar
- S. P. Borgatti et al., "Ucinet for Windows: Software for social network analysis," Harvard, MA: Analytic Technologies, 2002.Google Scholar
- Mining the Networks of Telecommunication Fraud Groups using Social Network Analysis
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