Collaborative Tools for Counter-Terrorism Analysis

Popp, Robert L.; Pattipati, Krishna R.; Willett, Peter; Serfaty, Daniel; Stacy, Webb; Carley, Kathleen M.; Allanach, J.; Tu, Haiying; Singh, Satnam

doi:10.1109/aero.2005.1559623

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Much of the reported research in the AT domain has focused on the design and development of information retrieval and aggregation tools for analysis of large data sets [3], tools for modeling terrorist attacks [4], collaborative tools for counterterrorism [5], cyber security [6], and managing privacy [7]. Significantly less evident in the literature are studies describing the design and evaluation of systems designed to support anti-terrorism activity, such as planning, decision-making, and emergency response, and what can be done to make these systems more effective and efficient.…”

Section: Anti-terrorism Planning and Resource Allocationmentioning

confidence: 99%

Rampart: A Service and Agent-Based Architecture for Anti-Terrorism Planning and Resource Allocation

Haynes

Kannampallil

Cohen

et al. 2008

Intelligence and Security Informatics

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Abstract. This paper describes a novel architecture for anti-terrorism planning and resource allocation systems. The architecture uses web services and agents to provide a re-usable and extensible architecture for these types of systems. A dynamic web-based client and custom middle ware provide for the integration of otherwise de-coupled software components. The architecture has evolved from a longitudinal field study of U.S. Marine Corps anti-terrorism planners through recent empirical work in a more controlled setting. We describe this evolution, the architecture and its rationale, and provide some lessons learned for others engaged in development of anti-terrorism systems.

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Section: Anti-terrorism Planning and Resource Allocationmentioning

confidence: 99%

Rampart: A Service and Agent-Based Architecture for Anti-Terrorism Planning and Resource Allocation

Haynes

Kannampallil

Cohen

et al. 2008

Intelligence and Security Informatics

View full text Add to dashboard Cite

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Modeling and detection techniques for Counter-Terror Social Network Analysis and Intent Recognition

Weinstein

Campbell

Delaney

et al. 2009

2009 IEEE Aerospace Conference

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In this paper, we describe our approach and initial results on modeling, detection, and tracking of terrorist groups and their intents based on multimedia data. While research on automated information extraction from multimedia data has yielded significant progress in areas such as the extraction of entities, links, and events, less progress has been made in the development of automated tools for analyzing the results of information extraction to "connect the dots." Hence, our Counter-Terror Social Network Analysis and Intent Recognition (CT-SNAIR) work focuses on development of automated techniques and tools for detection and tracking of dynamically-changing terrorist networks as well as recognition of capability and potential intent. In addition to obtaining and working with real data for algorithm development and test, we have a major focus on modeling and simulation of terrorist attacks based on real information about past attacks. We describe the development and application of a new Terror Attack Description Language (TADL), which is used as a basis for modeling and simulation of terrorist attacks. Examples are shown which illustrate the use of TADL and a companion simulator based on a Hidden Markov Model (HMM) structure to generate transactions for attack scenarios drawn from real events. We also describe our techniques for generating realistic background clutter traffic to enable experiments to estimate performance in the presence of a mix of data. An important part of our effort is to produce scenarios and corpora for use in our own research, which can be shared with a community of researchers in this area. We describe our scenario and corpus development, including specific examples from the September 2004 bombing of the Australian embassy in Jakarta and a fictitious scenario which was developed in a prior project for research in social network analysis. The scenarios can be created by subject matter experts using a graphical editing tool. Given a set of time ordered transactions between actors, we employ social network analysis (SNA) algorithms as a filtering step to divide the actors into distinct communities before determining intent. This helps reduce clutter and enhances the ability to determine activities within a specific group. For modeling and simulation purposes, we generate random networks with structures and properties similar to real-world social networks. Modeling of background traffic is an important step in generating classifiers that can separate harmless activities from suspicious activity. An algorithm for recognition of simulated potential attack scenarios in clutter based on Support Vector Machine (SVM) techniques is presented. We show performance examples, including probability of detection versus probability of false alarm tradeoffs, for a range of system parameters.

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Collaborative Tools for Counter-Terrorism Analysis

Cited by 2 publications

References 6 publications

Rampart: A Service and Agent-Based Architecture for Anti-Terrorism Planning and Resource Allocation

Rampart: A Service and Agent-Based Architecture for Anti-Terrorism Planning and Resource Allocation

Modeling and detection techniques for Counter-Terror Social Network Analysis and Intent Recognition

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