Finding Hidden Links in Terrorist Networks by Checking Indirect Links of Different Sub-Networks

Modeling and analyzing criminal and terrorists networks is a challenging problem that has attracted considerable attention in the academia, industry and government institutions, especially intelligence services. Criminals try to keep their communications and interactions uncovered as much as possible in order not to be discovered and resolved. Their success is our society failure and vice versa. Hence, it is essential to thoroughly study such networks to investigate their details. However, incompleteness of criminal networks is one of the main problems facing investigators, due to missing links in the network; and social network methods could be effectively used to analyze and hopefully prevent, avoid or stop criminal activities. Social network analysis can be applied to criminal networks in order to elaborate on good strategies to prosecute or prevent criminal activities. Having all this in mind, our research provides a method to identify hidden links between nodes in a network using the current information available to investigators. The method presented generates networks that represent all the possible hidden links, and the links of these generated networks represent the number of times the two entities are indirectly connected in each relationship type. The method was tested on multiple small terrorism data sets and the results demonstrate that the proposed method is capable of finding interesting hidden links. This is a valuable technique in criminal network analysis, because it can assist investigators in finding hidden links in the network and reduce the amount of missing data.

[1]  Wang Jiaxin,et al.  Discovering Hierarchical Structure in Terrorist Networks , 2006, 2006 International Conference on Emerging Technologies.

[2]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[3]  Jonathan David Farley,et al.  Breaking Al Qaeda Cells: A Mathematical Analysis of Counterterrorism Operations (A Guide for Risk Assessment and Decision Making) , 2003 .

[4]  Hsinchun Chen,et al.  Analyzing Terrorist Networks: A Case Study of the Global Salafi Jihad Network , 2005, ISI.

[5]  Malik Magdon-Ismail,et al.  Locating Hidden Groups in Communication Networks Using Hidden Markov Models , 2003, ISI.

[6]  Reda Alhajj,et al.  A Global Measure for Estimating the Degree of Organization of Terrorist Networks , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[7]  Malcolm K. Sparrow,et al.  The application of network analysis to criminal intelligence: An assessment of the prospects , 1991 .

[8]  Malik Magdon-Ismail,et al.  Discovering Hidden Groups in Communication Networks , 2004, ISI.

[9]  Kathleen M. Carley,et al.  Structural Knowledge and Success of Anti-Terrorist Activity: The Downside of Structural Equivalence , 2005, J. Soc. Struct..

[10]  Hsinchun Chen,et al.  CrimeNet explorer: a framework for criminal network knowledge discovery , 2005, TOIS.

[11]  S. Strogatz.  Exploring complex networks , 2001, Nature.

[12]  W. Baker,et al.  THE SOCIAL ORGANIZATION OF CONSPIRACY: ILLEGAL NETWORKS IN THE HEAVY ELECTRICAL EQUIPMENT INDUSTRY* , 1993 .

[13]  Valdis E. Krebs,et al.  Mapping Networks of Terrorist Cells , 2001 .

[14]  Abdul Rasool Qureshi,et al.  Design and development of an early warning system to prevent terrorist attacks , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

[15]  P. Jones,et al.  Inferring missing links in partially observed social networks , 2009, J. Oper. Res. Soc..

[16]  Massimo Marchiori,et al.  How the science of complex networks can help developing strategies against terrorism , 2004 .