Data mining is emerging as one of the key features of many homeland security
initiatives. Often used as a means for detecting fraud, assessing risk, and product
retailing, data mining involves the use of data analysis tools to discover previously
unknown, valid patterns and relationships in large data sets. In the context of
homeland security, data mining is often viewed as a potential means to identify
terrorist activities, such as money transfers and communications, and to identify and
track individual terrorists themselves, such as through travel and immigration
records.
The ability of terrorist networks to conduct
sophisticated and simultaneous attacks – the most recent
one on March 11, 2004 in Madrid, Spain – suggests that
there is a significant need for developing information
technology tools for counter-terrorism analysis. These
technologies could empower intelligence analysts to find
information faster, share, and collaborate across
agencies, "connect the dots" better, and conduct quicker
and better analyses. One such technology, the Adaptive
Safety Analysis and Monitoring (ASAM) system, is under
development at the University of Connecticut. In this
paper, the ASAM system is introduced and its capabilities
are discussed. The vulnerabilities at the Athens 2004
Olympics are modeled and patterns of anomalous
behavior are identified using a combination of featureaided
multiple target tracking, hidden Markov models
(HMMs), and Bayesian networks (BNs). Functionality of
the ASAM system is illustrated by way of application to
two hypothetical models of terrorist activities at the
Athens 2004 Olympics.*
Abstract: Better ways are needed to understand how terrorist groups become more effective and dangerous. Learning is the link between what a group wants to do and its ability to actually do it; therefore, a better understanding of group learning might contribute to the design of better measures for combating terrorism. This study analyzes current understanding of group learning and the factors that influence it and outlines a framework that should be useful in present analytical efforts and for identifying areas requiring further study
Better ways are needed to understand how terrorist groups increase their effectiveness and become more dangerous. Learning is the link between what a group wants to do and its ability to actually do it; therefore, a better understanding of group learning might contribute to the design of better measures for combating terrorism. This study analyzes current understanding of group learning and the factors that influence it. It presents detailed case studies of learning in five terrorist organizations and develops a methodology for ascertaining what and why groups have learned, providing insights into their learning processes.