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基于核主元分析的过程控制系统欺骗攻击 被引量：1

Deception attacks in process control systems using kernel principal component analyses
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摘要 针对过程控制系统的传感器信号欺骗攻击,提出了一种基于数据驱动的黑箱检测方法。首先以典型化工单元连续搅拌釜式反应器CSTR过程控制系统为对象,建立了其欺骗攻击模型;接着,开发了基于核主元分析传感器信号欺骗检测算法和在线实施步骤,最后针对CSTR控制系统的反应温度欺骗攻击检测进行了仿真研究,结果表明本文方法在攻击行为发生后能快速、准确地检测出攻击行为。 A deception detection method was developed using a data-driven technique to deal with sensor signal attacks on process control systems. An attack model is developed to attack the control system of a continuously stirred tank reactor （CSTR）. Then, an algorithm was developed for deception detection using kernel principal component analysis with on line implementation. Simulation of the deception detection of the reactor temperature show that this method more quickly and accurately detects eyber attacks.
作者 陈冬青 彭勇 王华忠
机构地区 中国信息安全测评中心 华东理工大学自动化系
出处 《清华大学学报（自然科学版）》 EI CAS CSCD 北大核心 2013年第12期1694-1698,共5页 Journal of Tsinghua University(Science and Technology)
关键词 核函数主元分析 欺骗攻击 信息安全 过程控制系统 kernel principal component analysis deception attack cyber security process control systems
分类号 TP29 [自动化与计算机技术—检测技术与自动化装置]
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参考文献6

  • 1彭勇,江常青,谢丰,戴忠华,熊琦,高洋.工业控制系统信息安全研究进展[J].清华大学学报（自然科学版）,2012,52(10):1396-1408. 被引量：175
  • 2Liu Y, Ning P, Reiter M. False data injection attacks against state estimation in electric power grids [C] //Proceedings of the 16th ACM Conference on Computer and Communications Security. New York, USA: Association for Computing Machinery Press, 2009 : 21 - 32.
  • 3Saurabh A, Xavier L, Shankar S S, et al. Stealthy deception attacks on water SCADA systems [C]// Proceedings of the 13th ACM International Conference on Hybrid Systems: Computation and Control-HSCC'10. New York, USA: Association for Computing Machinery Press, 2010: 161 - 170.
  • 4Yoon S, Macgregor J F. Statistical and causal model based approaches to fault detection and isolation [J]. AIChE J, 2000, 46(9), 1813-1824.
  • 5Scholkopf B, Srnola A, Muller K-R. Nonlinear component analysis as a Kernel eigenvalue problem [J]. Neural Computation, 1998, 10(5), 1299 - 1319.
  • 6Lee J M, Yoo C K, Choi S W, et al. Nonlinear process monitoring using kernel principal component analysis [J]. Chernical Engineering Science, 2004, 59(1): 23-234.

二级参考文献96

  • 1NIST SP800-82.Guide to Industrial Control Systems(ICS)Security[S].Gaithersburg,USA:National Institute of Standards and Technology(NIST),2011.
  • 2Simon H A.The architecture of complexity[C] //Proceedings of the American Philosophical Society.Philadelphia,USA:Batsford,1962:467-482.
  • 3Bishop M.Computer Security[M].Boston,USA:Addison Wesley,2003.
  • 4Department of Homeland Security(DHS).Cyber Security Assessments of Industrial Control System[S].Washington DC,USA:Department of Homeland Security(DHS),2010.
  • 5The European Network and Information Security Agency(ENISA).Protecting Industrial Control Systems,Recommendations for Europe and Member States[R].Heraklion,Greece:Recommendations for Europe and Member States,2011.
  • 6Byres E J,Kay J,Carter J.Myths and facts behind cyber security and industrial control(2003)[Z/OL].(2010-02-12),http://www.pimaweb.org/conference/april2003/pdfs/MythsAndFactsBehindCyberSecurity.pdf.
  • 7David A.Multiple Efforts to Secure Control Systems Are Under Way,but Challenges Remain,GAO-07-1036[R].Washington DC,USA:US Government Accountability Office(US GAO),2007.
  • 8IEC61508.Functional Safety of E/E/PE Safety-Related Systems[S].Geneva,Switzerland:International Electrotechnical Commission(IEC),2000.
  • 9Piètre-Cambacédès L,Chaudet C.The SEMA referential framework:Avoiding ambiguities in the terms“security”and“safety”[J].International Journal of Critical Infrastructure Protection,2010,3(2):55-66.
  • 10NIST906330.Security Assurance Levels:A Vector Approach to Describing Security Requirements[S].Gaithersburg,USA:National Institute of Standards and Technology(NIST),2010.

共引文献174

同被引文献17

  • 1魏利军,关磊,吴宗之.重大危险源安全监控系统运行模式分析与探讨[J].中国安全生产科学技术,2006,2(5):37-40. 被引量：21
  • 2VAPNIK V N.统计学习理论[M].许建华,张学工,译.北京:电子工业出版社,2004.
  • 3Macgregor J F, Kourti T. Statistical process control of multivariate process[J]. Control Engineering Practice, 1995, 3(3): 403-414.
  • 4Geladi P, Kowalski B R. Partial least-squares regression: a tutorial[J]. Anal. Chem. Acta, 1986, 185: 1-17.
  • 5Kourti T. Application of latent variable methods to process control and multivariate statistical process control in industry[J]. International Journal of Adaptive Control and Signal Processing, 2005, 19(4): 213-246.
  • 6Qin S J, Mcavoy T J. Nonlinear PLS Modeling Using Neural Network[J]. Computers & Chemical Engineering, 1992, 16(4): 379-391.
  • 7Malthouse E C, Tamhane A C, Mah R S H. Non-linear partial least squares[J]. Computer and Chemical Engineering, 1997, 21(8): 875-890.
  • 8Scholkopf B, Smola A J. Learning with kernels - support vector machines, regularization, optimization and beyond[M]. Cambridge, MA: MIT Press 2002.
  • 9王桂增, 叶昊. 主元分析与偏最小二乘法[M]. 北京: 清华大学出版社, 2012.
  • 10Rosipal R. Kernel partial least squares for nonlinear regression and discrimination[J]. Neural Network World, 2003, 13: 291-300.

引证文献1

清华大学学报（自然科学版）
2013年 第12期

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