Counter-Terrorism Threat Prediction Architecture

摘要

This paper will evaluate the feasibility of constructing a system to support intelligence analysts engaged in counter-terrorism. It will discuss the use of emerging techniques to evaluate a large-scale threat data repository (or Infosphere) and comparing analyst developed models to identify and discover potential threat-related activity with a uncertainty metric used to evaluate the threat. This system will also employ the use of psychological (or intent) modeling to incorporate combatant (i.e. terrorist) beliefs and intent. The paper will explore the feasibility of constructing a hetero-hierarchical (a hierarchy of more than one kind or type characterized by loose connection/feedback among elements of the hierarchy) agent based framework or "family of agents" to support "evidence retrieval" defined as combing, or searching the threat data repository and returning information with an uncertainty metric. The counter-terrorism threat prediction architecture will be guided by a series of models, constructed to represent threat operational objectives, potential targets, or terrorist objectives. The approach would compare model representations against information retrieved by the agent family to isolate or identify patterns that match within reasonable measures of proximity. The central areas of discussion will be the construction of an agent framework to search the available threat related information repository, evaluation of results against models that will represent the cultural foundations, mindset, sociology and emotional drive of typical threat combatants (i.e. the mind and objectives of a terrorist), and the development of evaluation techniques to compare result sets with the models representing threat behavior and threat targets. The applicability of concepts surrounding Modeling Field Theory (MFT) will be discussed as the basis of this research into development of proximity measures between the models and result sets and to provide feedback in support of model adaptation (learning). The increasingly complex demands facing analysts evaluating activity threatening to the security of the United States make the family of agent-based data collection (fusion) a promising area. This paper will discuss a system to support the collection and evaluation of potential threat activity as well as an approach fro presentation of the information.