Journal of University of Chinese Academy of Sciences >
Terrorism threat assessment with multi-module Bayesian network
Received date: 2014-03-31
Revised date: 2014-05-07
Online published: 2015-03-15
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
WEI Jing , WANG Juyun , YU Hua . Terrorism threat assessment with multi-module Bayesian network[J]. Journal of University of Chinese Academy of Sciences, 2015 , 32(2) : 264 -272 . DOI: 10.7523/j.issn.2095-6134.2015.02.017
[1] 李健和,王存奎,梅建明,等. 当代恐怖主义的特征与发展趋势[J]. 中国人民公安大学学报: 社会科学版, 2008(3):1-7.
[2] Geng R, Xu G. Application of AHP FSE method in the network course quality evaluation[J]. Procedia Engineering, 2011, 15: 4 136-4 141.
[3] Li H, Chen Z, Chen S. The study of evaluation of the quality of the accounting information based on the fuzzy-AHP model[J]. Advances in Services Science and Services Information Technology (Set), 2014, 52: 185.
[4] Pradhan B. An assessment of the use of an advanced neural network model with five different training strategies for the preparation of landslide susceptibility maps[J]. Journal of Data Science, 2011, 9(1): 65-81.
[5] 沈阳武,彭晓涛,施通勤,等. 基于最优组合权重的电能质量灰色综合评价方法[J]. 电力系统自动化, 2012,36(10): 67-73.
[6] Baesens B, Verstraeten G, Van den Poel D, et al. Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers[J]. European Journal of Operational Research, 2004, 156(2): 508-523.
[7] Liu K F R, Lu C F, Chen C W, et al. Applying Bayesian belief networks to health risk assessment[J]. Stochastic Environmental Research and Risk Assessment, 2012, 26(3): 451-465.
[8] Weber P, Medina-Oliva G, Simon C, et al. Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas[J]. Engineering Applications of Artificial Intelligence, 2012, 25(4): 671-682.
[9] Allanach J, Tu H, Singh S, et al. Detecting, tracking, and counteracting terrorist networks via hidden Markov models [C]//Aerospace Conference, 2004, Proceedings, IEEE. IEEE, 2004: 5.
[10] Singh S, Allanach J, Tu H, et al. Stochastic Modeling of a Terrorist Event via the ASAM system [C]//Systems, Man and Cybernetics, 2004 IEEE International Conference on. IEEE, 2004, 6: 5 673-5 678.
[11] Su X, Bai P, Du F, et al. Application of Bayesian networks in situation assessment [M]. Intelligent Computing and Information Science, Springer Berlin Heidelberg, 2011: 643-648.
[12] Julia Flores M, Nicholson A E, Brunskill A, et al. Incorporating expert knowledge when learning Bayesian network structure: a medical case study[J]. Artificial Intelligence in Medicine, 2011, 53(3): 181-204.
[13] 叶跃祥,糜仲春,王宏宇,等. 基于贝叶斯网络的不确定环境下多属性决策方法[J]. 系统工程理论与实践,2007(4):107-113.
[14] Laskey K B, Mahoney S M, Wright E. Hypothesis management in situation-specific network construction [C]//Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc, 2001: 301-309.
[15] Mahoney S M, Laskey K B. Constructing situation specific belief networks [C]//Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc, 1998: 370-378.
[16] Elzinga P, Poelmans J, Viaene S, et al. Terrorist threat assessment with formal concept analysis [C]//Intelligence and Security Informatics (ISI), 2010 IEEE International Conference on. IEEE, 2010: 77-82.
[17] Cooper G F, Herskovits E. A Bayesian method for the induction of probabilistic networks from data[J]. Machine Learning, 1992, 9(4): 309-347.
[18] Bouckaert R R. Optimizing causal orderings for generating DAGs from data [C]//Proceedings of the Eighth International Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc, 1992: 9-16.
[19] 王双成. 贝叶斯网络学习、推理与应用[M].上海:立信会计出版社,2010.
[20] 赵晓风. 恐怖主义活动的社会心理危害及对策探讨[J]. 理论导刊,2009 (11):48-50.
[21] Pearl J. Fusin, propagation, and structuring in belief networks[J]. Artificial Intelligence, 1986, 29: 241-288.
[22] de Campos C P, Cozman F G. Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks [C]//AAAI Conference on Artificial Intelligence. 2013: 217-223.
[23] Lauritzen S L, Spiegelhalter D J. Local computations with probabilities on graphical structures and their application to expert systems[J]. Journal of Royal Statistical Society, 1988, 50(2):157-224.
[24] 鞠彦兵,王爱华. 基于贝叶斯网络的突发事件影响度研究[J]. 自然灾害学报,2011,20(5):40-46.
/
| 〈 |
|
〉 |