New framework that uses patterns and relations to understand terrorist behaviors

A new framework is proposed to understand the activity patterns and relations.A new similarity function is proposed to estimate the relationships among events.The new model is proposed to estimate the importance of the features.Over-training in the model is prevented by using the LASSO regularization.The government can control the terrorist behaviors using the intelligent framework. Terrorism is a complex phenomenon with high uncertainties in user strategy. The uncertain nature of terrorism is a main challenge in the design of counter-terrorism policy. Government agencies (e.g., CIA, FBI, NSA, etc.) cannot always use social media and telecommunications to capture the intentions of terrorists because terrorists are very careful in the use of these environments to plan and prepare attacks. To address this issue, this research aims to propose a new framework by defining the useful patterns of suicide attacks to analyze the terrorist activity patterns and relations, to understand behaviors and their future moves, and finally to prevent potential terrorist attacks. In the framework, a new network model is formed, and the structure of the relations is analyzed to infer knowledge about terrorist attacks. More specifically, an Evolutionary Simulating Annealing Lasso Logistic Regression (ESALLOR) model is proposed to select key features for similarity function. Subsequently, a new weighted heterogeneous similarity function is proposed to estimate the relationships among attacks. Moreover, a graph-based outbreak detection is proposed to define hazardous places for the outbreak of violence. Experimental results demonstrate the effectiveness of our framework with high accuracy (more than 90% accuracy) for finding patterns when compared with that of actual terrorism events in 2014 and 2015. In conclusion, by using this intelligent framework, governments can understand automatically how terrorism will impact future events, and governments can control terrorists behaviors and tactics to reduce the risk of future events.

[1]  Ilker Akgun,et al.  Fuzzy integrated vulnerability assessment model for critical facilities in combating the terrorism , 2010, Expert Syst. Appl..

[2]  Mansi Ghodsi,et al.  A review of data mining applications in crime , 2016, Stat. Anal. Data Min..

[3]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[4]  Shyam Varan Nath,et al.  Crime Pattern Detection Using Data Mining , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops.

[5]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[6]  Mário A. T. Figueiredo,et al.  An unsupervised approach to feature discretization and selection , 2012, Pattern Recognit..

[7]  Albert-László Barabási,et al.  Linked: The New Science of Networks , 2002 .

[8]  Parham Moradi,et al.  A graph theoretic approach for unsupervised feature selection , 2015, Eng. Appl. Artif. Intell..

[9]  George M. Mohay,et al.  Mining e-mail content for author identification forensics , 2001, SGMD.

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

[11]  I ScottKirkpatrick Optimization by Simulated Annealing: Quantitative Studies , 1984 .

[12]  Renuka Nagpal,et al.  Crime Analysis using K-Means Clustering , 2013 .

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

[14]  Rick Archibald,et al.  Feature Selection and Classification of Hyperspectral Images With Support Vector Machines , 2007, IEEE Geoscience and Remote Sensing Letters.

[15]  S. Gunasundari,et al.  Velocity Bounded Boolean Particle Swarm Optimization for improved feature selection in liver and kidney disease diagnosis , 2016, Expert Syst. Appl..

[16]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[17]  P. Thongtae,et al.  An Analysis of Data Mining Applications in Crime Domain , 2008, 2008 IEEE 8th International Conference on Computer and Information Technology Workshops.

[18]  Bastin Tony Roy Savarimuthu,et al.  Extracting Crime Information from Online Newspaper Articles , 2014, AWC.

[19]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[20]  Sukanya,et al.  Criminals and crime hotspot detection using data mining algorithms: clustering and classification , 2012 .

[21]  Huan Liu,et al.  Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.

[22]  Panagiotis Kanellis,et al.  Digital Crime And Forensic Science in Cyberspace (N/A) , 2006 .

[23]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

[24]  Amber Jaycocks,et al.  Predicting Suicide Attacks: Integrating Spatial, Temporal, and Social Features of Terrorist Attack Targets , 2013 .

[25]  Scott Kirkpatrick,et al.  Optimization by simulated annealing: Quantitative studies , 1984 .

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

[27]  T.R. Coffman,et al.  Pattern classification in social network analysis: a case study , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[28]  Hsinchun Chen,et al.  Using Coplink to Analyze Criminal-Justice Data , 2002, Computer.

[29]  Pankoo Kim,et al.  Text analysis for detecting terrorism-related articles on the web , 2014, J. Netw. Comput. Appl..

[30]  Hiroki Sayama,et al.  Introduction to the Modeling and Analysis of Complex Systems , 2015 .

[31]  Edwin R. Hancock,et al.  A Graph-Based Approach to Feature Selection , 2011, GbRPR.

[32]  Gang Wang,et al.  Crime data mining: a general framework and some examples , 2004, Computer.

[33]  Vipin Kumar,et al.  Similarity Measures for Categorical Data: A Comparative Evaluation , 2008, SDM.

[34]  Edwin R. Hancock,et al.  Localized Graph-Based Feature Selection for Clustering , 2012, ICIAR.

[35]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[36]  Chun-An Chou,et al.  A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey , 2015 .

[37]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[38]  David R. Frelinger,et al.  Understanding Why Terrorist Operations Succeed or Fail , 2009 .

[39]  Roger A. Clarke,et al.  Information technology and dataveillance , 1988, CACM.

[40]  Hsinchun Chen Dark Web: Exploring and Data Mining the Dark Side of the Web , 2011 .

[41]  Luciano Telesca,et al.  Are global terrorist attacks time-correlated? , 2006 .

[42]  Christian Baumgartner,et al.  A network-based feature selection approach to identify metabolic signatures in disease. , 2012, Journal of theoretical biology.

[43]  E. Chenoweth,et al.  On Classifying Terrorism: A Potential Contribution of Cluster Analysis for Academics and Policy-makers , 2007 .

[44]  K. Rameshkumar,et al.  A Complete Survey on application of Frequent Pattern Mining and Association Rule Mining on Crime Pattern Mining , 2014 .

[45]  Fengxi Song,et al.  Feature Selection Using Principal Component Analysis , 2010, 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization.

[46]  Huaiqing Wang,et al.  An ontology for causal relationships between news and financial instruments , 2008, Expert Syst. Appl..

[47]  Jiawei Han,et al.  Generalized Fisher Score for Feature Selection , 2011, UAI.

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

[49]  TutunSalih,et al.  New framework that uses patterns and relations to understand terrorist behaviors , 2017 .

[50]  Alessandro Vespignani,et al.  Large scale networks fingerprinting and visualization using the k-core decomposition , 2005, NIPS.

[51]  Ben-xian Li,et al.  Networks model of the East Turkistan terrorism , 2015 .

[52]  John Bohannon,et al.  Counterterrorism's new tool: 'metanetwork' analysis. , 2009, Science.

[53]  Tony R. Martinez,et al.  Improved Heterogeneous Distance Functions , 1996, J. Artif. Intell. Res..

[54]  Hsinchun Chen,et al.  An International Perspective on Fighting Cybercrime , 2003, ISI.

[55]  Hui-Huang Hsu,et al.  Hybrid feature selection by combining filters and wrappers , 2011, Expert Syst. Appl..

[56]  Selma Ayse Özel,et al.  A hybrid approach of differential evolution and artificial bee colony for feature selection , 2016, Expert Syst. Appl..

[57]  Divya Prakash,et al.  Detection and Analysis of Hidden Activities in Social Networks , 2013 .