Computational Methods for Counterterrorism

ISBN-13: 9783642011405 / Angielski / Twarda / 2009 / 306 str.

Shlomo Argamon; Howard Newton

Opis

Modern terrorist networks pose an unprecedented threat to international security. The question of how to neutralize that threat is complicated radically by their fluid, non-hierarchical structures, religious and ideological motivations, and predominantly non-territorial objectives. Governments and militaries are crafting new policies and doctrines to combat terror, but they desperately need new technologies to make these efforts effective. This book collects a wide range of the most current computational research that addresses critical issues for countering terrorism, including:

• Finding, summarizing, and evaluating relevant information from large and changing data stores;
• Simulating and predicting enemy acts and outcomes; and
• Producing actionable intelligence by finding meaningful patterns hidden in huge amounts of noisy data.

The book s four sections describe current research on discovering relevant information buried in vast amounts of unstructured data; extracting meaningful information from digitized documents in multiple languages; analyzing graphs and networks to shed light on adversaries goals and intentions; and developing software systems that enable analysts to model, simulate, and predict the effects of real-world conflicts. The research described in this book is invaluable reading for governmental decision-makers designing new policies to counter terrorist threats, for members of the military, intelligence, and law enforcement communities devising counterterrorism strategies, and for researchers developing more effective methods for knowledge discovery in complicated and diverse datasets."

Spis treści

Foreword, James A. Hendler Preface Part I: Information Access 1) On Searching in the 'Real World' 2) Signature-Based Retrieval of Scanned Documents Using Conditional Random Fields 3) What Makes a Good Summary? 4) A Prototype Search Toolkit Part II: Text Analysis 5) Unapparent Information Revelation: Text Mining for Counterterrorism 6) Identification of Sensitive Unclassified Information 7) Rich Language Analysis for Counterterrorism Part III: Graphical Models 8) Dicliques: Finding Needles in Haysticks 9) Information Superiority via Formal Concept Analysis 10) Reflexive Analysis of Groups 11) Evaluating Self-Reflexion Analysis Using Repertory Grids Part IV: Conflict Analysis 12) Anticipating Terrorist Safe Havens from Instability Induced Conflict 13) Applied Counterfactual Reasoning 14) Adversarial Planning in Networks 15) Gaming and Simulating Ethno-Political Conflicts Index

O autorze

Shlomo Argamon is Associate Professor of Computer Science at the Illinois Institute of Technology, Chicago, IL, USA, since 2002. Prior to that, he had held academic positions at Bar-Ilan University, where he held a Fulbright Postdoctoral Fellowship (1994-96), and at the Jerusalem College of Technology. Dr. Argamon received his B.S. (1988) in Applied Mathematics from Carnegie-Mellon University, and his M.Phil. (1991) and Ph.D. (1994) in Computer Science from Yale University, where he was a Hertz Foundation Fellow. His current research interests lie mainly in the use of machine learning methods to aid in functional analysis of natural language, with particular focus on questions of style. During his career, Dr. Argamon has worked on a variety of problems in experimental machine learning, including robotic map-learning, theory revision, and natural language processing, and has published numerous research papers in these areas.

Blurb

Modern terrorist networks pose an unprecedented threat to international security. Their fluid and non-hierarchical structures, their religious and ideological motivations, and their predominantly non-territorial objectives all radically complicate the question of how to neutralize them. As governments and militaries work to devise new policies and doctrines to combat terror, new technologies are desperately needed to make these efforts effective.

This book collects a wide range of the most current computational research addressing critical issues for counterterrorism in a dynamic and complex threat environment:

• finding, summarizing, and evaluating relevant information from large and dynamic data stores;
• simulation and prediction of likely enemy actions and the effects of proposed counter-efforts; and
• producing actionable intelligence by finding meaningful patterns hidden in masses of noisy data items.

The contributions are organized thematically into four sections. The first section concerns efforts to provide effective access to small amounts of relevant information buried in enormous amounts of diverse unstructured data. The second section discusses methods for the key problem of extracting meaningful information from digitized documents in various languages. The third section presents research on analyzing graphs and networks, offering new ways of discovering hidden structures in data and profiles of adversaries’ goals and intentions. Finally, the fourth section of the book describes software systems that enable analysts to model, simulate, and predict the effects of real-world conflicts.

The models and methods discussed in this book are invaluable reading for governmental decision-makers designing new policies to counter terrorist threats, for members of the military, intelligence, and law enforcement communities devising counterterrorism strategies based on new technologies, and for academic and industrial researchers devising more effective methods for knowledge discovery in complicated and diverse datasets.