Memon / Farley / Hicks Mathematical Methods in Counterterrorism

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

E-Book, Englisch, 389 Seiten, eBook

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.

Zielgruppe

Research

Autoren/Hrsg.

Memon, Nasrullah

Rosenorn, Torben

Farley, Jonathan David

Hicks, David L.

Inhaltsverzeichnis

Mathematical Methods in Counterterrorism: Tools and Techniques for a New Challenge.- Mathematical Methods in Counterterrorism: Tools and Techniques for a New Challenge.- Network Analysis.- Modeling Criminal Activity in Urban Landscapes.- Extracting Knowledge from Graph Data in Adversarial Settings.- Mathematically Modeling Terrorist Cells: Examining the Strength of Structures of Small Sizes.- Combining Qualitative and Quantitative Temporal Reasoning for Criminal Forensics.- Two Theoretical Research Questions Concerning the Structure of the Perfect Terrorist Cell.- Forecasting.- Understanding Terrorist Organizations with a Dynamic Model.- Inference Approaches to Constructing Covert Social Network Topologies.- A Mathematical Analysis of Short-term Responses to Threats of Terrorism.- Network Detection Theory.- Communication/Interpretation.- Security of Underground Resistance Movements.- Intelligence Constraints on Terrorist Network Plots.- On Heterogeneous Covert Networks.- Two Models for Semi-Supervised Terrorist Group Detection.- Behavior.- CAPE: Automatically Predicting Changes in Group Behavior.- Interrogation Methods and Terror Networks.- Terrorists and Sponsors. An Inquiry into Trust and Double-Crossing.- Simulating Terrorist Cells: Experiments and Mathematical Theory.- Game Theory.- A Brinkmanship Game Theory Model of Terrorism.- Strategic Analysis of Terrorism.- Underfunding in Terrorist Organizations.- History of the Conference on Mathematical Methods in Counterterrorism.- Personal Reflections on Beauty and Terror.

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.