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摘要: 粒子滤波(Particle filtering，PF)算法能有效地对工控系统这一类非线性、非高斯噪声系统进行状态估计，但在实际采用经典粒子滤波状态估计检测攻击时，实验结果显示该方法存在很高的漏检率，无法保障系统安全.因此改进经典算法，提出了基于粒子滤波输入估计的态势理解算法.该算法在考虑系统输入与输出关系的同时，结合蒙特卡洛思想，提取工控系统态势特征，计算态势指标，最终实现态势理解.实验结果表明，该算法能有效地感知持续性攻击，并判断系统态势.Abstract: Particle filtering (PF) algorithm can estimate the states of industrial control systems, which are non-linear and have non-Gaussian noises. However, when using classical particle filtering state estimation to detect continuous attacks, it is shown that the false negative rate is too high to ensure the security of the system. Therefore, a situation perception algorithm by means of particle filtering input estimation is proposed to improve the effectiveness of the classical algorithm. Considering the relationship between system input and output and combining Monte-Carlo simulation, the proposed algorithm can extract industrial control system situation features, calculate situation metrics and realize the situation perception. Experimental results indicate that the proposed algorithm can recognize continuous attacks and judge the system situation effectively.1) 本文责任编委高会军
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图 5 提馏段温度单回路控制系统方框图
Fig. 5 Block diagram of temperature single loop control scheme of distillation
图 7 PF状态估计算法仿真结果
Fig. 7 The simulation results related to PF state estimation algorithm
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[1] 黄家辉, 冯冬芹, 王虹鉴.基于攻击图的工控系统脆弱性量化方法.自动化学报, 2016, 42(5):792-798 http://www.aas.net.cn/CN/abstract/abstract18868.shtmlHuang Jia-Hui, Feng Dong-Qin, Wang Hong-Jian. A method for quantifying vulnerability of industrial control system based on attack graph. Acta Automatica Sinica, 2016, 42(5):792-798 http://www.aas.net.cn/CN/abstract/abstract18868.shtml [2] Genge B, Nai Fovino I, Siaterlis C, Masera M. Analyzing cyber-physical attacks on networked industrial control systems. Critical Infrastructure Protection V. Berlin Heidelberg, Germany:Springer, 2011. 167-183 [3] Lu J, Yang X W, Zhang G Q. Support vector machine-based multi-source multi-attribute information integration for situation assessment. Expert Systems with Applications, 2008, 34(2):1333-1340 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=JJ027793165 [4] Bass T. Multisensor data fusion for next generation distributed intrusion detection systems. In: Proceedings of the 1999 IRIS National Symposium on Sensor and Data Fusion. Washington, USA: IRIS, 1999. 24-27 [5] Naderpour M, Lu J, Zhang G Q. An abnormal situation modeling method to assist operators in safety-critical systems. Reliability Engineering and System Safety, 2015, 133:33-47 doi: 10.1016/j.ress.2014.08.003 [6] Kim M C, Seong P H. An analytic model for situation assessment of nuclear power plant operators based on Bayesian inference. Reliability Engineering and System Safety, 2006, 91(3):270-282 doi: 10.1016/j.ress.2005.01.012 [7] 贾驰千, 冯冬芹.基于多目标决策的工控系统设备安全评估方法研究.自动化学报, 2016, 42(5):706-714 http://www.aas.net.cn/CN/abstract/abstract18860.shtmlJia Chi-Qian, Feng Dong-Qin. Industrial control system devices security assessment with multi-objective decision. Acta Automatica Sinica, 2016, 42(5):706-714 http://www.aas.net.cn/CN/abstract/abstract18860.shtml [8] Doucet A, de Freitas N, Gordon N. Sequential Monte Carlo Methods in Practice. New York, USA:Springer, 2001. [9] Carpenter J, Clifford P, Fearnhead P. Improved particle filter for nonlinear problems. IEE Proceedings-Radar, Sonar and Navigation, 1999, 146(1):2-7 doi: 10.1049/ip-rsn:19990255 [10] Kadirkamanathan V, Li P, Jaward M H, Fabri S G. Particle filtering-based fault detection in non-linear stochastic systems. International Journal of Systems Science, 2002, 33(4):259-265 doi: 10.1080/00207720110102566 [11] Arulampalam S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2):174-188 doi: 10.1109/78.978374 [12] 汤永利, 李伟杰, 于金霞, 闫玺玺.基于粒子滤波的网络安全态势预测方法研究.计算机应用与软件, 2017, 34(1):293-297 doi: 10.3969/j.issn.1000-386x.2017.01.053Tang Yong-Li, Li Wei-Jie, Yu Jin-Xia, Yan Xi-Xi. Research on a prediction method of network security situation based on particle filter. Computer Applications and Software, 2017, 34(1):293-297 doi: 10.3969/j.issn.1000-386x.2017.01.053 [13] Salerno J J, Blasch E P, Hinman M, Boulware D M. Evaluating algorithmic techniques in supporting situation awareness. In: Proceedings of the 2005 Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications. Orlando, Florida, USA: SPIE, 2005. 96-104 [14] Endsley M R. Design and evaluation for situation awareness enhancement. In: Proceedings of the 32nd Human Factors Society Annual Meeting. Santa Monica, USA: SAGE, 1988. 97-101 [15] Naderpour M, Lu J, Zhang G Q. A situation risk awareness approach for process systems safety. Safety Science, 2014, 64:173-189 doi: 10.1016/j.ssci.2013.12.005 [16] Gonzalez C A, Hinton A. Detecting malicious software execution in programmable logic controllers using power fingerprinting. Critical Infrastructure Protection Ⅷ. Berlin Heidelberg, Germany:Springer, 2014. 15-27 [17] 陆耿虹, 冯冬芹.工控网络安全态势感知算法实现.控制理论与应用, 2016, 33(8):1054-1060 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201608009Lu Geng-Hong, Feng Dong-Qin. Industrial control system network security situation awareness modeling and algorithm implementation. Control Theory and Applications, 2016, 33(8):1054-1060 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201608009 [18] Cheng Q, Varshney P K, Michels J, Belcastro C M. Distributed fault detection via particle filtering and decision fusion. In: Proceedings of the 8th International Conference on Information Fusion. Philadelphia, PA, USA: IEEE, 2005. 1239-1246 [19] 李天成, 范红旗, 孙树栋.粒子滤波理论、方法及其在多目标跟踪中的应用.自动化学报, 2015, 41(12):1981-2002 http://www.aas.net.cn/CN/abstract/abstract18773.shtmlLi Tian-Cheng, Fan Hong-Qi, Sun Shu-Dong. Particle filtering:theory, approach, and application for multitarget tracking. Acta Automatica Sinica, 2015, 41(12):1981-2002 http://www.aas.net.cn/CN/abstract/abstract18773.shtml [20] 戴连奎, 于玲, 田学民, 王树青.过程控制工程(第3版).北京:化学工业出版社, 2012.Dai Lian-Kui, Yu Ling, Tian Xue-Min, Wang Shu-Qing. Process Control Engineering (3rd edition). Beijing:Chemical Industry Press, 2012. -
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