收稿日期: 2014-03-31
修回日期: 2014-05-07
网络出版日期: 2015-03-15
基金资助
国家重点基础研究发展计划(2011CB706900)、国家自然科学基金(70971128)和北京市自然科学基金(9102022)资助
Terrorism threat assessment with multi-module Bayesian network
Received date: 2014-03-31
Revised date: 2014-05-07
Online published: 2015-03-15
通过考察恐怖袭击事件可能造成的后果, 针对恐怖袭击事件评估信息的多样性、不确定性及模糊性, 提出用贝叶斯网络方法对恐怖袭击威胁进行评估, 从而为反恐决策者提供决策支持, 以减少恐怖袭击所造成的影响.针对恐怖袭击事件的复杂性, 提出多模块贝叶斯网络的恐怖袭击威胁评估模型, 并对多模块贝叶斯网络的结构学习、参数学习和推理进行研究, 提出多模块贝叶斯网络的推理算法.最后, 给出恐怖袭击威胁度的计算方法, 并对多模块贝叶斯网络的威胁评估模型进行实例分析.实例表明, 基于多模块贝叶斯网络的恐怖袭击威胁评估模型, 能有效评估恐怖袭击事件的威胁程度.
魏静 , 王菊韵 , 于华 . 基于多模块贝叶斯网络的恐怖袭击威胁评估[J]. 中国科学院大学学报, 2015 , 32(2) : 264 -272 . DOI: 10.7523/j.issn.2095-6134.2015.02.017
This study intends to provide decision support for counter-terrorism according to the threat of terrorist attacks. Because of the diversity, uncertainty, and ambiguity of assessment information about terrorist attacks, Bayesian network is proposed to assess threat from the consequence of attacks. This study presents a multi-module Bayesian network threat assessment model for the complexity of the terrorist attacks, and this model combines the qualitative and quantitative assessment. We study the multi-module Bayesian network structure learning, parameter learning, and inference. Finally we compute the terrorism threat degree and conduct instance analysis. Simulation results show that this model effectively assesses the real threat degree of terrorist attacks.
Key words： terrorist attacks; threat assessment; multi-module; Bayesian network; inference
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