Terrorist Network Analysis and Identification of Main Actors Using Machine Learning Techniques

The prediction of terrorist network and identifying main actors is an important issue for intelligence and security informatics. In this article we present a method to analyze social network using machine learning techniques. The proposed technique uses k-core concept to remove unwanted and passive nodes from the whole network. It then extracts multiple features and uses hybrid classifier to identify main actors. The proposed technique is tested on a publicly available dataset and results show significance of proposed system.

[1]  Noah E. Friedkin,et al.  Social positions in influence networks , 1997 .

[2]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[3]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[4]  H. Stanley,et al.  Multifractal properties of price fluctuations of stocks and commodities , 2003, cond-mat/0308012.

[5]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[6]  Dipak K. Gupta,et al.  Understanding Terrorism and Political Violence: The Life Cycle of Birth, Growth, Transformation, and Demise , 2008 .

[7]  Reinhard Schneider,et al.  A survey of visualization tools for biological network analysis , 2008, BioData Mining.

[8]  Brian Delaney,et al.  Modeling and detection techniques for Counter-Terror Social Network Analysis and Intent Recognition , 2009, 2009 IEEE Aerospace conference.

[9]  Allan Bickle,et al.  The k-Cores of a Graph , 2010 .

[10]  Kathleen M. Carley,et al.  Detecting Change in Longitudinal Social Networks , 2011, J. Soc. Struct..

[11]  Chaochang Chiu,et al.  Internet Auction Fraud Detection Using Social Network Analysis and Classification Tree Approaches , 2011, Int. J. Electron. Commer..

[12]  Vladimir Batagelj,et al.  Fast algorithms for determining (generalized) core groups in social networks , 2011, Adv. Data Anal. Classif..

[13]  Muhammad Younus Javed,et al.  Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier , 2014, TheScientificWorldJournal.

[14]  J Gómez-Gardeñes,et al.  k-core percolation on multiplex networks. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Nitesh V. Chawla,et al.  Deep learning for network analysis: Problems, approaches and challenges , 2016, MILCOM 2016 - 2016 IEEE Military Communications Conference.

[16]  Moonsoo Kang,et al.  Euclidean distance based label noise cleaning , 2017, 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN).

[17]  Jeffrey Xu Yu,et al.  Finding influential communities in massive networks , 2017, The VLDB Journal.

[18]  Domenico Talia,et al.  A Peak Detection Method to Uncover Events from Social Media , 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[19]  Alexander Gutfraind,et al.  A graph database framework for covert network analysis: An application to the Islamic State network in Europe , 2017, Soc. Networks.

[20]  Ahmad Kamran Malik,et al.  Significance of machine learning algorithms in professional blogger's classification , 2017, Comput. Electr. Eng..

[21]  H. A. Eiselt.  Destabilization of terrorist networks , 2018 .