: Nasrullah Memon, Jonathan David Farley, David L. Hicks, Torben Rosenorn
: Nasrullah Memon, Jonathan David Farley, David L. Hicks, Torben Rosenorn
: Mathematical Methods in Counterterrorism
: Springer-Verlag
: 9783211094426
: 1
: CHF 133.90
:
: Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
: English
: 389
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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.
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.
Foreword5
Contents7
Mathematical Methods in Counterterrorism: Tools and Techniques for a New Challenge14
1 Introduction14
2 Organization15
3 Conclusion and Acknowledgements17
Network Analysis19
Modeling Criminal Activity in Urban Landscapes20
1 Introduction20
2 Background and Motivation22
3 Mastermind Framework25
4 Mastermind: Modeling Criminal Activity30
5 Concluding Remarks39
References40
Extracting Knowledge from Graph Data in Adversarial Settings43
1 Characteristics of Adversarial Settings43
2 Sources of Graph Data44
3 Eigenvectors and the Global Structure of a Graph45
4 Visualization46
5 Computation of Node Properties47
6 Embedding Graphs in Geometric Space49
7 Summary62
References63
Mathematically Modeling Terrorist Cells: Examining the Strength of Structures of Small Sizes65
1 Back to Basics : Recap of the Poset Model of Terrorist Cells65
2 Examining the Strength of Terrorist Cell Structures – Questions Involved and Relevance to Counterterrorist Operations67
3 Definition of Strength in Terms of the Poset Model68
4 Posets Addressed69
5 Algorithms Used69
6 Structures of Posets of Size 7: Observations and Patterns71
7 Implications and Applicability75
8 Ideas for Future Research76
9 Conclusion77
Acknowledgments77
References77
Combining Qualitative and Quantitative Temporal Reasoning for Criminal Forensics*78
1 Introduction78
2 Temporal Knowledge Representation and Reasoning80
3 Point-Interval Logic81
4 Using Temper for Criminal Forensics – The London Bombing91
5 Conclusion97
Acknowledgements98
References98
Two Theoretical Research Questions Concerning the Structure of the Perfect Terrorist Cell100
Appendix: Cutsets and Minimal Cutsets of All n-Member Posets(n = 5)103
References111
Forecasting113
Understanding Terrorist Organizations with a Dynamic Model114
1 Introduction114
2 A Mathematical Model116
3 Analysis of the Model118
4 Discussion121
5 Counter-Terrorism Strategies124
6 Conclusions127
7 Appendix128
References131
Inference Approaches to Constructing Covert Social Network Topologies133
1 Introduction133
2 Network Analysis134
3 A Bayesian Inference Approach135
4 Case 1 Analysis137
5 Case 2 Analysis140
6 Conclusions144
References145
A Mathematical Analysis of Short-term Responses to Threats of Terrorism147
1 Introduction147
2 Information Model151
3 Defensive Measures154
4 Analysis158
5 Illustrative numerical experiments162
6 Summary164
References166
Network Detection Theory167
1 Introduction167
2 Random Intersection Graphs171
3 Subgraph Count Variance175
4 Dynamic Random Graphs178
5 Tracking on Networks179
6 Hierarchical Hypothesis Management183
7 Conclusion186
Acknowledgments186
References186
Communication/Interpretation188
Security of Underground Resistance Movements189
1 Introduction189
2 Best defense against optimal subversive strategies190
3 Best defense against random subversive strategies194
4 Maximizing the size of surviving components197
5 Ensuring that the survivor graph remains connected200
References207
Intelligence Constraints on Terrorist Network Plots209
1 Introduction209
2 Tipping Point in Conspiracy Size210
3 Tipping Point Examples213
4 Stopping Rule for Terrorist Attack Multiplicity216
5 Preventing Spectacular Attacks217
References218
On Heterogeneous Covert Networks219
1 Introduction220
2 Preliminaries221
3 Secrecy and Communication in Homogeneous Covert Networks222
4 Jemaah Islamiya Bali bombing224
5 A First Approach to Heterogeneity in Covert Networks227
References232
Two Models for Semi-Supervised Terrorist Group Detection233
1 Introduction233
2 Terrorist Group Detection from Crime and Demographics Data234
3 Offender Group Representation Model (OGRM)239
4 Group Detection Model (GDM)240
5 Offender Group Detection Model (OGDM)241
6 Experiments and Evaluation246
7 Conclusion248
References251