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2013 | Buch

The Global Terrorism Database, 1970–2010 Kapitel lesen Erstes Kapitel lesen

Handbook of Computational Approaches to Counterterrorism

herausgegeben von: V.S. Subrahmanian

Verlag: Springer New York

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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 book’s 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.

Inhaltsverzeichnis

Frontmatter

DATA AND DATA ACQUISITION

The Global Terrorism Database, 1970–2010
Abstract
Terrorism event databases provide systematized descriptive information about terrorist attacks from unclassified sources making the attack the unit of analysis. These databases generally follow the classic journalistic format of providing information on who is responsible for an attack, what happened, where it happened, when it happened and to the extent that it is known, how it happened. There have been a dozen or more major systematic efforts to build terrorism event databases over the past four decades. Because terrorism is a type of behavior that is difficult to study by police reports or victim or offender surveys, event databases have come to fill an important role. At the present moment, the longest running, most comprehensive of these data bases is the Global Terrorism Database (GTD) maintained by the START Consortium at the University of Maryland. Because most terrorists seek publicity, event databases that rely on print and electronic media are likely more useful for studying terrorism than most other types of crime. Nevertheless, event data have important weaknesses, most notably media inaccuracies; conflicting information or false, multiple or no claims of responsibility; and government censorship and disinformation. We use the GTD to describe the characteristics of world-wide terrorism from 1970 to 2010. We conclude with some observations about the future of terrorism event data bases.
Gary LaFree, Laura Dugan
Automated Coding of Political Event Data
Abstract
Political event data have long been used in the quantitative study of international politics, dating back to the early efforts of Edward Azar’s COPDAB [1] andCharles McClelland’s WEIS [18] as well as a variety of more specialized efforts such as Leng’s BCOW [16]. By the late 1980s, the NSF-funded Data Development in International Relations project [20] had identified event data as the second most common form of data—behind the various Correlates of War data sets— used in quantitative studies. The 1990s saw the development of two practical automated event data coding systems, the NSF-funded KEDS (http://eventdata. psu.edu; [9, 31, 33]) and the proprietary VRA-Reader (http://vranet.com; [15, 27]) and in the 2000s, the development of two new political event coding ontologies— CAMEO [34] and IDEA[4,27]—designed for implementation in automated coding systems. A summary of the current status of political event projects, as well as detailed discussions of some of these, can be found in [10, 32].
Philip A. Schrodt, David Van Brackle
Automatic Extraction of Events from Open Source Text for Predictive Forecasting
Abstract
Automated analysis of news reports is a significant empowering technology for predictive models of political instability. To date, the standard approach to this analytic task has been embodied in systems such as KEDS/TABARI [1], which use manually-generated rules and shallow parsing techniques to identify events and their participants in text. In this chapter we explore an alternative to event extraction based on BBN SERIFTM, and BBN OnTopicTM, two state-of-the-art statistical natural language processing engines. We empirically compare this new approach to existing event extraction techniques on five dimensions: (1) Accuracy: when an event is reported by the system, how often is it correct? (2) Coverage: how many events are correctly reported by the system? (3) Filtering of historical events: how well are historical events (e.g. 9/11) correctly filtered out of the current event data stream? (4) Topic-based event filtering: how well do systems filter out red herrings based on document topic, such as sports documents mentioning “clashes” between two countries on the playing field? (5) Domain shift: how well do event extraction models perform on data originating from diverse sources? In all dimensions we show significant improvement to the state-of-the-art by applying statistical natural language processing techniques. It is our hope that these results will lead to greater acceptance of automated coding by creators and consumers of social science models that depend on event data and provide a new way to improve the accuracy of those predictive models.
Elizabeth Boschee, Premkumar Natarajan, Ralph Weischedel
Automated Coding of Decision Support Variables
Abstract
With the enormous amount of textual information now available online, there is an increasing demand – especially in the national security community – for tools capable of automatically extracting certain types of information from massive amounts of raw data. In the last several years, ad-hoc Information Extraction (IE) systems have been developed to help address this need [6]. However, there are applications where the types of questions that need to be answered are far more complex than those that traditional IE systems can handle, and require to integrate information from several sources. For instance, political scientists need to monitor political organizations and conflicts, while defense and security analysts need to monitor terrorist groups. Typically, political scientists and analysts define a long list of variables – referred to as “codebook” – that they want to monitor over time for a number of groups. Currently, in most such efforts, the task of finding the right value for each variable – denoted as “coding” – is performed manually by human coders, and is extremely time consuming. Thus, the need for automation is enormous.
Massimiliano Albanese, Marat Fayzullin, Jana Shakarian, V. S. Subrahmanian

BEHAVIORAL MODELS AND FORECASTING

Qualitative Analysis & Computational Techniques for the Counter-Terror Analyst
Abstract
The combination of traditional qualitative analysis techniques with new developments in computer science has created important new options for counter-terror analysts. Written from this perspective of a counter-terror analyst, this chapter provides an overview of a number of useful modes of analysis for understanding terrorist organization behavior and developing strategies and policies that can counter terrorist group activities. The chapter discusses the strategic perspective on terrorist group decision-making, the application of organizational theory to terrorist organizations, as well as studies about who joins, and who leaves, terrorist organizations. In each of these areas of study, computational analysis can play an important role augmenting the human analyst’s ability to use data and generate effective counter-terror policies.
Aaron Mannes
SOMA: Stochastic Opponent Modeling Agents for Forecasting Violent Behavior
Abstract
The modern global political environment is growing increasingly complex, characterized by webs of interdependency, interaction, and conflict that are difficult to untangle. Technological expansion has led to an explosion in the information available, as well as the need for more sophisticated analysis methods. In this security and information environment, behaviors in the domain of counterterrorism and conflict can be understood as the confluence of many dynamic factors—cultural, economic, social, political, and historical—in an extremely complex system. Behavioral models and forecasts can be leveraged to manage the analytic complexity of these situations, providing intelligence analysts and policy-makers with decision support for developing security strategies. In this chapter, we develop the Stochastic Opponent Modeling Agents (SOMA) framework as a stochastic model of terror group behavior, presenting several scalable forecasting algorithms and a methodology for creating behavioral models from relational data.
Amy Sliva, Gerardo Simari, Vanina Martinez, V. S. Subrahmanian
Data-based Computational Approaches to Forecasting Political Violence
Abstract
The challenge of terrorism dates back centuries if not millennia. Until recently, the basic approaches to analyzing terrorism—historical analogy and monitoring the contemporary words and deeds of potential perpetrators—have changed little: the Roman authorities warily observing the Zealots in first-century Jerusalem could have easily traded places with the Roman authorities combatting the Red Brigades in twentieth century Italy.
Philip A. Schrodt, James Yonamine, Benjamin E. Bagozzi
Using Hidden Markov Models to Predict Terror Before it Hits (Again)
Abstract
This chapter presents auto-coded events data used for monthly predictions of force in Iraq and Afghanistan using Hidden Markov Models (HMMs). They were estimated and trained by the Baum-Welch and the Viterbi algorithms. Predictions were made using the HMMs in their generative capacity as opposed to past, static pattern matching. Along with our novel generation procedure, this chapter illustrates a set of empirically derived guidelines aimed at improving the accuracy of prediction as well as their practicality by using root mean squared forecast error tests. The cases of Iraq and Afghanistan were chosen with data through January 31 2009 not only because they are high on the decision maker agendas, but also because the on-going conflicts.
Vladimir B. Petroff, Joe H. Bond, Doug H. Bond, Doug H. Bond
Forecasting Group-Level Actions Using Similarity Measures
Abstract
In real-world settings, and in particular in counterterrorism efforts, there is a constant need for a given reasoning agent to have the means by which to “stay ahead” of certain other agents, such as organizations or individuals who may carry out actions against its interests. In this work, we focus on one such way in which a reasoning agent can do this: forecasting group-level actions. This ability is indeed a useful one to have, and one that is definitely attainable if the right kind of data is available. For example, consider the Minorities at Risk Organizational Behavior (MAROB) dataset [4, 19]. This data tracks the behavior for 118 ethnopolitical organizations in the Middle East and Asia Pacific on a yearly basis from 1980 to 2004. For each year, values have been gathered for about 175 measurable variables for each group in the sample. These variables include strategic conditions such as the tendency to commit bombings and armed attacks, as well as background information about the type of leadership, whether the group is involved in cross border violence, etc. Only a subset—around 43—of the approximately 175 attributes in the data represent strategic actions taken by the group, while the others represent variables relating to the environment or context in which the group functions. This context includes variables about the degree of military and financial support the group gets from foreign nations or the ethnic diaspora, the degree of state government repression and persecution against the group, and so forth. It also includes variables about the structure of the group and how factionalized it may or may not be, the level of violence and protests in which the group engages, and the amount of participation in the political process.
Gerardo I. Simari, Damon Earp, Maria Vanina Martinez, Amy Sliva, V. S. Subrahmanian
Forecasting the Use of Violence by Ethno–Political Organizations: Middle Eastern Minorities and the Choice of Violence
Abstract
Can analytic models, informed by social scientific theories using computational engineering approaches, offer effective forecasting of violent behavior? This chapter discusses a new data set which codes the structure and behavior of ethno-political organizations and the use of a new approach for forecasting political behavior drawn from computer engineering. In the chapter, we build a forecasting model and then test the model against existing data as well as a predictive analysis for the year 2009 (the analysis was done in 2008 and data for 2005–2009 has not yet been collected for this data set). The data used was drawn from the Minorities at Risk Organizational Behavior (MAROB) data set. MAROB was created through collaboration between the National Consortium for the Study of Terrorism and Responses to Terrorism and the Minorities at Risk (MAR) Project. This data focuses on ethno-political organizations in the Middle East to test factors that make it more or less likely that an organization will choose to use violence. While the variables on which data was collected were informed by theories of contentious politics, this chapter focuses primarily on the data itself and the forecasting approach that we used and less on the social science theoretical models as such. Analytically we use multiple approaches for data massaging, classification and forecasting to achieve high classification accuracies (measured in terms of overall accuracy, recall, precision, false positives, and F-measure). We also strive for parsimony in the number of variables we use to make our forecasting predictions.
Kihoon Choi, Victor Asal, Jonathan Wilkenfeld, Krishna R. Pattipati
Forecasting Changes in Terror Group Behavior
Abstract
The ability to model, forecast, and analyze the behaviors of other agents has applications in many diverse contexts. For example, behavioral models can be used in multi-player games to forecast an opponent’s next move, in economics to forecast a merger decision by a CEO, or in international politics to predict the behavior of a rival state or group. Such models can facilitate formulation of effective mitigating responses and provide a foundation for decision-support technologies.
Maria Vanina Martinez, Amy Sliva, Gerardo I. Simari, V. S. Subrahmanian
Using Temporal Probabilistic Rules to Learn Group Behavior
Abstract
The ability to reason about the past, present, or future state of the world is widely applicable to many fields. Additionally, considering uncertainty over the precise time at which events occurred or will occur increases realism, but also increases theoretical and computational intractability. This sort of probabilistic temporal reasoning is important in domains like those listed below.
John P. Dickerson, Gerardo I. Simari, V. S. Subrahmanian

TERRORIST NETWORK ANALYSIS

Leaderless Covert Networks: A Quantitative Approach
Abstract
It is hypothesized that many of the current covert organizations organize according to leaderless principles, see [15]. According to [4] international law enforcement pressure is forcing criminal and terrorist organizations to decentralize their organizational structures, e.g., Mexican law enforcement efforts are causing drug cartels in Mexico to break into smaller units. It is also known that terror organizations exist that are a mix of hierarchical and decentralized structures, i.e., think of Hezbollah and Peru former’s Shining Path (cf. [4]). Clearly this is done to frustrate intelligence agencies that try to disrupt such organizations by taking out key leaders. If the networks are flat rather than hierarchical it becomes very difficult to determine who the leaders are based on network principles alone. The study of covert networks has received high levels of attention from the modeling community in the last decade. Among others, covert networks have been formally characterized by Tsvetovat and Carley[18], McAllister [13] and McCormick and Owen [14], and their optimal network structures have been analyzed and approximated by Lindelauf et al.[9] and Enders and Su [5]. Other approaches concern covert network destabilization strategies, see [6] and [3], and tools to identify the most important members of the corresponding organizations, see [8, 12] and [17].
Bart Husslage, Roy Lindelauf, Herbert Hamers
Link Prediction in Highly Fractional Data Sets
Abstract
In recent years, online social networks have grown in scale and variability and offer individuals with similar interests the possibility of exchanging ideas and networking. On the one hand, social networks create new opportunities to develop friendships, share ideas, and conduct business. On the other hand, they are also an effective media tool for plotting crime and organizing extremists groups around the world. Online social networks, such as Facebook, Google+, and Twitter are hard to track due to their massive scale and increased awareness of privacy. Criminals and terrorists strive to hide their relationships, especially those that can associate them with a executed terror act.
Michael Fire, Rami Puzis, Yuval Elovici
Data Analysis Based Construction and Evolution of Terrorist and Criminal Networks
Abstract
The wide-spread usage of network and graph based approaches in modeling data has been approved to be effective for various applications. The network based framework becomes more powerful when it is expanded to benefit from the widely available techniques for data mining and machine learning which allow for effective knowledge discovery from the investigated domain. The underlying reason for the substantial efficacy in studying graphs, either directly (i.e., data is given in graph format, for example, the “phone-call” network in studying social evolutions) or indirectly (network is inferred from data by predefined method or scheme, such as co-occurrence network for studying genetic behaviors), is the fact that graph structures emphasize the intrinsic relationship between entities, i.e., nodes (or vertices) in the network (in this chapter, the terms network and graph are used interchangeably). For the indirect case information extraction techniques may be adapted to investigate open sources of data in order to derive the required network structure as reflected in the current available data. This is a tedious process but effective and could lead to more realistic and up-to-date information reflected in the network. The latter network will lead to better and close to real-time knowledge discovery in case online information extraction is affordable and provided. Estimating network structure has attracted the attention of other researchers involved in terrorist network analysis, e.g.[9].
Khaled Dawoud, Tamer N. Jarada, Wadhah Almansoori, Alan Chen, Shang Gao, Reda Alhajj, Jon Rokne
CrimeFighter Investigator: Criminal Network Sense-Making
Abstract
Criminal network investigation involves a number of complex knowledge management tasks such as collection, processing, and analysis of information. Synthesis and sense-making are core analysis tasks; analysts move pieces of information around, they stop to look for patterns that can help them relate the information pieces, they add new pieces of information and iteration after iteration the information becomes increasingly structured and valuable. Synthesizing emerging and evolving information structures is a creative and cognitive process best performed by humans. Making sense of synthesized information structures (i.e., searching for patterns) is a more logic-based process where computers outperform humans as information volume and complexity increases. CrimeFighter Investigator is a novel tool that supports sense-making tasks through the application of advanced software technologies such as hypertext structure domains, semantic web concepts, known human-computer interaction metaphors, and a tailorable computational model rooted in a conceptual model defining first class entities that enable separation of structural and mathematical models.
Rasmus Rosenqvist Petersen, Uffe Kock Wiil

SYSTEMS, FRAMEWORKS, and CASE STUDIES

The NOEM: A Tool for Understanding/ Exploring the Complexities of Today’s Operational Environment
Abstract
Recent events have shown that today’s and future wars are/will be much different than those fought in the previous decades. Destroying the will of the people will no longer be the primary goal in such endeavors. Success will be decided by our ability to understand the local populace, provide human security, and eventually enable the country/region to govern itself and maintain security. To accomplish this, we need to understand the environment in which we are to operate in—including the local populace. The National Operational Environment Model (NOEM) is a large-scale stochastic model representing the environment of a nation-state or region along with a set of capabilities which allow one to exercise the model. The NOEM enables a user to identify potential problem regions within the environment, test a wide variety of policy options on a national or regional basis, determine suitable courses of actions given a specified set of initial conditions, and investigate resource allocation levels that will best improve overall country or regional stability. The different policy options or actions can be simulated, revealing potential unforeseen effects and general trends over time. In this chapter we explore previous work in the area of stability management, provide an overview of the NOEM, and discuss two different use cases.
John J. Salerno, Jason E. Smith, Warren M. Geiler, Patrick K. McCabe, Aleksey V. Panasyuk, Walter D. Bennette, Adam Kwiat
A Multi-Method Approach for Near Real Time Conflict and Crisis Early Warning
Abstract
This chapter describes a system that was developed by the Department of Defense to monitor, assess and forecast a variety of de-stabilizing events throughout the world, including insurgencies, rebellions, international crises, domestic political crises, and ethnic/religious violence. The chapter describes each of the key components of the system, and the lessons the author learned as he integrated basic research and transformed it into an operationally useful system for crisis early warning.
Sean P. O’Brien
A Realistic Framework for Counter-terrorism in Multimedia
Abstract
The internet has become an easy platform for video broadcasting by providing an inexpensive but weak publishing barrier to everyone and eventually attracting the huge audience. Terrorists make use of videos as an efficient medium for spreading their message; showing violence to attract sympathies, terrify viewers and promoting radicalization; videos act as an important weapon towards their mission. Existing internet security solutions and blocking methods rely on textual data and lack analysis of visual content. In this paper, we propose a novel framework that can realistically automate the screening of such videos using visual content analysis and consequently alert the authorities. After examining existing discrete definitions of violence, we classify the broad spectrum of violence in videos into four streams which serve as the core functions of the proposed system. The system is capable of evaluating the semantic context as well as the extent of violence in the videos. Being fully automatic and comprehensive, the system works efficiently and more effectively in comparison to any existing systems proving itself as a powerful combating tool in controlling the means and effects of this mode of terrorism.
Durat-ul-Ain Mirza, Nasrullah Memon
PROTECT in the Ports of Boston, New York and Beyond: Experiences in Deploying Stackelberg Security Games with Quantal Response
Abstract
The global need for security of key infrastructure with limited resources has led to significant interest in research conducted in multiagent systems towards game-theory for real-world security. As reported previously at AAMAS, three applications based on Stackelberg games have been transitioned to real-world deployment. This includes ARMOR, used by the Los Angeles International Airport to randomize checkpoints of roadways and canine patrols [16]; IRIS, which helps the US Federal Air Marshal Service [22] in scheduling air marshals on international flights; and GUARDS [17], which is under evaluation by the US Transportation Security Administration to allocate resources for airport protection. We as a community remain in the early stages of these deployments, and must continue to develop our understanding of core principles of innovative applications of game theory for security.
Eric Shieh, Bo An, Rong Yang, Milind Tambe, Craig Baldwin, Joseph DiRenzo, Ben Maule, Garrett Meyer, Kathryn Moretti
Government Actions in Terror Environments (GATE): A Methodology that Reveals how Governments Behave toward Terrorists and their Constituencies
Abstract
With the persistent alarm being raised about terrorist violence by the media and government officials it is unsurprising that scholarship in this area has grown well beyond its traditional disciplinary boundaries (i.e., political science and international relations). As scholars from disciplines such as criminology [27, 30], computer science [11, 12, 35], economics [25], and others get more involved, more data sources have become available [1, 19, 28, 50] and more sophisticated analytical methods have been applied to terrorism research [14, 17, 30]. Yet, research on the effectiveness of counterterrorism measures has only incrementally improved in recent years [33].
Laura Dugan, Erica Chenoweth

NEW DIRECTIONS

A CAST Case-Study: Assessing Risk in the Niger Delta
Abstract
Prohibitively expensive, both in terms of human and financial cost, robust conflict assessment and early warning has traditionally been the purview of governments and large international organizations. But now, with rapid advances in information technology and computational methods, this work has become more accessible, allowing smaller nongovernmental organizations and civil society to participate. The Conflict Assessment System Tool (CAST) is an approach that takes advantage of these developments. This chapter outlines the way in which CAST ensures the incorporation of sound theory, good data, and expert analysis, and illustrate its use for the assessment of risk in the Niger Delta region of Nigeria, specifically the states of Delta, Bayelsa, Rivers, Akwa Ibom, and Abia in the year 2011, where there was significant levels of militancy, criminality, and political violence. This case study highlights the challenges and opportunities of early warning in an interconnected world.
Nate Haken, Patricia Taft, Raphaël Jaeger
Policy Analytics Generation Using Action Probabilistic Logic Programs
Abstract
Action probabilistic logic programs (ap-programs for short) [15] are a class of the extensively studied family of probabilistic logic programs [14, 21, 22].
Gerardo I. Simari, John P. Dickerson, Amy Sliva, V. S. Subrahmanian
The Application of Search Games to Counter Terrorism Studies
Abstract
The arrest of Saddam Hussein on 13th of December 2003 in a farmhouse near his hometown of Tikrit marked the end of military operation Red Dawn, a man hunt that had been planned by Major Brian J. Reed, who traced Saddam using social network analysis. Major Reed stated that: the intelligence background and link diagrams that we built were rooted in the concepts of network analysis [24]. The process of daily intelligence gathering led coalition forces to identify and locate more of the key players in the insurgent network [29]. This finally resulted in diagrams of Saddam’s highly trusted relatives and clan members. A series of raids designed to capture some of those key individuals finally led to the information necessary to find Hussein. Operation Red Dawn took approximately half a year.
Robbert Fokkink, Roy Lindelauf
Temporal and Spatial Analyses for Large-Scale Cyber Attacks
Abstract
Prevalent computing devices with networking capabilities have become critical cyber infrastructure for government, industry, academia and every-day life. As their value rises, the motivation driving cyber attacks on this infrastructure has shifted from the pursuit of notoriety to the pursuit of profit [1, 2] or political gains, leading to cyber terrorism on various scales. Cyber terrorism has had its share of case studies and definitions since late 1990s and early 2000s [3–5]. A common denominator of the definition of cyber terrorism is the threat posed through the use of cyber infrastructure, especially the Internet. Stuxnet, a malware discovered in June 2010, which was a directed attack against the Iranian nuclear program [6], represented a milestone on cyber warfare and posed a new challenge to analyze and understand cyber attacks due to its complexity in attack strategy. While cyber terrorism can have many elements beyond exploiting cyber vulnerabilities, this chapter focuses on analyzing techniques that process observables of malicious activities in the cyberspace.
Haitao Du, Shanchieh Jay Yang
Metadaten
Titel
Handbook of Computational Approaches to Counterterrorism
herausgegeben von
V.S. Subrahmanian
Copyright-Jahr
2013
Verlag
Springer New York
Electronic ISBN
978-1-4614-5311-6
Print ISBN
978-1-4614-5310-9
DOI
https://doi.org/10.1007/978-1-4614-5311-6

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