terrorism

Abstract: We view the task of sensemaking in intelligence as that of abducing a story whose plot explains the current data and makes verifiable predictions about the future and the past. We have developed a computational system, called STAB, that abduces stories from data. The story plots in STAB are represented as processes with goals and states, and organized in an abstraction hierarchy. STAB analyzes the VAST dataset generated by PNNL. This dataset pertains to normal and typical activities, as well as illegal and unethical activities, in a fictitious town in the United States. Given the VAST data incrementally, STAB retrieves and invokes multiple story plots as explanatory hypotheses and generates expectations about future data. It uses supporting and contradicting evidence to build justifications for its final conclusions.

 analysis;  terrorism;  knowledge-discovery;  intelligence;  narrative;  

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Abstract: A method is presented for detecting changes to the distribution of a criminal or terrorist point process between two time periods using a non-model-based approach. By treating the criminal/terrorist point process as an intelligent site selection problem, changes to the process can signify changes in the behavior or activity level of the criminals/terrorists. The locations of past events and an associated vector of geographic, environmental, and socio-economic feature values are employed in the analysis. By modeling the locations of events in each time period as a marked point process, we can then detect differences in the intensity of each component process. A modified PRIM (patient rule induction method) is implemented to partition the high-dimensional feature space, which can include mixed variables, into the most likely change regions. Monte Carlo simulations are easily and quickly generated under random relabeling to test a scan statistic for significance. By detecting local regions of change, not only can it be determined if change has occurred in the study area, but the specific spatial regions where change occurs is also identified. An example is provided of breaking and entering crimes over two-time periods to demonstrate the use of this technique for detecting local regions of change. This methodology also applies to detecting regions of differences between two types of events such as in case-control data.

 algorithm;  terrorism;  clustering;  automated;  

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Abstract: Understanding networks of connections among individuals is an important element of counterterrorism analysis. Determining nodes and links for such networks is one of the most labor-intensive aspects of counterterrorism analysis. This paper presents an automated approach for generating and displaying an initial estimate of nodes and links relevant to a chosen topic. This work combines the use of entity extraction and latent semantic indexing (LSI).

 analysis;  terrorism;  semantic;  information-retrieval;  graphs;  

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Abstract: Sorry no abstract available for this article

 network analysis;  terrorism;  

Abstract: Sorry no abstract available for this article

 network analysis;  terrorism;