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首页> 外文会议>Sensors, and command, control, communications, and intelligence (C3I) technologies for homeland security and homeland defense IX >A Bayesian Belief Network of Threat Anticipation and Terrorist Motivations
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A Bayesian Belief Network of Threat Anticipation and Terrorist Motivations

机译：威胁预期和恐怖主义动机的贝叶斯信念网络

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摘要

Recent events highlight the need for efficient tools for anticipating the threat posed by terrorists, whether individual or groups. Antiterrorism includes fostering awareness of potential threats, deterring aggressors, developing security measures, planning for future events, halting an event in process, and ultimately mitigating and managing the consequences of an event. To analyze such components, one must understand various aspects of threat elements like physical assets and their economic and social impacts. To this aim, we developed a three-layer Bayesian belief network (BBN) model that takes into consideration the relative threat of an attack against a particular asset (physical layer) as well as the individual psychology and motivations that would induce a person to either act alone or join a terrorist group and commit terrorist acts (social and economic layers). After researching the many possible motivations to become a terrorist, the main factors are compiled and sorted into categories such as initial and personal indicators, exclusion factors, and predictive behaviors. Assessing such threats requires combining information from disparate data sources most of which involve uncertainties. BBN combines these data in a coherent, analytically defensible, and understandable manner. The developed BBN model takes into consideration the likelihood and consequence of a threat in order to draw inferences about the risk of a terrorist attack so that mitigation efforts can be optimally deployed. The model is constructed using a network engineering process that treats the probability distributions of all the BBN nodes within the broader context of the system development process.
机译：最近发生的事件突出表明，需要有效的工具来预测恐怖分子（无论是个人还是群体）构成的威胁。反恐怖主义包括提高对潜在威胁的认识，威慑侵略者，制定安全措施，为将来的事件进行计划，中止正在发生的事件以及最终减轻和管理事件的后果。要分析这些组成部分，必须了解威胁要素的各个方面，例如实物资产及其经济和社会影响。为此，我们开发了一个三层的贝叶斯信念网络（BBN）模型，该模型考虑了针对特定资产（物理层）的攻击的相对威胁以及个人的心理和动机，这些动机和动机会诱使人们选择独自行动或加入恐怖组织并实施恐怖行为（社会和经济层面）。在研究了成为恐怖分子的多种可能动机之后，主要因素被汇总并归类为诸如初始和个人指标，排斥因素和预测行为等类别。评估此类威胁需要组合来自不同数据源的信息，其中大多数数据都涉及不确定性。 BBN以连贯，分析可靠且易于理解的方式结合了这些数据。发达的BBN模型考虑了威胁的可能性和后果，以便推断出恐怖袭击的风险，从而可以最佳地部署缓解措施。该模型是使用网络工程过程构建的，该过程在系统开发过程的更广泛上下文中处理所有BBN节点的概率分布。

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