Advertisement

Cyberattack-Resilient Hybrid Controller Design with Application to UAS

  • Cheolhyeon Kwon
  • Inseok HwangEmail author
Chapter
Part of the Unmanned System Technologies book series (UST)

Abstract

With the widespread use of cyber-physical systems (CPS), the resilience of CPS control against cyberattacks is critical for such systems to operate securely and safely. In this research, safety and performance implications of the CPS subject to cyberattacks are investigated from a control systems perspective. Due to the unpredictable nature of cyberattacks, existing controller designs have been limited to assume some specific attack strategies at the risk of degrading control performance if the actual attack is not so. To design an attack-resilient controller while not making it excessively conservative, this work proposes a hybrid control framework containing multiple sub-controllers that can adapt to various cyberattacks by switching sub-controllers. Further, the robustness of the controller is achieved by the switching logic which determines the safest sub-controller whose future performance under attack is the least bad. The developed hybrid controller is analytically verified for the finite time horizon case, and further extended to the infinite time horizon case. Simulation results are provided to demonstrate the performance and applicability of the proposed controller design to an unmanned aircraft system subject to cyberattacks.

References

  1. 1.
    A. Humayed, J. Lin, F. Li, B. Luo, Cyber-physical systems security–a survey. IEEE Internet of Things J. 4, 1802–1831 (2017)CrossRefGoogle Scholar
  2. 2.
    V.L. Do, L. Fillatre, I. Nikiforov, P. Willett et al., Security of SCADA systems against cyber–physical attacks. IEEE Aerosp. Electron. Syst. Mag. 32(5), 28–45 (2017)CrossRefGoogle Scholar
  3. 3.
    A. Avizenis, J. Laprie, B. Randell, C. Landwehr, Basic concepts and taxonomy of dependable and secure computing. IEEE Trans. Dependable Secure Comput. 1(1), 11–32 (2004)CrossRefGoogle Scholar
  4. 4.
    J. Saltzer, M. Schroeder, The protection of information in computer systems. Proc. IEEE 63, 1278–1308 (1975)CrossRefGoogle Scholar
  5. 5.
    T. Ahmed, A.R. Tripathi, Security policies in distributed CSCW and workflow systems. IEEE Trans. Syst. Man Cybern. Syst. Hum. 40, 1220–1231 (2010)CrossRefGoogle Scholar
  6. 6.
    A. Teixeira, D. Pérez, H. Sandberg, K.H. Johansson, Attack models and scenarios for networked control systems, in Proceedings of the 1st International Conference on High Confidence Networked Systems (ACM, New York, 2012), pp. 55–64Google Scholar
  7. 7.
    D.P. Shepard, J.A. Bhatti, T.E. Humphreys, A.A. Fansler, Evaluation of smart grid and civilian UAV vulnerability to GPS spoofing attacks, in Proceedings of the ION GNSS Meeting, vol. 3 (2012), pp. 3591–3605Google Scholar
  8. 8.
    N.O. Tippenhauer, C. Pöpper, K.B. Rasmussen, S. Capkun, On the requirements for successful GPS spoofing attacks, in Proceedings of the 18th ACM Conference on Computer and Communications Security (ACM, New York, 2011), pp. 75–86Google Scholar
  9. 9.
    H. Qi, X. Wang, L.M. Tolbert, F. Li, F.Z. Peng, P. Ning, M. Amin, A resilient real-time system design for a secure and reconfigurable power grid. IEEE Trans. Smart Grid 2(4), 770–781 (2011)CrossRefGoogle Scholar
  10. 10.
    M. Pajic, J. Weimer, N. Bezzo, O. Sokolsky, G.J. Pappas, I. Lee, Design and implementation of attack-resilient cyberphysical systems: with a focus on attack-resilient state estimators. IEEE Control. Syst. 37(2), 66–81 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    F. Pasqualetti, F. Dorfler, F. Bullo, Attack detection and identification in cyber-physical systems. IEEE Trans. Autom. Control 58, 2715–2729 (2013)MathSciNetCrossRefGoogle Scholar
  12. 12.
    C. Kwon, W. Liu, I. Hwang, Security analysis for cyber-physical systems against stealthy deception attacks, in American Control Conference (ACC), 2013 (IEEE, Piscataway, 2013), pp. 3344–3349Google Scholar
  13. 13.
    V. Gligor, A note on denial-of-service in operating systems. IEEE Trans. Softw. Eng. SE-10(3), 320–324 (1984)CrossRefGoogle Scholar
  14. 14.
    A. Gupta, C. Langbort, T. Basar, Optimal control in the presence of an intelligent jammer with limited actions, in 49th IEEE Conference on Decision and Control (2010), pp. 1096–1101Google Scholar
  15. 15.
    S. Amin, G. Schwartz, S. Sastry, Security of interdependent and identical networked control systems. Automatica 49, 186–192 (2013)MathSciNetCrossRefGoogle Scholar
  16. 16.
    C. Kwon, W. Liu, I. Hwang, Analysis and design of stealthy cyber attacks on unmanned aerial systems. AIAA J. Aerosp. Inf. Syst. 11, 525–539 (2014)Google Scholar
  17. 17.
    Y. Mo, R. Chabukswar, B. Sinopoli, Detecting integrity attacks on SCADA systems. IEEE Trans. Control Syst. Technol. 22(4), 1396–1407 (2014)CrossRefGoogle Scholar
  18. 18.
    Y. Liu, P. Ning, M. Reiter, False data injection attacks against state estimation in electric power grids, in 16th ACM Conference on Computer and Communications Security (2009), pp. 21–32Google Scholar
  19. 19.
    Y. Mo, B. Sinopoli, False data injection attacks in cyber physical systems, in 1st Workshop on Secure Control Systems, no. 7 (2010)Google Scholar
  20. 20.
    A. Teixeira, I. Shames, H. Sandberg, K.H. Johansson, Revealing stealthy attacks in control systems, in 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2012 (IEEE, Piscataway, 2012), pp. 1806–1813CrossRefGoogle Scholar
  21. 21.
    R.S. Smith, A decoupled feedback structure for covertly appropriating networked control systems. IFAC Proc. Vol. 44(1), 90–95 (2011)CrossRefGoogle Scholar
  22. 22.
    A. de Sa, L. Carmo, R. Machado, Covert attacks in cyber-physical control systems. IEEE Trans. Ind. Inf. 13, 1641–1651 (2017)CrossRefGoogle Scholar
  23. 23.
    A. Teixeira, S. Amin, H. Sandberg, K. Johansson, S. Sastry, Cyber security analysis of state estimators in electric power systems, in 49th IEEE Conference on Decision and Control (2010), pp. 5991–5998Google Scholar
  24. 24.
    H. Sandberg, A. Teixeira, K. Johansson, On security indices for state estimators in power networks, in 1st Workshop on Secure Control Systems, no. 8 (2010)Google Scholar
  25. 25.
    S. Amin, X. Litrico, S. Sastry, A.M. Bayen, Cyber security of water SCADA systems. Part I: analysis and experimentation of stealthy deception attacks. IEEE Trans. Control Syst. Technol. 21(5), 1963–1970 (2013)Google Scholar
  26. 26.
    L. Hu, Z. Wang, Q.-L. Han, X. Liu, State estimation under false data injection attacks: security analysis and system protection. Automatica 87, 176–183 (2018)MathSciNetCrossRefGoogle Scholar
  27. 27.
    M. Pajic, I. Lee, G.J. Pappas, Attack-resilient state estimation for noisy dynamical systems. IEEE Trans. Control Netw. Syst. 4(1), 82–92 (2017)MathSciNetCrossRefGoogle Scholar
  28. 28.
    H. Fawzi, P. Tabuada, S. Diggavi, Secure state-estimation for dynamical systems under active adversaries, in 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2011 (IEEE, Piscataway, 2011), pp. 337–344CrossRefGoogle Scholar
  29. 29.
    Y. Shoukry, P. Nuzzo, A. Puggelli, A.L. Sangiovanni-Vincentelli, S.A. Seshia, P. Tabuada, Secure state estimation for cyber physical systems under sensor attacks: a satisfiability modulo theory approach. IEEE Trans. Autom. Control 62, 4917–4932 (2017)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Y. Chen, S. Kar, J.M. Moura, Dynamic attack detection in cyber-physical systems with side initial state information. IEEE Trans. Autom. Control 62(9), 4618–4624 (2017)MathSciNetCrossRefGoogle Scholar
  31. 31.
    F. Miao, Q. Zhu, M. Pajic, G.J. Pappas, Coding schemes for securing cyber-physical systems against stealthy data injection attacks. IEEE Trans. Control Netw. Syst. 4(1), 106–117 (2017)MathSciNetCrossRefGoogle Scholar
  32. 32.
    C. Kwon, S. Yantek, I. Hwang, Safety assessment of unmanned aerial systems subject to stealthy cyber attacks. AIAA J. Aerosp. Inf. Syst. 13, 27–45 (2016)Google Scholar
  33. 33.
    C. Kwon, I. Hwang, Reachability analysis for safety assurance of cyber-physical systems against cyber attacks. IEEE Trans. Autom. Control 63, 2272–2279 (2017)MathSciNetCrossRefGoogle Scholar
  34. 34.
    A.-Y. Lu, G.-H. Yang, Input-to-state stabilizing control for cyber-physical systems with multiple transmission channels under denial-of-service. IEEE Trans. Autom. Control 63, 1813–1820 (2017)MathSciNetCrossRefGoogle Scholar
  35. 35.
    V. Dolk, P. Tesi, C. De Persis, W. Heemels, Event-triggered control systems under denial-of-service attacks. IEEE Trans. Control Netw. Syst. 4(1), 93–105 (2017)MathSciNetCrossRefGoogle Scholar
  36. 36.
    X. Jin, W.M. Haddad, T. Yucelen, An adaptive control architecture for mitigating sensor and actuator attacks in cyber-physical systems. IEEE Trans. Autom. Control 62, 6058–6064 (2017)MathSciNetCrossRefGoogle Scholar
  37. 37.
    A. Abbaspour, M. Sanchez, A. Sargolzaei, K. Yen, N. Sornkhampan, Adaptive neural network based fault detection design for unmanned quadrotor under faults and cyber attacks, in 25th International Conference on Systems Engineering (ICSEng) (IEEE, Piscataway, 2017)Google Scholar
  38. 38.
    T. Basar, G. Olsder, Dynamic Noncooperative Game Theory, 2nd edn. (Society for Industrial and Applied Mathematics, Philadelphia, 1999)zbMATHGoogle Scholar
  39. 39.
    Q. Zhu, T. Basar, Robust and resilient control design for cyber-physical systems with an application to power systems, in 50th IEEE Conference on Decision and Control and European Control Conference (2011), pp. 4066–4071Google Scholar
  40. 40.
    T. Basar, A dynamic games approach to controller design: disturbance rejection in discrete-time. IEEE Trans. Autom. Control 36(8), 936–952 (1991)MathSciNetCrossRefGoogle Scholar
  41. 41.
    C. Seah, I. Hwang, Stochastic linear hybrid systems: modeling, estimation, and application in air traffic control. IEEE Trans. Control Syst. Technol. 17, 563–575 (2009)CrossRefGoogle Scholar
  42. 42.
    C.E. Garcia, D.M. Prett, M. Morari, Model predictive control: theory and practice—a survey. Automatica 25, 335–348 (1989)CrossRefGoogle Scholar
  43. 43.
    E. Scholte, M.E. Campbell, Robust nonlinear model predictive control with partial state information. IEEE Trans. Control Syst. Technol. 16, 636–651 (2008)CrossRefGoogle Scholar
  44. 44.
    G.E. Dullerud, F. Paganini, A Course in Robust Control Theory: A Convex Approach (Springer, New York, 2005)zbMATHGoogle Scholar
  45. 45.
    K.S. Narenda, S.S. Tripathi, Identification and optimization of aircraft dynamics. AIAA J. Aircraft 10(4), 193–199 (1973)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Purdue UniversityWest LafayetteUSA

Personalised recommendations