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access icon openaccess Novel cyber fault prognosis and resilience control for cyber–physical systems

Cyber–physical systems (CPSs) consists of a network, computation, and physical process. Embedded networks, which deliver control and sensing signal, can potentially affect CPSs performance. However, the degradation of physical system performance caused by the embedded networks is frequently oversimplified with strong assumptions. The proposed scheme effectively relaxes those assumptions in the existing works that network delays are bounded in a specific range or its distribution is time invariant. Most of the existing works on fault diagnosis and prognosis addressed the physical system fault detection and isolation, and ignore cyber network faults. A novel cyber network fault prognosis scheme is proposed to deal with both of cyber and physical system fault. It can identify when a cyber fault has occurred, and pinpoint the type of fault based on CPS system performance prediction, then, trigger resilience controller at an appropriate time to minimise the computational overhead. Thus, it can guarantee the stability of the entire CPS and substantially reduce computational overhead of the resilience control by triggering it if necessary.


    1. 1)
      • 9. Cardenas, A., Amin, S., Sastry, S.: ‘Secure control: towards survivable cyber-physical systems’. 2008 The 28th Int. Conf. on Distributed Computing Systems Workshops, Beijing, China, 2008, pp. 495500.
    2. 2)
      • 16. Jiang, W., Guo, W.H., Sang, N.: ‘Periodic real-time message scheduling for confidentiality-aware cyber-physical system in wireless networks’. Proc. of Fifth Int. Conf. on Frontier of Computer Science and Technology, Changchun, China, 2010.
    3. 3)
      • 7. Zhu, Z.Q., Zhou, X.Z.: ‘Fault detection based on the states observer for networked control systems with uncertain long time-delay’. Proc. IEEE Int. Conf. Automation Logistics, Jinan, China, August 2007, pp. 23202324.
    4. 4)
      • 5. Zhang, H., Yang, J., Su, C.-Y.: ‘T-S fuzzy-model-based robust H design for networked control systems with uncertainties’, IEEE Trans. Ind. Inf., 2007, 3, (4), pp. 289301.
    5. 5)
      • 6. Wang, Y., Ye, H., Wang, G.: ‘A new method for fault detection of networked control systems’. Proc. IEEE Conf. Industrial Electronics Applications, Singapore, 24–26 May 2006, pp. 14.
    6. 6)
      • 19. Zhu, M., Martinez, S.: ‘Stackelberg-game analysis of correlated attacks in cyber-physical systems’. Proc. American Control Conf., San Francisco, CA, USA, July 2011, pp. 40634068.
    7. 7)
      • 3. Liu, F.C., Yao, Y.: ‘Modeling and analysis of networked control systems using hidden Markov models’. Proc. Int. Conf. Machine Learning Cybernetics, Guangzhou, China, 2005, pp. 928931.
    8. 8)
      • 10. Amin, S., Cárdenas, A., Sastry, S.: ‘Safe and secure networked control systems under denial-of-service attacks’, Hybrid Syst.: Comput. Control, 2009, 5469, pp. 3145.
    9. 9)
      • 21. Bi, S., Zawodniok, M.: ‘A novel cyber network fault diagnosis scheme for cyber-physical systems’. 2017 IEEE Int. Conf. on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, UK, 2017, pp. 3036.
    10. 10)
      • 1. Fisher, A., Jacobson, C.A., Lee, E.A.: ‘Industrial cyber-physical systems – iCyPhy’, in Aiguier, M., Boulanger, F., Krob, D., et al (Eds.): ‘Complex systems design and management’ (Springer International Publishing, Dordrecht, Switzerland, 2014), pp. 2137.
    11. 11)
      • 11. Liu, Y., Reiter, M.K., Ning, P.: ‘False data injection attacks against state estimation in electric power grids’. Proc. ACM Conf. Computer Communications Security, Chicago, IL, USA, November 2009, pp. 2132.
    12. 12)
      • 17. Pasqualetti, F., Dorfler, F., Bullo, F.: ‘Attack detection and identification in cyber-physical systems’, IEEE Trans. Autom. Control, 2013, 58, (11), pp. 27152729.
    13. 13)
      • 14. Smith, R.: ‘A decoupled feedback structure for covertly appropriating network control systems’. Proc. IFAC World Congress, Milan, Italy, August 2011, pp. 9095.
    14. 14)
      • 15. Gamage, T., McMillin, B.M., Roth, T.P.: ‘Enforcing information flow security properties in cyber-physical systems: a generalized framework based on compensation’. 2010 IEEE 34th Annual Computer Software and Applications Conf. Workshops (COMPSACW), Seoul, South Korea, 2010, pp. 158163.
    15. 15)
      • 13. Mo, Y., Sinopoli, B.: ‘Secure control against replay attacks’. Proc. Allerton Conf. Communication, Control, Computing, Monticello, IL, USA, September 2010, pp. 911918.
    16. 16)
      • 8. Rawat, D., Rodrigues, J., Stojmenovic, I.: ‘Cyber-physical systems: from theory to practice’, 2016.
    17. 17)
      • 20. Bi, S., Zawodniok, M.: ‘PDF-based tuning of stochastic optimal controller design for cyber-physical systems with uncertain delay dynamics’, IET Cyber-Phys. Syst., Theory Appl., 2017, 2, (1), pp. 19.
    18. 18)
      • 12. Teixeira, A., Amin, S., Sandberg, H., et al: ‘Cyber security analysis of state estimators in electric power systems’. Proc. IEEE Conf. Decision Control, Atlanta, GA, USA, December 2010, pp. 59915998.
    19. 19)
      • 2. Yagdereli, E., Gemci, C., Aktas, A.Z.: ‘A study on cyber-security of autonomous and unmanned vehicle’, J. Def. Model. Simul., 2015, 12, (4), pp. 369381.
    20. 20)
      • 4. Liu, G.P., Xia, Y., Chen, J., et al: ‘Networked predictive control of systems with random network delays in both forward and feedback channels’, IEEE Trans. Ind. Electron., 2007, 54, (3), pp. 12821297.
    21. 21)
      • 18. Xu, H., Jagannathan, S.: ‘Stochastic optimal control of unknown linear networked control system in the presence of random delays and packet losses’, Automatica, 2012, 48, pp. 10171029.

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