Memon / Farley / Hicks Mathematical Methods in Counterterrorism

1. Auflage 2009
ISBN: 978-3-211-09442-6
Verlag: Springer Vienna
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 389 Seiten

Terrorism is one of the serious threats to international peace and security that we face in this decade. No nation can consider itself immune from the dangers it poses, and no society can remain disengaged from the efforts to combat it. The termcounterterrorism refers to the techniques, strategies, and tactics used in the ?ght against terrorism. Counterterrorism efforts involve many segments of so- ety, especially governmental agencies including the police, military, and intelligence agencies (both domestic and international). The goal of counterterrorism efforts is to not only detect and prevent potential future acts but also to assist in the response to events that have already occurred. A terrorist cell usually forms very quietly and then grows in a pattern - sp- ning international borders, oceans, and hemispheres. Surprising to many, an eff- tive 'weapon', just as quiet - mathematics - can serve as a powerful tool to combat terrorism, providing the ability to connect the dots and reveal the organizational pattern of something so sinister. The events of 9/11 instantly changed perceptions of the wordsterrorist andn- work, especially in the United States. The international community was confronted with the need to tackle a threat which was not con?ned to a discreet physical - cation. This is a particular challenge to the standard instruments for projecting the legal authority of states and their power to uphold public safety. As demonstrated by the events of the 9/11 attack, we know that terrorist attacks can happen anywhere.

Autoren/Hrsg.

Memon, Nasrullah

Rosenorn, Torben

Farley, Jonathan David

Hicks, David L.

Inhaltsverzeichnis

1;Foreword;5
2;Contents;7
3;Mathematical Methods in Counterterrorism: Tools and Techniques for a New Challenge;14
3.1;1 Introduction;14
3.2;2 Organization;15
3.3;3 Conclusion and Acknowledgements;17
4;Network Analysis;19
4.1;Modeling Criminal Activity in Urban Landscapes;20
4.1.1;1 Introduction;20
4.1.2;2 Background and Motivation;22
4.1.3;3 Mastermind Framework;25
4.1.4;4 Mastermind: Modeling Criminal Activity;30
4.1.5;5 Concluding Remarks;39
4.1.6;References;40
4.2;Extracting Knowledge from Graph Data in Adversarial Settings;43
4.2.1;1 Characteristics of Adversarial Settings;43
4.2.2;2 Sources of Graph Data;44
4.2.3;3 Eigenvectors and the Global Structure of a Graph;45
4.2.4;4 Visualization;46
4.2.5;5 Computation of Node Properties;47
4.2.6;6 Embedding Graphs in Geometric Space;49
4.2.7;7 Summary;62
4.2.8;References;63
4.3;Mathematically Modeling Terrorist Cells: Examining the Strength of Structures of Small Sizes;65
4.3.1;1 Back to Basics : Recap of the Poset Model of Terrorist Cells;65
4.3.2;2 Examining the Strength of Terrorist Cell Structures – Questions Involved and Relevance to Counterterrorist Operations;67
4.3.3;3 Definition of Strength in Terms of the Poset Model;68
4.3.4;4 Posets Addressed;69
4.3.5;5 Algorithms Used;69
4.3.6;6 Structures of Posets of Size 7: Observations and Patterns;71
4.3.7;7 Implications and Applicability;75
4.3.8;8 Ideas for Future Research;76
4.3.9;9 Conclusion;77
4.3.10;Acknowledgments;77
4.3.11;References;77
4.4;Combining Qualitative and Quantitative Temporal Reasoning for Criminal Forensics*;78
4.4.1;1 Introduction;78
4.4.2;2 Temporal Knowledge Representation and Reasoning;80
4.4.3;3 Point-Interval Logic;81
4.4.4;4 Using Temper for Criminal Forensics – The London Bombing;91
4.4.5;5 Conclusion;97
4.4.6;Acknowledgements;98
4.4.7;References;98
4.5;Two Theoretical Research Questions Concerning the Structure of the Perfect Terrorist Cell;100
4.5.1;Appendix: Cutsets and Minimal Cutsets of All n-Member Posets(n = 5);103
4.5.2;References;111
5;Forecasting;113
5.1;Understanding Terrorist Organizations with a Dynamic Model;114
5.1.1;1 Introduction;114
5.1.2;2 A Mathematical Model;116
5.1.3;3 Analysis of the Model;118
5.1.4;4 Discussion;121
5.1.5;5 Counter-Terrorism Strategies;124
5.1.6;6 Conclusions;127
5.1.7;7 Appendix;128
5.1.8;References;131
5.2;Inference Approaches to Constructing Covert Social Network Topologies;133
5.2.1;1 Introduction;133
5.2.2;2 Network Analysis;134
5.2.3;3 A Bayesian Inference Approach;135
5.2.4;4 Case 1 Analysis;137
5.2.5;5 Case 2 Analysis;140
5.2.6;6 Conclusions;144
5.2.7;References;145
5.3;A Mathematical Analysis of Short-term Responses to Threats of Terrorism;147
5.3.1;1 Introduction;147
5.3.2;2 Information Model;151
5.3.3;3 Defensive Measures;154
5.3.4;4 Analysis;158
5.3.5;5 Illustrative numerical experiments;162
5.3.6;6 Summary;164
5.3.7;References;166
5.4;Network Detection Theory;167
5.4.1;1 Introduction;167
5.4.2;2 Random Intersection Graphs;171
5.4.3;3 Subgraph Count Variance;175
5.4.4;4 Dynamic Random Graphs;178
5.4.5;5 Tracking on Networks;179
5.4.6;6 Hierarchical Hypothesis Management;183
5.4.7;7 Conclusion;186
5.4.8;Acknowledgments;186
5.4.9;References;186
6;Communication/Interpretation;188
6.1;Security of Underground Resistance Movements;189
6.1.1;1 Introduction;189
6.1.2;2 Best defense against optimal subversive strategies;190
6.1.3;3 Best defense against random subversive strategies;194
6.1.4;4 Maximizing the size of surviving components;197
6.1.5;5 Ensuring that the survivor graph remains connected;200
6.1.6;References;207
6.2;Intelligence Constraints on Terrorist Network Plots;209
6.2.1;1 Introduction;209
6.2.2;2 Tipping Point in Conspiracy Size;210
6.2.3;3 Tipping Point Examples;213
6.2.4;4 Stopping Rule for Terrorist Attack Multiplicity;216
6.2.5;5 Preventing Spectacular Attacks;217
6.2.6;References;218
6.3;On Heterogeneous Covert Networks;219
6.3.1;1 Introduction;220
6.3.2;2 Preliminaries;221
6.3.3;3 Secrecy and Communication in Homogeneous Covert Networks;222
6.3.4;4 Jemaah Islamiya Bali bombing;224
6.3.5;5 A First Approach to Heterogeneity in Covert Networks;227
6.3.6;References;232
6.4;Two Models for Semi-Supervised Terrorist Group Detection;233
6.4.1;1 Introduction;233
6.4.2;2 Terrorist Group Detection from Crime and Demographics Data;234
6.4.3;3 Offender Group Representation Model (OGRM);239
6.4.4;4 Group Detection Model (GDM);240
6.4.5;5 Offender Group Detection Model (OGDM);241
6.4.6;6 Experiments and Evaluation;246
6.4.7;7 Conclusion;248
6.4.8;References;251
7;Behavior;254
7.1;CAPE: Automatically Predicting Changes in Group Behavior;255
7.1.1;1 Introduction;255
7.1.2;2 CAPE Architecture;257
7.1.3;3 SitCAST Predictions;258
7.1.4;4 CONVEX and SitCAST;260
7.1.5;5 The CAPE Algorithm;262
7.1.6;6 Experimental Results;268
7.1.7;7 Related Work;269
7.1.8;8 Conclusions;270
7.1.9;Acknowledgements;270
7.1.10;References;271
7.2;Interrogation Methods and Terror Networks;272
7.2.1;1 Introduction;272
7.2.2;2 Model;275
7.2.3;3 The Optimal Network;279
7.2.4;4 The Enforcement Agency;282
7.2.5;5 Extensions and Conclusions;286
7.2.6;Appendix;287
7.2.7;References;291
7.3;Terrorists and Sponsors. An Inquiry into Trust and Double- Crossing;292
7.3.1;1 State-Terrorist Coalitions;292
7.3.2;2 The Mathematical Model;296
7.3.3;3 Equilibrium Strategies;298
7.3.4;4 Payoff to T;301
7.3.5;5 The Trust Factor;303
7.3.6;6 Interpretation;304
7.3.7;7 Conclusion. External Shocks;309
7.3.8;References;309
7.4;Simulating Terrorist Cells: Experiments and Mathematical Theory;310
7.4.1;1 Introduction;310
7.4.2;2 The Question of Theory versus Real-Life Applications;311
7.4.3;3 Design;312
7.4.4;4 Procedure;313
7.4.5;5 Analysis and Conclusions;314
7.4.6;Appendix;317
7.4.7;References;317
8;Game Theory;318
8.1;A Brinkmanship Game Theory Model of Terrorism;319
8.1.1;1 Introduction;319
8.1.2;2 The Extensive Form of the Brinkmanship Game;322
8.1.3;3 Incentive Compatibility ( Credibility ) Constraints;325
8.1.4;4 Equilibrium Solution and Interpretation of the Results;328
8.1.5;5 Conclusion;330
8.1.6;References;332
8.2;Strategic Analysis of Terrorism;333
8.2.1;1 Introduction;333
8.2.2;2 Strategic Substitutes and Strategic Complements in the Study of Terrorism;335
8.2.3;3 Terrorist Signaling: Backlash and Erosion Effects;342
8.2.4;4 Concluding Remarks;347
8.2.5;References;347
8.3;Underfunding in Terrorist Organizations;349
8.3.1;1 Introduction;349
8.3.2;2 Motivation;353
8.3.3;3 Model;359
8.3.4;4 Results;361
8.3.5;5 Discussion;370
8.3.6;6 Conclusion;375
8.3.7;Mathematical Appendix;377
8.3.8;References;380
9;History of the Conference on Mathematical Methods in Counterterrorism;383
9.1;Personal Reflections on Beauty and Terror;384
9.1.1;1 Shadows Strike;384
9.1.2;2 The Thinking Man’s Game ;384
9.1.3;3 The Elephant: Politics;386
9.1.4;4 Toward a Mathematical Theory of Counterterrorism;388

Leseproben

CAPE: Automatically Predicting Changes in Group Behavior (p. 253-254)

Amy Sliva, V.S. Subrahmanian, Vanina Martinez, and Gerardo Simari

Abstract There is now intense interest in the problem of forecasting what a group will do in the future. Past work [1, 2, 3] has built complex models of a group’s behavior and used this to predict what the group might do in the future. However, almost all past work assumes that the group will not change its past behavior. Whether the group is a group of investors, or a political party, or a terror group, there is much interest in when and how the group will change its behavior. In this paper, we develop an architecture and algorithms called CAPE to forecast the conditions under which a group will change its behavior.We have tested CAPE on social science data about the behaviors of seven terrorist groups and show that CAPE is highly accurate in its predictions—at least in this limited setting.

1 Introduction

Group behavior is a continuously evolving phenomenon. The way in which a group of investors behaves is very different from the way a tribe in Afghanistan might behave, which in turn, might be very different from how a political party in Zimbabwe might behave. Most past work [1, 4, 2, 3, 5] on modeling group behaviors focuses on learning a model of the behavior of the group, and using that to predict what the group might do in the future. In contrast, in this paper, we develop algorithms to learn when a given group will change its behaviors.

As an example, we note that terrorist groups are constantly evolving. When a group establishes a standard operating procedure over an extended period of time, the problem of predicting what that group will do in a given situation (hypothetical or real) is easier than the problem of determining when, if, and how the group will exhibit a significant change in its behavior or standard operating procedure. Systems such as the CONVEX system [1] have developed highly accurate methods of determining what a given group will do in a given situation based on its past behaviors. However, their ability to predict when a group will change its behaviors is yet to be proven.

In this paper, we propose an architecture called CAPE that can be used to effectively predict when and how a terror group will change its behaviors. The CAPE methodology and algorithms have been tested out on about 10 years of real world data on 5 terror groups in two countries and—in those cases at least—have proven to be highly accurate.

The rest of this paper describes how this forecasting has been accomplished with the CAPE methodology. In Section 2, we describe the architecture of the CAPE system. Section3 gives details of an algorithm to estimate what the environmental variables will look like at a future point in time. In Section 4, we briefly describe an existing system called CONVEX [1] for predicting what a group will do in a given situation s and describe how to predict the actions that a group will take at a given time in the future.