Target Threat Assessment using Rule-Based Joint Fuzzy and Probabilistic Networks

Document Type : Original Article

Authors

Abstract

Threat assessment is one of the most important pillars of data fusion systems. In this paper, we use two graphical models: fuzzy cognitive map and bayesian network to implement a complete threat assessment network. The structure of this network includes numerous variables of threat assessment and relates them well to each other. Given the uncertainty in all threat assessment issues, various types of uncertainty and how to deal with them are considered in this article. A comprehensive review has also been carried out on a variety of methods for incorporating both types of fuzzy and probabilistic uncertainties and a new approach is proposed. In this method, two separated fuzzy and bayesian networks are used to consider uncertainties. The approach of the proposed method is fully described, step-by-step. Furthermore, this paper addresses the major challenges of the threat assessment problem and shows that the proposed method is capable of solving these issues. To illustrate the effectiveness of the proposed method, a set of qualitative and         quantitative validation criteria is presented. As a test a scenario for air targets is simulated and the results of the proposed method are qualitatively and quantitatively compared with fuzzy cognitive map and    bayesian network methods. These results indicate that the proposed method works better than other      methods regarding root mean square error, total and trivial sensitivity degree and seperation degree. Moreover, the effectiveness of the proposed structure and method has been confirmed by experts in the field of battle management.
 

Keywords

References

 
[1]    R. Gholami and M. Okhovat, “Designing Radar and IR Sensors Data Fusion System for Target Tracking in Noise Jamming Conditions,” Journal Of Electronical & Cyber Defence, vol. 5, 2017. (In Persian).##
[2]    M. E. Liggins, D. L. Hall, and J. Llinas, “Handbook of multisensor data fusion: theory and practice,” ed: Taylor & Francis, 2009##.
[3]    V. Akbari and S. M. S. Homami, “A Framework For The Status Estimation In Distributed       Denial-Of-Service Attacks By Data Fusion of Human-And-Technical Sensors Based on Fuzzy Logic,” Journal of Electronical & Cyber Defence, vol. 5, 2017. (In Persian).##
[4]    D. L. Hall and J. Llinas, “An introduction to multisensor data fusion,” Proceedings of the IEEE, vol. 85, pp. 6-23, 1997.##
[5]    J. Yun, B. Choi, M.M. Han, and S.-H. Kim, “Air Threat Evaluation System using Fuzzy-Bayesian Network based on Information Fusion,” Journal of Internet Computing and Services, vol. 13, pp.     21-31, 2012##.
[6]    N. P. Rao, S. K. Kashyap, and G. Girija, “Situation assessment in air-combat: A fuzzy-bayesian hybrid approach,” International Conference on Aerospace Science and Technology, 2008##.
[7]    J. J. Salerno, S. J. Yang, I. Kadar, M. Sudit, G. P. Tadda, and J. Holsopple, “Issues and challenges in higher level fusion: Threat/impact assessment and intent modeling (a panel summary),” 13th Conference on Information Fusion, 2010.##
[8]    E. Shahbazian, G. Rogova, and M. J. de Weert, “Harbour protection through data fusion technologies,” Springer Science & Business Media, 2008##.
[9]    E. G. Little and G. L. Rogova, “An ontological analysis of threat and vulnerability,” 9th Conference on Information Fusion, 2006##.
[10] J. Llinas and R. T. Antony, “Blackboard concepts for data fusion applications,” International journal of pattern recognition and artificial intelligence, vol. 7, pp. 285-308, 1993##.
[11] A. J. Rashidi, K. D. Ahmadi, and F. S. Khodadad, “Projection of Muli Stage Cyber Attack Based on Belief Model and Fuzzy Inference,” Journal of Electronical & Cyber Defence, vol. 3, 2015. (In Persian)##
 [12] S. Kumar and A. M. Dixit, “Threat evaluation modelling for dynamic targets using fuzzy logic approach,” International Conference on Computer Science and Engineering, 2012##.
[13] L. Man and F. Xinxi, “Situation assessment based on bayesian networks,” 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008##.
[14] C. M. Bishop, “Graphical models,” Pattern recognition and machine learning, vol. 4, pp.    359-422, 2006##.
[15] M. I. Jordan and C. Bishop, “An introduction to graphical models,” unpublished book, 2001##.
[16] E. I. Papageorgiou, “Fuzzy cognitive maps for applied sciences and engineering,” ed: Springer, 2014##.
[17]  A. Mittal, “Bayesian Network Technologies: Applications and Graphical Models,” IGI Global, 2007##.
[18] P. P. Groumpos, “Fuzzy cognitive maps: Basic theories and their application to complex systems,” in Fuzzy cognitive maps, ed: Springer, pp. 1-22, 2010##.
[19] R. Axelrod, “Structure of decision: The cognitive maps of political elites,” Princeton University Press, 1976.##
[20] B. Kosko, “Fuzzy cognitive maps,” International Journal of man-machine studies, vol. 24, pp. 65-75, 1986##.
[21] E. I. Papageorgiou and J. L. Salmeron, “A review of fuzzy cognitive maps research during the last decade,” IEEE Transactions on Fuzzy Systems, vol. 21, pp. 66-79, 2013##.
[22] C. D. Stylios and P. P. Groumpos, “Modeling complex systems using fuzzy cognitive maps,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 34, pp. 155-162, 2004##.
[23] J. Pearl, “Probabilistic reasoning in intelligent systems: Networks of plausible inference,” ed: Morgan Kaufmann Publishers, Los Altos, 1988##.
[24] R. G. Cowell, P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter, “Probabilistic networks and expert systems: Exact computational methods for Bayesian networks,” Springer Science & Business Media, 2006##.
[25] O. Pourret, P. Naïm, and B. Marcot, “Bayesian networks: a practical guide to applications,” vol. 73, John Wiley & Sons, 2008##.
[26] R. Neut, “Uncertainty analysis in Bayesian networks,” Master thesis, Utrecht university, 2014##.
[27] M. L. Hinman, “Some computational approaches for situation assessment and impact assessment,” 5th Conference on Information Fusion, 2002##.
[28] Y. Liang, “An approximate reasoning model for situation and threat assessment,” 4th International Conference on Fuzzy Systems and Knowledge Discovery, 2007##.
[29] M. J. Liebhaber and B. Feher, “Air threat assessment: Research, model, and display guidelines,” Space and Naval Warfare Systems Command San Diego CA, 2002##.
[30] J. Baghmalayi and H. Sahami, “Investigation of Threat Measurement Indicators and Analysis of Ports Security Based on Vulnerability Assessment Model (Risk&Threat Analysis Matrix TRAM),” 13th Marine Industries Conference, 2011. (In Persian)##
[31] M. J. Wierman, “An Introduction to the Mathematics of Uncertainty,” Creighton University, 2010##.
[32] M. J. Liebhaber and C. Smith, “Naval air defense threat assessment: Cognitive factors and model,” Pacific Science And Engineering Group Inc San Diego CA, 2000##.
[33] S. K. Das, “High-level data fusion,” Artech House, 2008##.
[34] A. H. Meghdadi and M.-R. Akbarzadeh-T, “Probabilistic fuzzy logic and probabilistic fuzzy systems,” 10th IEEE International Conference on Fuzzy Systems, 2001##.
[35] M. Laviolette and J. W. Seaman, “Unity and diversity of fuzziness-from a probability viewpoint,” IEEE Transactions on Fuzzy Systems, vol. 2, pp. 38-42, 1994##.
[36] L. A. Zadeh, “Discussion: Probability theory and fuzzy logic are complementary rather than competitive,” Technometrics, vol. 37, pp. 271-276, 1995##.
[37] E. Kentel and M. M. Aral, “Probabilistic-fuzzy health risk modeling,” Stochastic Environmental Research and Risk Assessment, vol. 18, pp.      324-338, 2004##.
[38] H.-X. Li, X. Duan, and Z. Liu, “Three-dimensional fuzzy logic system for process modeling and control,” Journal of Control Theory and Applications, vol. 8, pp. 280-285, 2010##.
[39] A. F. Shapiro, “Fuzzy random variables,” Insurance: Mathematics and Economics, vol. 44, pp. 307-314, 2009##.
[40] S. Nadkarni and P. P. Shenoy, “A Bayesian network approach to making inferences in causal maps,” European Journal of Operational Research, vol. 128, pp. 479-498, 2001##.
[41] Y. Y. Wee, W. P. Cheah, S. C. Tan, and K. Wee, “A method for root cause analysis with a Bayesian belief network and fuzzy cognitive map,” Expert Systems with Applications, vol. 42, pp. 468-487, 2015##.
[42] W.-P. Cheah, K.-Y. Kim, H.-J. Yang, S.-H. Kim, and J.-S. Kim, “Fuzzy Cognitive Map and Bayesian Belief Network for Causal Knowledge Engineering: A Comparative Study,” The KIPS Transactions: PartB, vol. 15, pp. 147-158, 2008##.
[43] W. P. Cheah, Y. S. Kim, K.-Y. Kim, and H.-J. Yang, “Systematic causal knowledge acquisition using FCM constructor for product design decision support,” Expert Systems with Applications, vol. 38, pp. 15316-15331, 2011##.
 
[44] H.-J. Song, Z.-Q. Shen, C.-Y. Miao, Z.-Q. Liu, and Y. Miao, “Probabilistic fuzzy cognitive map,” IEEE International Conference on Fuzzy Systems, 2006##.
[45] A. Eleye‐Datubo, A. Wall, and J. Wang, “Marine and Offshore Safety Assessment by Incorporative Risk Modeling in a Fuzzy‐Bayesian Network of an Induced Mass Assignment Paradigm,” Risk Analysis, vol. 28, pp. 95-112, 2008##.
[46] J. T. Brignoli, M. M. Pires, S. M. Nassar, and D. Sell, “A fuzzy-Bayesian model based on the superposition of states applied to the clinical reasoning support,” SAI Intelligent Systems Conference (IntelliSys), 2015##.
[47] A. Le Dorze, B. Duval, L. Garcia, D. Genest, P. Leray, and S. Loiseau, “Probabilistic Cognitive Maps Semantics of a Cognitive Map when the Values are Assumed to be Probabilities,” International Conference on Agents and Artificial Intelligence (ICAART), 2014##.
[48] Y. Deng, “A threat assessment model under uncertain environment,” Mathematical Problems in Engineering, 2015##.
[49] J. Fang, J. Huang, and M. Liu, “The Research on Fuzzy Bayesian Network Model for the Network Public Opinion Situation and Threat Assessment,” Third International Conference on Networking and Distributed Computing, 2012##.
[50]  J. Chen, G.-h. Yu, and X.-g. Gao, “Cooperative t          hreat assessment of multi-aircrafts based on synthetic fuzzy cognitive map,” Journal of Shanghai Jiaotong University (Science), vol. 17, pp. 228-232, 2012.##
[51] C. Dongfeng, F. Yu, and L. Yongxue, “Threat assessment for air defense operations based on intuitionistic fuzzy logic,” Procedia Engineering, vol. 29, pp. 3302-3306, 2012##.
[52] E. Azimirad and J. Haddadnia, “Target threat assessment using fuzzy sets theory,” International Journal of Advances in Intelligent Informatics, vol. 1, pp. 57-74, 2015##.
[53] Y. Lu, Y. Wang, Y. Lei, and Y. Wang, “Air targets threat assessment based on fuzzy rough reasoning,” 27th Chinese Conference on Control and Decision (CCDC), 2015##.
[54] M. Fatahi and s. garocy, “Threat Evaluation Using Fuzzy Logic,” 6th National Conference of Iran's Scientific on Command, Control, Communications, Computer & Intelligence, 2012. (In Persian)##
[55] F. Johansson and G. Falkman, “A Bayesian network approach to threat evaluation with application to an air defense scenario,” 11th International Conference on Information Fusion, 2008##.
[56] X. T. Nguyen, “Threat assessment in tactical airborne environments,” Proceedings of the Fifth International Conference on Information Fusion, 2002##.
[57] N. Okello and G. Thoms, “Threat assessment using Bayesian networks,” Proceedings of the 6th International Conference on Information fusion, 2003##.
[58] J. F. Brancalion, D. de Oliveira Marques, and K. H. Kienitz, “Framework for situation assessment and threat evaluation with application to an air defense scenario,” 18th International Conference on Information Fusion (Fusion), 2015##.
[59] J. Aguilar, “A survey about fuzzy cognitive maps papers,” International journal of computational cognition, vol. 3, pp. 27-33, 2005##.
[60] J. Carvalho and J. A. Tomè, “Rule based fuzzy cognitive maps and fuzzy cognitive maps-a comparative study,” 18th International Conference of the North American, Fuzzy Information Processing Society, 1999##.
[61] J. P. Carvalho, “Rule based fuzzy cognitive maps in humanities, social sciences and economics,” Soft Computing in Humanities and Social Sciences, Springer, pp. 289-300, 2012.##
[62] E. I. Papageorgiou and J. L. Salmeron, “Methods and algorithms for fuzzy cognitive map-based modeling,” Fuzzy Cognitive Maps for Applied Sciences and Engineering, Springer, pp. 1-28, 2014.##
[63] K. F.-R. Liu, J.-Y. Kuo, K. Yeh, C.-W. Chen,      H.-H. Liang, and Y.-H. Sun, “Using fuzzy logic to generate conditional probabilities in Bayesian belief networks: a case study of ecological assessment,” International Journal of Environmental Science and Technology, vol. 12, pp. 871-884, 2015##.
[64] P. Baraldi, M. Conti, M. Librizzi, E. Zio, L. Podofillini, and V. Dang, “A Bayesian network model for dependence assessment in human reliability analysis,” Proceedings  of the Annual European Safety and Reliability Conference, ESREL, 2009##.
[65] P. Baraldi, L. Podofillini, L. Mkrtchyan, E. Zio, and V. N. Dang, “Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application,” Reliability Engineering & System Safety, vol. 138, pp. 176-193, 2015##.
[66] W. Van Leekwijck and E. E. Kerre, “Defuzzification: criteria and classification,” Fuzzy sets and systems, vol. 108, pp. 159-178, 1999##.
[67] F. Johansson and G. Falkman, “A comparison between two approaches to threat evaluation in an air defense scenario,” International Conference on Modeling Decisions for Artificial Intelligence, 2008##.
[68] Q. Changwen and H. You, “A method of threat assessment using multiple attribute decision making,” 6th International Conference on Signal Processing, 2002##.
[69] E. Mahboobi, M. Vahedian, and M. Hedayati, “Investigation and comparison of threat assessment methods in marine battles and suggestion of proposed model,” 15th Marine Industries Conference, 2013. (In Persian)##

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History

  • Receive Date: 18 December 2017
  • Accept Date: 27 May 2018
  • Publish Date: 20 February 2019

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