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Multi-path switching protection for networked control systems under unbounded DoS attacks

  • 1.

    College of Information Engineering, Zhejiang University of Technology, Hangzhou 310000, China

  • 2.

    School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China

Funds:  National Key Research and Development Program of China (2018AAA0100801) and the National Natural Science Foundation of China under Grant(61673350).
Cite this:
https://doi.org/10.52396/JUST-2020-1137
More Information
  • Author Bio:

    Zhu Qiaohui received the BE degree from Tianjin University of Technology and Education, Tianjin, China, in 2018, and is currently a Postgraduate with Zhejiang University of Technology, Hangzhou, China. Her research interests include networked control systems and network security.

    Ling Qipeng received his BE degree from Xi'an University of Technology, China, in 2016. He is currently pursuing a M.S. degree at College of Information Engineering, Zhejiang University of Technology. His main research interests include wireless network control systems.

    Kang Yu received the PhD degree in control theory and control engineering from the University of Science and Technology of China, Hefei, China, in 2005. From 2005 to 2007, he was a Postdoctoral Fellow with the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. He is currently a Professor with the State Key Laboratory of Fire Science, Department of Automation, and Institute of Advanced Technology, University of Science and Technology of China, and with the Key Laboratory of Technology in GeoSpatial Information Processing and Application System, Chinese Academy of Sciences. His current research interests include monitoring of vehicle emissions, adaptive/robust control, variable structure control, mobile manipulator, and Markovian jump systems.

  • Corresponding author: Zhao Yunbo received his BSc degree in mathematics from Shandong University, Jinan, China in 2003, MSc degree in systems sciences from the Key Laboratory of Systems and Control, Chinese Academy of Sciences, Beijing, China in 2007, and PhD degree in control engineering from the University of South Wales (formerly University of Glamorgan), Pontypridd, UK in 2008, respectively. He is currently a Professor with Zhejiang University of Technology, Hangzhou, China. He has worked on networked control systems for many years and proposed a unified control framework called "packet-based control''. He has also been interested in the understanding of protein synthesis by mathematically modelling such systems and discovering the underlying organization principles. His current interests mainly focus on AI-driven control and automation, specifically, AI-driven networked intelligent control, AI-driven human-machine autonomies and AI-driven machine gaming. E-mail: ybzhao@ustc.edu.cn
  • Publish Date: 31 January 2021
    Abstract

  • Abstract

    The strategy design and closed-loop stability of networked control systems under unbounded denial of service (DoS) attacks are probed. A multi-path switching protection strategy is firstly designed by noticing the usually available multiple paths in data communication networks. The strategy consists of a DoS attack detection module at the actuator side to identify DoS attacks from normal data packet dropouts, and a multi-path switching module at the sensor side to effectively switch the data transmission path when necessary. Then, the sufficient conditions for the closed-loop system being global mean square asymptotic stability are given, with a corresponding controller gain design method. Numerical examples illustrate the effectiveness of the proposed approach.

    Abstract

    The strategy design and closed-loop stability of networked control systems under unbounded denial of service (DoS) attacks are probed. A multi-path switching protection strategy is firstly designed by noticing the usually available multiple paths in data communication networks. The strategy consists of a DoS attack detection module at the actuator side to identify DoS attacks from normal data packet dropouts, and a multi-path switching module at the sensor side to effectively switch the data transmission path when necessary. Then, the sufficient conditions for the closed-loop system being global mean square asymptotic stability are given, with a corresponding controller gain design method. Numerical examples illustrate the effectiveness of the proposed approach.
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    • References(43)
  • [1]
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    [2]
    Zhang D, Shi P, Wang Q G, et al. Analysis and synthesis of networked control systems: A survey of recent advances and challenges. ISA Transactions, 2017, 66: 376-392.
    [3]
    Zhang X M, Han Q L, Yu X. Survey on recent advances in networked control systems. IEEE Transactions on Industrial Informatics, 2015, 12(5): 1740-1752.
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    Ge X, Yang F, Han Q L. Distributed networked control systems: A brief overview. Information Sciences, 2017, 380: 117-131.
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    Rouamel M, Gherbi S, Bourahala F. Robust stability and stabilization of networked control systems with stochastic time-varying network induced delays. Transactions of the Institute of Measurement and Control, 2020, 42(10): 1782-1796.
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    Zhang Z, Zheng W, Xie P, et al.H-infinity stability analysis and output feedback control for fuzzy stochastic networked control systems with time-varying communication delays and multipath packet dropouts. Neural Computing and Applications, 2020: 1-19.
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    Li M, Chen Y. Challenging research for networked control systems: A survey. Transactions of the Institute of Measurement and Control, 2019, 41(9): 2400-2418.
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    Mahmoud M S, Hamdan M M, Baroudi U A. Modeling and control of cyber-physical systems subject to cyber attacks: A survey of recent advances and challenges. Neurocomputing, 2019, 338: 101-115.
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    Shen Y, Zhang W, Ni H, et al. Guaranteed cost control of networked control systems with DoS attack and time-varying delay. International Journal of Control, Automation and Systems, 2019, 17(4): 811-821.
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    Ten C W, Liu C C, Manimaran G. Vulnerability assessment of cybersecurity for SCADA systems. IEEE Transactions on Power Systems, 2008, 23(4): 1836-1846.
    [17]
    Befekadu G K, Gupta V, Antsaklis P J. Risk-sensitive control under Markov modulated denial-of-service (DoS) attack strategies. IEEE Transactions on Automatic Control, 2015, 60(12): 3299-3304.
    [18]
    Zargar S T, Joshi J, Tipper D. A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Communications Surveys & Tutorials, 2013, 15(4): 2046-2069.
    [19]
    Loukas G,Öke G. Protection against denial of service attacks: A survey. The Computer Journal, 2010, 53(7): 1020-1037.
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    [21]
    Lai S, Chen B, Li T, et al. Packet-based state feedback control under DoS attacks in cyber-physical systems. IEEE Transactions on Circuits and Systems II: Express Briefs, 2018, 66(8): 1421-1425.
    [22]
    Lu A Y, Yang G H. Stability analysis for cyber-physical systems under denial-of-service attacks. IEEE Transactions on Cybernetics(Access), 2020: 1-10.
    [23]
    Liu Y. Secure control of networked switched systems with random DoS attacks via event-triggered approach. International Journal of Control, Automation and Systems, 2020,18(5): 1-8.
    [24]
    Yang C, Yang W, Shi H. DoS attack in centralised sensor network against state estimation. IET Control Theory & Applications, 2018, 12(9): 1244-1253.
    [25]
    Zhu Y, Zheng W X. Observer-based control for cyber-physical systems with periodic DoS attacks via a cyclic switching strategy. IEEE Transactions on Automatic Control, 2019,65(8): 3714-3721.
    [26]
    Hu S, Yue D, Xie X, et al. Resilient event-triggered controller synthesis of networked control systems under periodic DoS jamming attacks. IEEE Transactions on Cybernetics, 2018, 49(12): 4271-4281.
    [27]
    Tian E, Wang X, Peng C. Probabilistic-constrained distributed filtering for a class of nonlinear stochastic systems subject to periodic DoS attacks. IEEE Transactions on Circuits and Systems I, 2020, 67(12):5369-5379.
    [28]
    Yue M, Wu Z, Wang J. Detecting LDoS attack bursts based on queue distribution. IET Information Security, 2019, 13(3): 285-292.
    [29]
    Guo L, Yu H, Hao F. Event-triggered control for stochastic networked control systems against denial-of-Service attacks. Information Sciences, 2020, 527:51-69.
    [30]
    Zhao H, Niu Y, Zhao J. Event-triggered sliding mode control of uncertain switched systems under denial-of-service attacks. Journal of the Franklin Institute, 2019, 356(18): 11414-11433.
    [31]
    Su L, Ye D. A cooperative detection and compensation mechanism against denial-of-service attack for cyber-physical systems. Information Sciences, 2018, 444: 122-134.
    [32]
    Lalropuia K C, Gupta V. Modeling cyber-physical attacks based on stochastic game and Markov processes. Reliability Engineering & System Safety, 2019, 181: 28-37.
    [33]
    Ni H, Xu Z, Cheng J, et al. Robust stochastic sampled-data-based output consensus of heterogeneous multi-agent systems subject to random DoS attack: A Markovian jumping system approach. International Journal of Control, Automation and Systems, 2019, 17(7): 1687-1698.
    [34]
    Sun Y C, Yang G H. Event-triggered resilient control for cyber-physical systems under asynchronous DoS attacks. Information Sciences, 2018, 465: 340-352.
    [35]
    Yuan H, Xia Y, Yang H. Resilient state estimation of cyber-physical system with multichannel transmission under DoS attack. IEEE Transactions on Systems, Man, and Cybernetics: Systems(Access), 2020: 1-12.
    [36]
    Sun Y C, Yang G H. Periodic event-triggered resilient control for cyber-physical systems under denial-of-service attacks. Journal of the Franklin Institute, 2018, 355(13): 5613-5631.
    [37]
    Lu A Y, Yang G H. Input-to-state stabilizing control for cyber-physical systems with multiple transmission channels under denial of service. IEEE Transactions on Automatic Control, 2017, 63(6): 1813-1820.
    [38]
    Feng S, Tesi P. Resilient control under denial-of-service: Robust design. Automatica, 2017, 79: 42-51.
    [39]
    Liu J, Wang Y, Cao J, et al. Secure adaptive-event-triggered filter design with input constraint and hybrid cyber attack. IEEE Transactions on Cybernetics, 2020: 1-11.
    [40]
    Gao H, Meng X, Chen T. Stabilization of networked control systems with a new delay characterization. IEEE Transactions on Automatic Control, 2008, 53(9): 2142-2148.
    [41]
    Li Y, Liu Y. Stability of solutions of sinular systems with delay. Control Theory and Applications, 1998,15(4): 542-550.
    [42]
    El Ghaoui L, Oustry F, AitRami M. A cone complementarity linearization algorithm for static output-feedback and related problems. IEEE Transactions on Automatic Control, 1997, 42(8): 1171-1176.
    [43]
    Xiong J, Lam J. Stabilization of linear systems over networks with bounded packet loss. Automatica, 2007, 43(1): 80-87.
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    [1]
    Park P, Ergen S C, Fischione C, et al.Wireless network design for control systems: A survey. IEEE Communications Surveys & Tutorials, 2017, 20(2): 978-1013.
    [2]
    Zhang D, Shi P, Wang Q G, et al. Analysis and synthesis of networked control systems: A survey of recent advances and challenges. ISA Transactions, 2017, 66: 376-392.
    [3]
    Zhang X M, Han Q L, Yu X. Survey on recent advances in networked control systems. IEEE Transactions on Industrial Informatics, 2015, 12(5): 1740-1752.
    [4]
    Ge X, Yang F, Han Q L. Distributed networked control systems: A brief overview. Information Sciences, 2017, 380: 117-131.
    [5]
    Rouamel M, Gherbi S, Bourahala F. Robust stability and stabilization of networked control systems with stochastic time-varying network induced delays. Transactions of the Institute of Measurement and Control, 2020, 42(10): 1782-1796.
    [6]
    Li Y, Liu G P, Sun S, et al. Prediction-based approach to finite-time stabilization of networked control systems with time delays and data packet dropouts. Neurocomputing, 2019, 329: 320-328.
    [7]
    Zhang Z, Zheng W, Xie P, et al.H-infinity stability analysis and output feedback control for fuzzy stochastic networked control systems with time-varying communication delays and multipath packet dropouts. Neural Computing and Applications, 2020: 1-19.
    [8]
    Zhao Y B, He J T, Zhu Q H, et al. Classification-based control for wireless networked control systems with lossy multipacket transmission. IEEJ Transactions on Electrical and Electronic Engineering, 2019, 14(11): 1667-1672.
    [9]
    Zhao Y B, Huang T, Kang Y, et al. Stochastic stabilization of wireless networked control systems with lossy multi-packet transmission. IET Control Theory & Applications, 2018, 13(4): 594-601.
    [10]
    Ding D, Han Q L, Xiang Y, et al. A survey on security control and attack detection for industrial cyber-physical systems. Neurocomputing, 2018, 275: 1674-1683.
    [11]
    Cetinkaya A, Ishii H, Hayakawa T. An overview on denial-of-service attacks in control systems: Attack models and security analyses. Entropy, 2019, 21(2): 210.
    [12]
    Li M, Chen Y. Challenging research for networked control systems: A survey. Transactions of the Institute of Measurement and Control, 2019, 41(9): 2400-2418.
    [13]
    Mahmoud M S, Hamdan M M, Baroudi U A. Modeling and control of cyber-physical systems subject to cyber attacks: A survey of recent advances and challenges. Neurocomputing, 2019, 338: 101-115.
    [14]
    Sandberg H, Amin S, Johansson K H. Cyberphysical security in networked control systems: An introduction to the issue. IEEE Control Systems Magazine, 2015, 35(1): 20-23.
    [15]
    Shen Y, Zhang W, Ni H, et al. Guaranteed cost control of networked control systems with DoS attack and time-varying delay. International Journal of Control, Automation and Systems, 2019, 17(4): 811-821.
    [16]
    Ten C W, Liu C C, Manimaran G. Vulnerability assessment of cybersecurity for SCADA systems. IEEE Transactions on Power Systems, 2008, 23(4): 1836-1846.
    [17]
    Befekadu G K, Gupta V, Antsaklis P J. Risk-sensitive control under Markov modulated denial-of-service (DoS) attack strategies. IEEE Transactions on Automatic Control, 2015, 60(12): 3299-3304.
    [18]
    Zargar S T, Joshi J, Tipper D. A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Communications Surveys & Tutorials, 2013, 15(4): 2046-2069.
    [19]
    Loukas G,Öke G. Protection against denial of service attacks: A survey. The Computer Journal, 2010, 53(7): 1020-1037.
    [20]
    Wood A D, Stankovic J A. Denial of service in sensor networks. computer, 2002, 35(10): 54-62.
    [21]
    Lai S, Chen B, Li T, et al. Packet-based state feedback control under DoS attacks in cyber-physical systems. IEEE Transactions on Circuits and Systems II: Express Briefs, 2018, 66(8): 1421-1425.
    [22]
    Lu A Y, Yang G H. Stability analysis for cyber-physical systems under denial-of-service attacks. IEEE Transactions on Cybernetics(Access), 2020: 1-10.
    [23]
    Liu Y. Secure control of networked switched systems with random DoS attacks via event-triggered approach. International Journal of Control, Automation and Systems, 2020,18(5): 1-8.
    [24]
    Yang C, Yang W, Shi H. DoS attack in centralised sensor network against state estimation. IET Control Theory & Applications, 2018, 12(9): 1244-1253.
    [25]
    Zhu Y, Zheng W X. Observer-based control for cyber-physical systems with periodic DoS attacks via a cyclic switching strategy. IEEE Transactions on Automatic Control, 2019,65(8): 3714-3721.
    [26]
    Hu S, Yue D, Xie X, et al. Resilient event-triggered controller synthesis of networked control systems under periodic DoS jamming attacks. IEEE Transactions on Cybernetics, 2018, 49(12): 4271-4281.
    [27]
    Tian E, Wang X, Peng C. Probabilistic-constrained distributed filtering for a class of nonlinear stochastic systems subject to periodic DoS attacks. IEEE Transactions on Circuits and Systems I, 2020, 67(12):5369-5379.
    [28]
    Yue M, Wu Z, Wang J. Detecting LDoS attack bursts based on queue distribution. IET Information Security, 2019, 13(3): 285-292.
    [29]
    Guo L, Yu H, Hao F. Event-triggered control for stochastic networked control systems against denial-of-Service attacks. Information Sciences, 2020, 527:51-69.
    [30]
    Zhao H, Niu Y, Zhao J. Event-triggered sliding mode control of uncertain switched systems under denial-of-service attacks. Journal of the Franklin Institute, 2019, 356(18): 11414-11433.
    [31]
    Su L, Ye D. A cooperative detection and compensation mechanism against denial-of-service attack for cyber-physical systems. Information Sciences, 2018, 444: 122-134.
    [32]
    Lalropuia K C, Gupta V. Modeling cyber-physical attacks based on stochastic game and Markov processes. Reliability Engineering & System Safety, 2019, 181: 28-37.
    [33]
    Ni H, Xu Z, Cheng J, et al. Robust stochastic sampled-data-based output consensus of heterogeneous multi-agent systems subject to random DoS attack: A Markovian jumping system approach. International Journal of Control, Automation and Systems, 2019, 17(7): 1687-1698.
    [34]
    Sun Y C, Yang G H. Event-triggered resilient control for cyber-physical systems under asynchronous DoS attacks. Information Sciences, 2018, 465: 340-352.
    [35]
    Yuan H, Xia Y, Yang H. Resilient state estimation of cyber-physical system with multichannel transmission under DoS attack. IEEE Transactions on Systems, Man, and Cybernetics: Systems(Access), 2020: 1-12.
    [36]
    Sun Y C, Yang G H. Periodic event-triggered resilient control for cyber-physical systems under denial-of-service attacks. Journal of the Franklin Institute, 2018, 355(13): 5613-5631.
    [37]
    Lu A Y, Yang G H. Input-to-state stabilizing control for cyber-physical systems with multiple transmission channels under denial of service. IEEE Transactions on Automatic Control, 2017, 63(6): 1813-1820.
    [38]
    Feng S, Tesi P. Resilient control under denial-of-service: Robust design. Automatica, 2017, 79: 42-51.
    [39]
    Liu J, Wang Y, Cao J, et al. Secure adaptive-event-triggered filter design with input constraint and hybrid cyber attack. IEEE Transactions on Cybernetics, 2020: 1-11.
    [40]
    Gao H, Meng X, Chen T. Stabilization of networked control systems with a new delay characterization. IEEE Transactions on Automatic Control, 2008, 53(9): 2142-2148.
    [41]
    Li Y, Liu Y. Stability of solutions of sinular systems with delay. Control Theory and Applications, 1998,15(4): 542-550.
    [42]
    El Ghaoui L, Oustry F, AitRami M. A cone complementarity linearization algorithm for static output-feedback and related problems. IEEE Transactions on Automatic Control, 1997, 42(8): 1171-1176.
    [43]
    Xiong J, Lam J. Stabilization of linear systems over networks with bounded packet loss. Automatica, 2007, 43(1): 80-87.
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