Handbook of Computational Approaches to Counterterrorism

Terrorist groups throughout the world have been studied primarily through the use of social science methods. However, major advances in IT during the past decade have led to significant new ways of studying terrorist groups, making forecasts, learning models of their behaviour, and shaping policies about their behaviour. Handbook of Computational Approaches to Counterterrorism provides the first in-depth look at how advanced mathematics and modern computing technology is shaping the study of terrorist groups. This book includes contributions from world experts in the field, and presents extensive information on terrorism data sets, new ways of building such data sets in real-time using text analytics, introduces the mathematics and computational approaches to understand terror group behaviour, analyzes terror networks, forecasts terror group behaviour, and shapes policies against terrorist groups. Auxiliary information will be posted on the books website. This book targets defence analysts, counter terror analysts, computer scientists, mathematicians, political scientists, psychologists, and researchers from the wide variety of fields engaged in counter-terrorism research. Advanced-level students in computer science, mathematics and social sciences will also find this book useful.

[1]  W. W. Peterson,et al.  The theory of signal detectability , 1954, Trans. IRE Prof. Group Inf. Theory.

[2]  F. N. David,et al.  Principles and procedures of statistics. , 1961 .

[3]  E. B. Page.  Ordered Hypotheses for Multiple Treatments: A Significance Test for Linear Ranks , 1963 .

[4]  Stephen J. Andriole,et al.  The rise and fall of event data: From basic research to applied use in the u.s. department of defense , 1984 .

[5]  W. Gove,et al.  ARE UNIFORM CRIME REPORTS A VALID INDICATOR OF THE INDEX CRIMES? AN AFFIRMATIVE ANSWER WITH MINOR QUALIFICATIONS* , 1985 .

[6]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[7]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[8]  S Kullback,et al.  LETTER TO THE EDITOR: THE KULLBACK-LEIBLER DISTANCE , 1987 .

[9]  T. Gurr.  Empirical Research on Political Terrorism: The State of the Art and How it Might be Improved , 1988 .

[10]  Martin Hollis,et al.  Explaining and Understanding International Relations , 1990 .

[11]  Ariel Merari,et al.  Academic research and government policy on terrorism , 1991 .

[12]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[13]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[14]  R. Bro.  Multiway calibration. Multilinear PLS , 1996 .

[15]  Frances Hagopian.  Comparative Politics: Rationality, Culture and Structure , 1998 .

[16]  D. Laitin.  Comparative Politics: the State of the Subdiscipline , 2000 .

[17]  Mary Caprioli,et al.  Gendered Conflict , 2000 .

[18]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[19]  Alexander J. Smola,et al.  Advances in Large Margin Classifiers , 2000 .

[20]  J. Tickner.  Gendering World Politics: Issues and Approaches in the Post-Cold War Era , 2001 .

[21]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[22]  Francesca Polletta,et al.  Dynamics of Contention , 2002 .

[23]  Holly J. McCammon,et al.  How Movements Win: Gendered Opportunity Structures and U.S. Women's Suffrage Movements, 1866 to 1919 , 2001, American Sociological Review.

[24]  Gary King,et al.  Improving Forecasts of State Failure , 2001 .

[25]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[26]  Sean P. O'Brien,et al.  Anticipating the Good, the Bad, and the Ugly , 2002 .

[27]  Robert J. Lempert,et al.  Making Computational Social Science Effective , 2002 .

[28]  B. Smith,et al.  THE PROSECUTION AND PUNISHMENT OF INTERNATIONAL TERRORISTS IN FEDERAL COURTS: 1980–1998* , 2002 .

[29]  J. Fearon,et al.  Ethnicity, Insurgency, and Civil War , 2003, American Political Science Review.

[30]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[31]  Ruxu Du,et al.  Fault diagnosis using support vector machine with an application in sheet metal stamping operations , 2004 .

[32]  Gary King,et al.  Theory and Evidence in International Conflict: A Response to de Marchi, Gelpi, and Grynaviski , 2004, American Political Science Review.

[33]  War is Too Important to Be Left to Ideological Amateurs , 2005 .

[34]  Ethan Bueno de Mesquita.  The Terrorist Endgame , 2005 .

[35]  M. Caprioli.  Primed for Violence: The Role of Gender Inequality in Predicting Internal Conflict , 2005 .

[36]  Ethan Bueno de Mesquita.  Conciliation, Counterterrorism, and Patterns of Terrorist Violence , 2005, International Organization.

[37]  Patrick M. Regan,et al.  Greed, Grievance, and Mobilization in Civil Wars , 2005 .

[38]  Ethan Bueno de Mesquita.  The Quality of Terror , 2005 .

[39]  Hsinchun Chen,et al.  Criminal network analysis and visualization , 2005, CACM.

[40]  J. Goldstone,et al.  A Global Forecasting Model of Political Instability , 2005 .

[41]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[42]  Rhonda L. Callaway,et al.  Toward a Theory of Terrorism: Human Security as a Determinant of Terrorism , 2006 .

[43]  Philip A. Schrodt.  Forecasting Conflict in the Balkans using Hidden Markov Models , 2006 .

[44]  Alexander W. Skaburskis,et al.  The Sandbox for analysis: concepts and methods , 2006, CHI.

[45]  D. S. Bennett,et al.  Predicting the Length of the 2003 U.S.-Iraq War , 2006 .

[46]  Gary King,et al.  When Can History Be Our Guide? The Pitfalls of Counterfactual Inference , 2007 .

[47]  J. Scott Armstrong,et al.  The Ombudsman: Value of Expertise for Forecasting Decisions in Conflicts , 2004, Interfaces.

[48]  V. S. Subrahmanian,et al.  CARA: A Cultural Adversarial Reasoning Architecture , 2007 .

[49]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[50]  Gary LaFree,et al.  Introducing the Global Terrorism Database , 2007 .

[51]  M. Mazarr.  The Iraq War and Agenda Setting , 2007 .

[52]  Kristian Skrede Gleditsch,et al.  Transnational Dimensions of Civil War , 2007 .

[53]  Philip A. Schrodt.  Pattern Recognition of International Crises using Hidden Markov Models , 2007 .

[54]  Ramazan Yalcinkaya,et al.  Police officers' adoption of information technology: A case study of the Turkish POLNET system. , 2007 .

[55]  D. J. Young,et al.  A Violent Future? Political Risk Insurance Markets and Violence Forecasts , 2008 .

[56]  Benjamin E. Goldsmith,et al.  Regime Type and International Conflict: Towards a General Model , 2008 .

[57]  Samir Khuller,et al.  Computing most probable worlds of action probabilistic logic programs: scalable estimation for 1030,000 worlds , 2007, Annals of Mathematics and Artificial Intelligence.

[58]  Krishna R. Pattipati,et al.  Model-Based Prognostic Techniques Applied to a Suspension System , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[59]  Ying Lu,et al.  Verbal Autopsy Methods with Multiple Causes of Death , 2008, 0808.0645.

[60]  J. Horgan,et al.  From Profiles to Pathways and Roots to Routes: Perspectives from Psychology on Radicalization into Terrorism , 2008 .

[61]  V. S. Subrahmanian,et al.  CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior , 2008, IEEE Intelligent Systems.

[62]  S. K. Awasthi,et al.  The New Age of Terrorism , 2009 .

[63]  V. S. Subrahmanian,et al.  CAPE: Automatically Predicting Changes in Group Behavior , 2009 .

[64]  Gerald Schneider,et al.  A global risk assessment model for civil wars , 2009 .

[65]  Alexander M. Millkey.  The Black Swan: The Impact of the Highly Improbable , 2009 .

[66]  Kristin M. Bakke,et al.  The perils of policy by p-value: Predicting civil conflicts , 2010 .

[67]  Gary LaFree,et al.  The Global Terrorism Database: Accomplishments and Challenges , 2010 .

[68]  John Wigle.  Introducing the Worldwide Incidents Tracking System (WITS) , 2010 .

[69]  Gary King,et al.  A Method of Automated Nonparametric Content Analysis for Social Science , 2010 .

[70]  Halil Aydınalp.  TERROR IN THE MIND OF GOD: THE GLOBAL RISE OF RELIGIOUS VIOLENCE , 2010 .

[71]  Krishna R. Pattipati,et al.  Intelligent data-driven classification and forecasting processes for complex engineering and social systems , 2011 .

[72]  Rami Puzis,et al.  Link Prediction in Highly Fractional Data Sets , 2013 .

[73]  John J. Salerno,et al.  The NOEM: A Tool for Understanding/ Exploring the Complexities of Today’s Operational Environment , 2013 .

[74]  Sean P. O'Brien.  A Multi-Method Approach for Near Real Time Conflict and Crisis Early Warning , 2013 .

[75]  V. S. Subrahmanian,et al.  Policy Analytics Generation Using Action Probabilistic Logic Programs , 2013 .

[76]  Nate Haken,et al.  A CAST Case-Study: Assessing Risk in the Niger Delta , 2013 .

[77]  Shanchieh Jay Yang,et al.  Temporal and Spatial Analyses for Large-Scale Cyber Attacks , 2013 .

[78]  Reda Alhajj,et al.  Data Analysis Based Construction and Evolution of Terrorist and Criminal Networks , 2013 .

[79]  Bo An,et al.  PROTECT in the Ports of Boston, New York and Beyond: Experiences in Deploying Stackelberg Security Games with Quantal Response , 2013 .

[80]  Robbert Fokkink,et al.  The Application of Search Games to Counter Terrorism Studies , 2013 .

[81]  Nasrullah Memon,et al.  A Realistic Framework for Counter-terrorism in Multimedia , 2013 .

[82]  W. Karush.  Minima of Functions of Several Variables with Inequalities as Side Conditions , 2014 .

[83]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .