A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 9 Issue 5
May  2022

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
W. L. Duo, M. C. Zhou, and A. Abusorrah, “A survey of cyber attacks on cyber physical systems: Recent advances and challenges,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 784–800, May 2022. doi: 10.1109/JAS.2022.105548
Citation: W. L. Duo, M. C. Zhou, and A. Abusorrah, “A survey of cyber attacks on cyber physical systems: Recent advances and challenges,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 784–800, May 2022. doi: 10.1109/JAS.2022.105548

A Survey of Cyber Attacks on Cyber Physical Systems: Recent Advances and Challenges

doi: 10.1109/JAS.2022.105548
Funds:  This work was supported by Institutional Fund Projects (IFPNC-001-135-2020). Therefore, authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia
More Information
  • A cyber physical system (CPS) is a complex system that integrates sensing, computation, control and networking into physical processes and objects over Internet. It plays a key role in modern industry since it connects physical and cyber worlds. In order to meet ever-changing industrial requirements, its structures and functions are constantly improved. Meanwhile, new security issues have arisen. A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems, and thus has gained increasing attention from researchers and practitioners. This paper presents a survey of state-of-the-art results of cyber attacks on cyber physical systems. First, as typical system models are employed to study these systems, time-driven and event-driven systems are reviewed. Then, recent advances on three types of attacks, i.e., those on availability, integrity, and confidentiality are discussed. In particular, the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and defenders. Namely, both attack and defense strategies are discussed based on different system models. Some challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

     

  • loading
  • [1]
    Y. Lu, “Cyber physical system (CPS)-based industry 4.0: A survey,” J. Ind. Int. Manage., vol. 2, no. 3, p. 1750014, Sep. 2017.
    [2]
    H. F. Fan, M. Ni, L. L. Zhao, and M. L. Li, “Review of cyber physical system and cyber attack modeling,” in Proc. 12th IEEE PES Asia-Pacific Power and Energy Eng. Conf., Nanjing, China, 2020, pp. 1 – 5.
    [3]
    L. P. Chang, T. W. Kuo, C. Gill, and J. Nakazawa, “Introduction to the special issue on real-time, embedded and cyber-physical systems,” ACM Trans. Embed. Comput. Syst., vol. 13, no. 5S, p. 155, Nov. 2014.
    [4]
    G. Franze, G. Fortino, X. H. Cao, G. M. L. Sarne, and Z. Song, “Resilient control in large-scale networked cyber-physical systems: Guest editorial,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1201–1203, Sept. 2020. doi: 10.1109/JAS.2020.1003327
    [5]
    S. Karnouskos, “Cyber-physical systems in the SmartGrid,” in Proc. 9th IEEE Int. Conf. Industrial Informatics, Lisbon, Portugal, 2011, pp. 20−23.
    [6]
    Y. Zhang, M. K. Qiu, C. W. Tsai, M. M. Hassan, and A. Alamri, “Health-CPS: Healthcare cyber-physical system assisted by cloud and big data,” IEEE Syst. J., vol. 11, no. 1, pp. 88–95, Mar. 2017. doi: 10.1109/JSYST.2015.2460747
    [7]
    Y. Liu, Y. Peng, B. L. Wang, S. R. Yao, and Z. H. Liu, “Review on cyber-physical systems,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 27–40, Jan. 2017. doi: 10.1109/JAS.2017.7510349
    [8]
    M. Muthuppalaniappan and K. Stevenson, “Healthcare cyber-attacks and the COVID-19 pandemic: An urgent threat to global health,” Int. J. Qual. Health Care, vol. 33, no. 1, p. mzaa117, Feb. 2021.
    [9]
    A. Humayed, J. Q. Lin, F. J. Li, and B. Luo, “Cyber-physical systems security — A survey,” IEEE Internet Things J., vol. 4, no. 6, pp. 1802–1831, Dec. 2017. doi: 10.1109/JIOT.2017.2703172
    [10]
    D. R. Ding, Q. L. Han, Y. Xiang, X. H. Ge, and X. M. Zhang, “A survey on security control and attack detection for industrial cyber-physical systems,” Neurocomputing, vol. 275, pp. 1674–1683, 2018. doi: 10.1016/j.neucom.2017.10.009
    [11]
    J. Giraldo, D. Urbina, A. Cardenas, J. Valente, M. Faisal, J. Ruths, N. O. Tippenhauer, H. Sandberg, and R. Candell, “A survey of physics-based attack detection in cyber-physical systems,” ACM Comput. Surv., vol. 51, no. 4, pp. 1–36, Jul. 2018.
    [12]
    M. S. Mahmoud, M. M. Hamdan, and U. A. Baroudi, “Modeling and control of cyber-physical systems subject to cyber attacks: A survey of recent advances and challenges,” Neurocomputing, vol. 338, pp. 101–115, Apr. 2019. doi: 10.1016/j.neucom.2019.01.099
    [13]
    A. Rashidinejad, B. Wetzels, M. Reniers, L. Y. Lin, Y. T. Zhu, and R. Su, “Supervisory control of discrete-event systems under attacks: An overview and outlook,” in Proc. 18th European Control Conf., Naples, Italy, 2019, pp. 1732−1739.
    [14]
    S. M. Dibaji, M. Pirani, D. B. Flamholz, A. M. Annaswamy, K. H. Johansson, and A. Chakrabortty, “A systems and control perspective of CPS security,” Annu. Rev. Control, vol. 47, pp. 394–411, Jan. 2019. doi: 10.1016/j.arcontrol.2019.04.011
    [15]
    S. Singh, N. Yadav, and P. K. Chuarasia, “A review on cyber physical system attacks: Issues and challenges,” in Proc. Int. Conf. Communication and Signal Processing, Chennai, India, 2020, pp. 1133−1138.
    [16]
    L. W. Cao, X. N. Jiang, Y. M. Zhao, S. G. Wang, D. You, and X. L. Xu, “A survey of network attacks on cyber-physical systems,” IEEE Access, vol. 8, pp. 44219–44227, Mar. 2020. doi: 10.1109/ACCESS.2020.2977423
    [17]
    S. Tan, J. M. Guerrero, P. L. Xie, R. K. Han, and J. C. Vasquez, “Brief survey on attack detection methods for cyber-physical systems,” IEEE Syst. J., vol. 14, no. 4, pp. 5329–5339, Dec. 2020. doi: 10.1109/JSYST.2020.2991258
    [18]
    D. Zhang, Q. G. Wang, G. Feng, Y. Shi, and A. V. Vasilakos, “A survey on attack detection, estimation and control of industrial cyber–physical systems,” ISA Trans., vol. 116, pp. 1–6, Oct. 2021. doi: 10.1016/j.isatra.2021.01.036
    [19]
    D. R. Ding, Q. L. Han, X. H. Ge, and J. Wang, “Secure state estimation and control of cyber-physical systems: A survey,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 1, pp. 176–190, Jan. 2021. doi: 10.1109/TSMC.2020.3041121
    [20]
    H. Zhang, Y. F. Qi, and J. F. Wu, “Optimal jamming power allocation against remote state estimation,” in Proc. American Control Conf., Seattle, USA, 2017, pp. 1660−1665.
    [21]
    Y. Zhao, Z. Chen, C. J. Zhou, Y. C. Tian, and Y. Q. Qin, “Passivity-based robust control against quantified false data injection attacks in cyber-physical systems,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 8, pp. 1440–1450, Aug. 2021. doi: 10.1109/JAS.2021.1004012
    [22]
    Y. Z. Li, D. E. Quevedo, S. Dey, and L. Shi, “SINR-based DoS attack on remote state estimation: A game-theoretic approach,” IEEE Trans. Control Netw. Syst., vol. 4, no. 3, pp. 632–642, Sep. 2017. doi: 10.1109/TCNS.2016.2549640
    [23]
    H. Zhang, Y. F. Qi, J. F. Wu, L. K. Fu, and L. D. He, “DoS attack energy management against remote state estimation,” IEEE Trans. Control Netw. Syst., vol. 5, no. 1, pp. 383–394, Mar. 2018. doi: 10.1109/TCNS.2016.2614099
    [24]
    J. H. Zhang, J. T. Sun, and H. Lin, “Optimal DoS attack schedules on remote state estimation under multi-sensor round-robin protocol,” Automatica, vol. 127, p. 109517, May 2021.
    [25]
    X. H. Ge, Q. L. Han, M. Y. Zhong, and X. M. Zhang, “Distributed Krein space-based attack detection over sensor networks under deception attacks,” Automatica, vol. 109, p. 108557, Nov. 2019.
    [26]
    G. K. Befekadu, V. Gupta, and P. J. Antsaklis, “Risk-sensitive control under Markov modulated denial-of-service (DoS) attack strategies,” IEEE Trans. Autom. Control, vol. 60, no. 12, pp. 3299–3304, Dec. 2015. doi: 10.1109/TAC.2015.2416926
    [27]
    Y. Wang, Y. T. Li, Z. H. Yu, N. Q. Wu, and Z. W. Li, “Supervisory control of discrete-event systems under external attacks,” Inf. Sci., vol. 562, pp. 398–413, Jul. 2021. doi: 10.1016/j.ins.2021.03.033
    [28]
    P. J. Ramadge and W. M. Wonham, “Supervisory control of a class of discrete event processes,” SIAM J. Control Optim., vol. 25, no. 1, pp. 206–230, Jan. 1987. doi: 10.1137/0325013
    [29]
    M. Wakaiki, P. Tabuada, and J. P. Hespanha, “Supervisory control of discrete-event systems under attacks,” Dyn. Games Appl., vol. 9, no. 4, pp. 965–983, Dec. 2019. doi: 10.1007/s13235-018-0285-3
    [30]
    W. Jiang, L. Wen, J. Y. Zhan, and K. Jiang, “Design optimization of confidentiality-critical cyber physical systems with fault detection,” J. Syst. Archit., vol. 107, p. 101739, Aug. 2020.
    [31]
    W. Yang, Z. Q. Zheng, G. R. Chen, Y. Tang, and X. F. Wang, “Security analysis of a distributed networked system under eavesdropping attacks,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 67, no. 7, pp. 1254–1258, Jul. 2020. doi: 10.1109/TCSII.2019.2928558
    [32]
    L. H. Peng, X. H. Cao, C. Y. Sun, Y. Cheng, and S. Jin, “Energy efficient jamming attack schedule against remote state estimation in wireless cyber-physical systems,” Neurocomputing, vol. 272, pp. 571–583, Jan. 2018. doi: 10.1016/j.neucom.2017.07.036
    [33]
    H. Zhang, P. Cheng, L. Shi, and J. M. Chen, “Optimal denial-of-service attack scheduling with energy constraint,” IEEE Trans. Autom. Control, vol. 60, no. 11, pp. 3023–3028, Nov. 2015. doi: 10.1109/TAC.2015.2409905
    [34]
    H. Zhang, P. Cheng, L. Shi, and J. M. Chen, “Optimal DoS attack scheduling in wireless networked control system,” IEEE Trans. Control Syst. Technol., vol. 24, no. 3, pp. 843–852, May 2016. doi: 10.1109/TCST.2015.2462741
    [35]
    J. H. Qin, M. L. Li, L. Shi, and X. H. Yu, “Optimal denial-of-service attack scheduling with energy constraint over packet-dropping networks,” IEEE Trans. Autom. Control, vol. 63, no. 6, pp. 1648–1663, Jun. 2018. doi: 10.1109/TAC.2017.2756259
    [36]
    J. H. Qin, M. L. Li, L. Shi, and Y. Kang, “Optimal denial-of-service attack energy management over an SINR-based network,” arXiv: 1810.02558, 2018.
    [37]
    B. B. Li, Y. H. Wu, J. R. Song, R. X. Lu, T. Li, and L. Zhao, “DeepFed: Federated deep learning for intrusion detection in industrial cyber-physical systems,” IEEE Trans. Ind. Inf., vol. 17, no. 8, pp. 5615–5624, Aug. 2021. doi: 10.1109/TII.2020.3023430
    [38]
    L. Liu, O. De Vel, Q. L. Han, J. Zhang, and Y. Xiang, “Detecting and preventing cyber insider threats: A survey,” IEEE Commun. Surv. Tutor., vol. 20, no. 2, pp. 1397–1417, Feb. 2018. doi: 10.1109/COMST.2018.2800740
    [39]
    F. O. Olowononi, D. B. Rawat, and C. M. Liu, “Resilient machine learning for networked cyber physical systems: A survey for machine learning security to securing machine learning for CPS,” IEEE Commun. Surv. Tutor., vol. 23, no. 1, pp. 524–552, Nov. 2021. doi: 10.1109/COMST.2020.3036778
    [40]
    S. Ramesh, C. Yaashuwanth, K. Prathibanandhi, A. R. Basha, and T. Jayasankar, “An optimized deep neural network based DoS attack detection in wireless video sensor network,” J. Ambient Intell. Hum. Comput., 2021, DOI: 10.1007/s12652-020-02763-9.
    [41]
    X. M. Zhang, Q. L. Han, X. H. Ge, D. R. Ding, L. Ding, D. Yue, and C. Peng, “Networked control systems: A survey of trends and techniques,” IEEE/CAA J. Autom. Sinic, vol. 7, no. 1, pp. 1–17, Jan. 2020. doi: 10.1109/JAS.2019.1911861
    [42]
    C. Peng, J. C. Li, and M. R. Fei, “Resilient event-triggering H load frequency control for multi-area power systems with energy-limited DoS attacks,” IEEE Trans. Power Syst., vol. 32, no. 5, pp. 4110–4118, Sep. 2017. doi: 10.1109/TPWRS.2016.2634122
    [43]
    Y. C. Sun and G. H. Yang, “Event-triggered resilient control for cyber-physical systems under asynchronous DoS attacks,” Inf. Sci., vol. 465, pp. 340–352, Oct. 2018. doi: 10.1016/j.ins.2018.07.030
    [44]
    W. H. M. H. Heemels, M. C. F. Donkers, and A. R. Teel, “Periodic event-triggered control for linear systems,” IEEE Trans. Autom. Control, vol. 58, no. 4, pp. 847–861, Apr. 2013. doi: 10.1109/TAC.2012.2220443
    [45]
    D. Yue, E. G. Tian, and Q. L. Han, “A delay system method for designing event-triggered controllers of networked control systems,” IEEE Trans. Autom. Control, vol. 58, no. 2, pp. 475–481, Feb. 2013. doi: 10.1109/TAC.2012.2206694
    [46]
    S. L. Hu, D. Yue, Q. L. Han, X. P. Xie, X. L. Chen, and C. X. Dou, “Observer-based event-triggered control for networked linear systems subject to denial-of-service attacks,” IEEE Trans. Cybern., vol. 50, no. 5, pp. 1952–1964, May 2020. doi: 10.1109/TCYB.2019.2903817
    [47]
    S. L. Hu, D. Yue, X. L. Chen, Z. H. Cheng, and X. P. Xie, “Resilient H filtering for event-triggered networked systems under nonperiodic DoS jamming attacks,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 3, pp. 1392–1403, Mar. 2021.
    [48]
    X. L. Chen, Y. G. Wang, and S. L. Hu, “Event-based robust stabilization of uncertain networked control systems under quantization and denial-of-service attacks,” Inf. Sci., vol. 459, pp. 369–386, Aug. 2018. doi: 10.1016/j.ins.2018.05.019
    [49]
    H. S. Foroush and S. Martínez, “On event-triggered control of linear systems under periodic denial-of-service jamming attacks,” in Proc. IEEE 51st IEEE Conf. Decision and Control, Maui, USA, 2012, pp. 2551−2556.
    [50]
    H. S. Foroush and S. Martinez, “On triggering control of single-input linear systems under pulse-width modulated DoS signals,” SIAM J. Control Optim., vol. 54, no. 6, pp. 3084–3105, Jan. 2016. doi: 10.1137/16M1069390
    [51]
    D. J. Thuente and M. Acharya, “Intelligent jamming in wireless networks with applications to 802.11b and other networks,” in Proc. IEEE Conf. Military Communications, Washington, USA: IEEE, 2006, pp. 1075−1081.
    [52]
    N. Zhao, P. Shi, W. Xing, and J. Chambers, “Observer-based event-triggered approach for stochastic networked control systems under denial of service attacks,” IEEE Trans. Control Netw. Syst., vol. 8, no. 1, pp. 158–167, Mar. 2021. doi: 10.1109/TCNS.2020.3035760
    [53]
    L. Guo, H. Yu, and F. Hao, “Event-triggered control for stochastic networked control systems against Denial-of-Service attacks,” Inf. Sci., vol. 527, pp. 51–69, Jul. 2020. doi: 10.1016/j.ins.2020.03.045
    [54]
    M. Sathishkumar and Y. C. Liu, “Resilient event-triggered fault-tolerant control for networked control systems with randomly occurring nonlinearities and DoS attacks,” Int. J. Syst. Sci., vol. 51, no. 14, pp. 2712–2732, Aug. 2020. doi: 10.1080/00207721.2020.1801880
    [55]
    S. Feng and P. Tesi, “Resilient control under denial-of-service: Robust design,” Automatica, vol. 79, pp. 42–51, May 2017. doi: 10.1016/j.automatica.2017.01.031
    [56]
    H. T. Sun, C. Peng, T. C. Yang, H. Zhang, and W. L. He, “Resilient control of networked control systems with stochastic denial of service attacks,” Neurocomputing, vol. 270, pp. 170–177, Dec. 2017. doi: 10.1016/j.neucom.2017.02.093
    [57]
    H. H. Yuan and Y. Q. Xia, “Resilient strategy design for cyber-physical system under DoS attack over a multi-channel framework,” Inf. Sci., vol. 454−455, pp. 312–327, Jul. 2018. doi: 10.1016/j.ins.2018.04.082
    [58]
    X. M. Zhang, Q. L. Han, X. H. Ge, and L. Ding, “Resilient control design based on a sampled-data model for a class of networked control systems under denial-of-service attacks,” IEEE Trans. Cybern., vol. 50, no. 8, pp. 3616–3626, Aug. 2020. doi: 10.1109/TCYB.2019.2956137
    [59]
    G. K. Befekadu, V. Gupta, and P. J. Antsaklis, “Risk-sensitive control under a class of denial-of-service attack models,” in Proc. American Control Conf., San Francisco, CA, USA, 2011, pp. 643−648.
    [60]
    G. K. Befekadu, V. Gupta, and P. J. Antsaklis, “Risk-sensitive control under a Markov modulated denial-of-service attack model,” in Proc. 50th IEEE Conf. Decision and Control and European Control Conf., Orlando, FL, USA, 2011, pp. 5714−5719.
    [61]
    W. Yang, Y. Zhang, G. R. Chen, C. Yang, and L. Shi, “Distributed filtering under false data injection attacks,” Automatica, vol. 102, pp. 34–44, Apr. 2019. doi: 10.1016/j.automatica.2018.12.027
    [62]
    T. Y. Zhang and D. Ye, “Distributed secure control against denial-of-service attacks in cyber-physical systems based on K-connected communication topology,” IEEE Trans. Cybern., vol. 50, no. 7, pp. 3094–3103, Jul. 2020. doi: 10.1109/TCYB.2020.2973303
    [63]
    A. Y. Lu and G. H. Yang, “Distributed consensus control for multi-agent systems under denial-of-service,” Inf. Sci., vol. 439-440, pp. 95–107, May 2018. doi: 10.1016/j.ins.2018.02.008
    [64]
    Y. Xu, M. Fang, P. Shi, and Z. G. Wu, “Event-based secure consensus of mutiagent systems against DoS attacks,” IEEE Trans. Cybern., vol. 50, no. 8, pp. 3468–3476, Aug. 2020. doi: 10.1109/TCYB.2019.2918402
    [65]
    D. Zhang, Y. P. Shen, S. Q. Zhou, X. W. Dong, and L. Yu, “Distributed secure platoon control of connected vehicles subject to DoS attack: Theory and application,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 11, pp. 7269–7278, Nov. 2021. doi: 10.1109/TSMC.2020.2968606
    [66]
    C. W. Wu, L. G. Wu, J. X. Liu, and Z. X. Jiang, “Active defense-based resilient sliding mode control under denial-of-service attacks,” IEEE Trans. Inf. Foren. Sec., vol. 15, pp. 237–249, May 2020. doi: 10.1109/TIFS.2019.2917373
    [67]
    C. De Persis and P. Tesi, “Input-to-state stabilizing control under denial-of-service,” IEEE Trans. Autom. Control, vol. 60, no. 11, pp. 2930–2944, Nov. 2015. doi: 10.1109/TAC.2015.2416924
    [68]
    G. Y. Wu, J. Sun, and J. Chen, “Optimal data injection attacks in cyber-physical systems,” IEEE Trans. Cybern., vol. 48, no. 12, pp. 3302–3312, Dec. 2018. doi: 10.1109/TCYB.2018.2846365
    [69]
    Y. Chen, S. Kar, and J. M. F. Moura, “Optimal attack strategies subject to detection constraints against cyber-physical systems,” IEEE Trans. Control Netw. Syst., vol. 5, no. 3, pp. 1157–1168, Sep. 2018. doi: 10.1109/TCNS.2017.2690399
    [70]
    Y. L. Mo and B. Sinopoli, “On the performance degradation of cyber-physical systems under stealthy integrity attacks,” IEEE Trans. Autom. Control, vol. 61, no. 9, pp. 2618–2624, Sep. 2016. doi: 10.1109/TAC.2015.2498708
    [71]
    Y. L. Mo and B. Sinopoli, “Secure control against replay attacks,” in Proc. 47th Ann. Allerton Conf. Communication, Control, and Computing, Monticello, IL, USA, 2009, pp. 911−918.
    [72]
    Y. L. Mo and B. Sinopoli, “Integrity attacks on cyber-physical systems,” in Proc. 1st Int. Conf. High Confidence Networked Systems, Beijing, China, 2012, pp. 47−54.
    [73]
    J. P. Hao, R. J. Piechocki, D. Kaleshi, W. H. Chin, and Z. Fan, “Sparse malicious false data injection attacks and defense mechanisms in smart grids,” IEEE Trans. Ind. Inf., vol. 11, no. 5, pp. 1–12, Oct. 2015. doi: 10.1109/TII.2015.2475695
    [74]
    Z. Y. Guo, D. W. Shi, K. H. Johansson, and L. Shi, “Optimal linear cyber-attack on remote state estimation,” IEEE Trans. Control Netw. Syst., vol. 4, no. 1, pp. 4–13, Mar. 2017. doi: 10.1109/TCNS.2016.2570003
    [75]
    S. Wu, Z. Y. Guo, D. W. Shi, K. H. Johansson, and L. Shi, “Optimal innovation-based deception attack on remote state estimation,” in Proc. American Control Conf., Seattle, WA, USA, 2018, pp. 3017−3022.
    [76]
    E. Mousavinejad, F. W. Yang, Q. L. Han, and L. Vlacic, “A novel cyber attack detection method in networked control systems,” IEEE Trans. Cybern., vol. 48, no. 11, pp. 3254–3264, Nov. 2018. doi: 10.1109/TCYB.2018.2843358
    [77]
    C. Z. Bai, F. Pasqualetti, and V. Gupta, “Security in stochastic control systems: Fundamental limitations and performance bounds,” in Proc. American Control Conf., Chicago, IL, USA, 2015, pp. 195−200.
    [78]
    C. Z. Bai, F. Pasqualetti, and V. Gupta, “Data-injection attacks in stochastic control systems: Detectability and performance tradeoffs,” Automatica, vol. 82, pp. 251–260, Aug. 2017. doi: 10.1016/j.automatica.2017.04.047
    [79]
    E. Kung, S. Dey, and L. Shi, “The performance and limitations of ϵ-stealthy attacks on higher order systems,” IEEE Trans. Autom. Control, vol. 62, no. 2, pp. 941–947, Feb. 2017. doi: 10.1109/TAC.2016.2565379
    [80]
    Q. R. Zhang, K. Liu, Y. Q. Xia, and A. Y. Ma, “Optimal stealthy deception attack against cyber-physical systems,” IEEE Trans. Cybern., vol. 50, no. 9, pp. 3963–3972, Sep. 2020. doi: 10.1109/TCYB.2019.2912622
    [81]
    R. Su, “Supervisor synthesis to thwart cyber attack with bounded sensor reading alterations,” Automatica, vol. 94, pp. 35–44, Aug. 2018. doi: 10.1016/j.automatica.2018.04.006
    [82]
    R. Meira-Góes, E. Kang, R. H. Kwong, and S. Lafortune, “Synthesis of sensor deception attacks at the supervisory layer of cyber–physical systems,” Automatica, vol. 121, p. 109172, Nov. 2020.
    [83]
    B. B. Li, R. X. Lu, K. K. R. Choo, W. Wang, and S. Luo, “On reliability analysis of smart grids under topology attacks: A stochastic petri net approach,” ACM Trans. Cyber-Phys. Syst., vol. 3, no. 1, pp. 1–25, Jan. 2018.
    [84]
    O. Kosut, L. Y. Jia, R. J. Thomas, and L. Tong, “Malicious data attacks on the smart grid,” IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 645–658, Dec. 2011. doi: 10.1109/TSG.2011.2163807
    [85]
    L. Y. Jia, R. J. Thomas, and L. Tong, “Impacts of malicious data on real-time price of electricity market operations,” in Proc. 45th Hawaii Int. Conf. System Sciences, Maui, HI, USA, 2012, pp. 1907−1914.
    [86]
    L. Xie, Y. L. Mo, and B. Sinopoli, “Integrity data attacks in power market operations,” IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 659–666, Dec. 2011. doi: 10.1109/TSG.2011.2161892
    [87]
    R. L. Deng, G. X. Xiao, and R. X. Lu, “Defending against false data injection attacks on power system state estimation,” IEEE Trans. Ind. Inf., vol. 13, no. 1, pp. 198–207, Feb. 2017. doi: 10.1109/TII.2015.2470218
    [88]
    H. Z. Fang, N. Tian, Y. B. Wang, M. C. Zhou, and M. A. Haile, “Nonlinear Bayesian estimation: From Kalman filtering to a broader horizon,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 401–417, Mar. 2018. doi: 10.1109/JAS.2017.7510808
    [89]
    C. Kwon, W. Y. Liu, and I. Hwang, “Security analysis for cyber-physical systems against stealthy deception attacks,” in Proc. American Control Conf., Washington, DC, USA, 2013, pp. 3344−3349.
    [90]
    Y. Z. Li, L. Shi, and T. W. Chen, “Detection against linear deception attacks on multi-sensor remote state estimation,” IEEE Trans. Control Netw. Syst., vol. 5, no. 3, pp. 846–856, Sep. 2018. doi: 10.1109/TCNS.2017.2648508
    [91]
    Q. Li, B. Shen, Y. R. Liu, and F. E. Alsaadi, “Event-triggered H state estimation for discrete-time stochastic genetic regulatory networks with Markovian jumping parameters and time-varying delays,” Neurocomputing, vol. 174, pp. 912–920, Jan. 2016. doi: 10.1016/j.neucom.2015.10.017
    [92]
    V. Ugrinovskii, “Distributed robust estimation over randomly switching networks using H consensus,” Automatica, vol. 49, no. 1, pp. 160–168, Jan. 2013. doi: 10.1016/j.automatica.2012.09.010
    [93]
    S. Mishra, Y. Shoukry, N. Karamchandani, S. N. Diggavi, and P. Tabuada, “Secure state estimation against sensor attacks in the presence of noise,” IEEE Trans. Control Netw. Syst., vol. 4, no. 1, pp. 49–59, Mar. 2017. doi: 10.1109/TCNS.2016.2606880
    [94]
    K. Liu, H. Guo, Q. R. Zhang, and Y. Q. Xia, “Distributed secure filtering for discrete-time systems under Round-Robin protocol and deception attacks,” IEEE Trans. Cybern., vol. 50, no. 8, pp. 3571–3580, Aug. 2020. doi: 10.1109/TCYB.2019.2897366
    [95]
    L. F. Ma, Z. D. Wang, Q. L. Han, and H. K. Lam, “Variance-constrained distributed filtering for time-varying systems with multiplicative noises and deception attacks over sensor networks,” IEEE Sens. J., vol. 17, no. 7, pp. 2279–2288, Apr. 2017. doi: 10.1109/JSEN.2017.2654325
    [96]
    H. F. Song, D. R. Ding, H. L. Dong, and Q. L. Han, “Distributed maximum correntropy filtering for stochastic nonlinear systems under deception attacks,” IEEE Trans. Cybern., 2020, DOI: 10.1109/TCYB.2020.3016093.
    [97]
    Y. L. Mo, R. Chabukswar, and B. Sinopoli, “Detecting integrity attacks on SCADA systems,” IEEE Trans. Control Syst. Technol., vol. 22, no. 4, pp. 1396–1407, Jul. 2014. doi: 10.1109/TCST.2013.2280899
    [98]
    D. B. Rawat and C. Bajracharya, “Detection of false data injection attacks in smart grid communication systems,” IEEE Signal Process. Lett., vol. 22, no. 10, pp. 1652–1656, Oct. 2015. doi: 10.1109/LSP.2015.2421935
    [99]
    J. Miloševič, T. Tanaka, H. Sandberg, and K. H. Johansson, “Analysis and mitigation of bias injection attacks against a Kalman filter,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 8393–8398, Jul. 2017. doi: 10.1016/j.ifacol.2017.08.1564
    [100]
    A. J. Gallo, M. S. Turan, P. Nahata, F. Boem, T. Parisini, and G. Ferrari-Trecate, “Distributed cyber-attack detection in the secondary control of DC microgrids,” in Proc. European Control Conf., Limassol, Cyprus, 2018, pp. 344−349.
    [101]
    X. Y. Luo, X. Y. Wang, X. Y. Pan, and X. P. Guan, “Detection and isolation of false data injection attack for smart grids via unknown input observers,” IET Gener. Transm. Distrib., vol. 13, no. 8, pp. 1277–1286, Apr. 2019. doi: 10.1049/iet-gtd.2018.5139
    [102]
    X. Y. Wang, X. Y. Luo, Y. Y. Zhang, and X. P. Guan, “Detection and isolation of false data injection attacks in smart grids via nonlinear interval observer,” IEEE Internet Things J., vol. 6, no. 4, pp. 6498–6512, Aug. 2019. doi: 10.1109/JIOT.2019.2916670
    [103]
    Z. Y. Guo, D. W. Shi, K. H. Johansson, and L. Shi, “Worst-case stealthy innovation-based linear attack on remote state estimation,” Automatica, vol. 89, pp. 117–124, 2018. doi: 10.1016/j.automatica.2017.11.018
    [104]
    J. H. Huang, D. W. C. Ho, F. F. Li, W. Yang, and Y. Tang, “Secure remote state estimation against linear man-in-the-middle attacks using watermarking,” Automatica, vol. 121, p. 109182, Nov. 2020.
    [105]
    D. Wang, J. H. Huang, Y. Tang, and F. F. Li, “A watermarking strategy against linear deception attacks on remote state estimation under K-L divergence,” IEEE Trans. Ind. Inf., vol. 17, no. 5, pp. 3273–3281, May 2021. doi: 10.1109/TII.2020.3009874
    [106]
    A. Naha, A. Teixeira, A. Ahlen, and S. Dey, “Quickest detection of deception attacks in networked control systems with physical watermarking,” arXiv: 2101.01466, 2021.
    [107]
    W. L. Chen, J. Hu, Z. H. Wu, X. Y. Yu, and D. Y. Chen, “Finite-time memory fault detection filter design for nonlinear discrete systems with deception attacks,” Int. J. Syst. Sci., vol. 51, no. 8, pp. 1464–1481, May 2020. doi: 10.1080/00207721.2020.1765219
    [108]
    D. R. Ding, Q. L. Han, Z. D. Wang, and X. H. Ge, “Recursive filtering of distributed cyber-physical systems with attack detection,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 10, pp. 6466–6476, Oct. 2021. doi: 10.1109/TSMC.2019.2960541
    [109]
    H. Fawzi, P. Tabuada, and S. Diggavi, “Secure estimation and control for cyber-physical systems under adversarial attacks,” IEEE Trans. Autom. Control, vol. 59, no. 6, pp. 1454–1467, Jun. 2014. doi: 10.1109/TAC.2014.2303233
    [110]
    C. H. Xie and G. H. Yang, “Observer-based attack-resilient control for linear systems against FDI attacks on communication links from controller to actuators,” Int. J. Robust Nonlinear Control, vol. 28, no. 15, pp. 4382–4403, Oct. 2018.
    [111]
    Y. Shoukry and P. Tabuada, “Event-triggered state observers for sparse sensor noise/attacks,” IEEE Trans. Autom. Control, vol. 61, no. 8, pp. 2079–2091, Aug. 2016. doi: 10.1109/TAC.2015.2492159
    [112]
    M. Pajic, I. Lee, and G. J. Pappas, “Attack-resilient state estimation for noisy dynamical systems,” IEEE Trans. Control Netw. Syst., vol. 4, no. 1, pp. 82–92, Mar. 2017. doi: 10.1109/TCNS.2016.2607420
    [113]
    A. Y. Lu and G. H. Yang, “Event-triggered secure observer-based control for cyber-physical systems under adversarial attacks,” Inf. Sci., vol. 420, pp. 96–109, Dec. 2017. doi: 10.1016/j.ins.2017.08.057
    [114]
    D. R. Ding, Q. L. Han, Z. D. Wang, and X. H. Ge, “A survey on model-based distributed control and filtering for industrial cyber-physical systems,” IEEE Trans. Ind. Inf., vol. 15, no. 5, pp. 2483–2499, May 2019. doi: 10.1109/TII.2019.2905295
    [115]
    M. H. Zhu and S. Martínez, “On distributed constrained formation control in operator—vehicle adversarial networks,” Automatica, vol. 49, no. 12, pp. 3571–3582, Dec. 2013. doi: 10.1016/j.automatica.2013.09.031
    [116]
    Y. Liu, H. H. Xin, Z. H. Qu, and D. Q. Gan, “An attack-resilient cooperative control strategy of multiple distributed generators in distribution networks,” IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2923–2932, Nov. 2016. doi: 10.1109/TSG.2016.2542111
    [117]
    A. Bidram, B. Poudel, L. Damodaran, R. Fierro, and J. M. Guerrero, “Resilient and cybersecure distributed control of inverter-based islanded microgrids,” IEEE Trans. Ind. Inf., vol. 16, no. 6, pp. 3881–3894, Jun. 2020. doi: 10.1109/TII.2019.2941748
    [118]
    N. Yassaie, M. Hallajiyan, I. Sharifi, and H. A. Talebi, “Resilient control of multi-microgrids against false data injection attack,” ISA Trans., vol. 110, pp. 238–246, Apr. 2021. doi: 10.1016/j.isatra.2020.10.030
    [119]
    C. Deng, Y. Wang, C. Y. Wen, Y. Xu, and P. F. Lin, “Distributed resilient control for energy storage systems in cyber-physical microgrids,” IEEE Trans. Ind. Inf., vol. 17, no. 2, pp. 1331–1341, Feb. 2020.
    [120]
    C. Deng and G. H. Yang, “Distributed adaptive fault-tolerant control approach to cooperative output regulation for linear multi-agent systems,” Automatica, vol. 103, pp. 62–68, May 2019. doi: 10.1016/j.automatica.2019.01.013
    [121]
    C. Deng, G. H. Yang, and M. J. Er, “Decentralized fault-tolerant MRAC for a class of large-scale systems with time-varying delays and actuator faults,” J. Process Control, vol. 75, pp. 171–186, Mar. 2019. doi: 10.1016/j.jprocont.2018.12.006
    [122]
    W. Ao, Y. D. Song, and C. Y. Wen, “Distributed secure state estimation and control for CPSs under sensor attacks,” IEEE Trans. Cybern., vol. 50, no. 1, pp. 259–269, Jan. 2020. doi: 10.1109/TCYB.2018.2868781
    [123]
    L. K. Carvalho, Y. C. Wu, R. Kwong, and S. Lafortune, “Detection and prevention of actuator enablement attacks in supervisory control systems,” in Proc. 13th Int. Workshop on Discrete Event Systems, Xi’an, China, 2016, pp. 298−305.
    [124]
    L. K. Carvalho, Y. C. Wu, R. Kwong, and S. Lafortune, “Detection and mitigation of classes of attacks in supervisory control systems,” Automatica, vol. 97, pp. 121–133, Nov. 2018. doi: 10.1016/j.automatica.2018.07.017
    [125]
    P. M. Lima, M. V. S. Alves, L. K. Carvalho, and M. V. Moreira, “Security against network attacks in supervisory control systems,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 12333–12338, Jul. 2017. doi: 10.1016/j.ifacol.2017.08.2161
    [126]
    P. M. Lima, L. K. Carvalho, and M. V. Moreira, “Detectable and undetectable network attack security of cyber-physical systems,” IFAC-PapersOnLine, vol. 51, no. 7, pp. 179–185, Jan. 2018. doi: 10.1016/j.ifacol.2018.06.298
    [127]
    P. M. Lima, M. V. S. Alves, L. K. Carvalho, and M. V. Moreira, “Security against communication network attacks of cyber-physical systems,” J. Control. Autom. Electr. Syst., vol. 30, no. 1, pp. 125–135, Feb. 2019. doi: 10.1007/s40313-018-0420-9
    [128]
    R. Meira-Góes, H. Marchand, and S. Lafortune, “Towards resilient supervisors against sensor deception attacks,” in Proc. IEEE 58th Conf. Decision and Control, Nice, France, 2019, pp. 5144−5149.
    [129]
    R. Meira-Góes, S. Lafortune, and H. Marchand, “Synthesis of supervisors robust against sensor deception attacks,” IEEE Trans. Autom. Control, vol. 66, no. 10, pp. 4990–4997, Oct. 2021. doi: 10.1109/TAC.2021.3051459
    [130]
    D. You, S. G. Wang, and C. Seatzu, “A Liveness-enforcing supervisor tolerant to sensor-reading modification attacks,” IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 4, pp. 2398−2411, Apr. 2022.
    [131]
    G. Bertoni, L. Breveglieri, I. Koren, P. Maistri, and V. Piuri, “A parity code based fault detection for an implementation of the advanced encryption standard,” in Proc. 17th IEEE Int. Symp. on Defect and Fault Tolerance in VLSI Systems, Vancouver, BC, Canada, 2002, pp. 51−59.
    [132]
    J. Blömer and J. P. Seifert, “Fault based cryptanalysis of the advanced encryption standard (AES),” in Porc. 7th Int. Conf. Financial Cryptography, Guadeloupe, French West Indies, 2003, pp. 162−181.
    [133]
    S. Bayat-Sarmadi, M. Mozaffari Kermani, R. Azarderakhsh, and C. Y. Lee, “Dual-basis superserial multipliers for secure applications and lightweight cryptographic architectures,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 61, no. 2, pp. 125–129, Feb. 2014. doi: 10.1109/TCSII.2013.2291075
    [134]
    Y. J. Chen, L. C. Wang, and C. H. Liao, “Eavesdropping prevention for network coding encrypted cloud storage systems,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 8, pp. 2261–2273, Aug. 2016. doi: 10.1109/TPDS.2015.2486772
    [135]
    K. Wang, H. Gao, X. L. Xu, J. F. Jiang, and D. Yue, “An energy-efficient reliable data transmission scheme for complex environmental monitoring in underwater acoustic sensor networks,” IEEE Sens. J., vol. 16, no. 11, pp. 4051–4062, Jun. 2016. doi: 10.1109/JSEN.2015.2428712
    [136]
    L. Yuan, K. Wang, T. Miyazaki, S. Guo, and M. Wu, “Optimal transmission strategy for sensors to defend against eavesdropping and jamming attacks,” in Proc. IEEE Int. Conf. Communications, Paris, France, 2017, pp. 1−6.
    [137]
    A. Chapman, M. Nabi-Abdolyousefi, and M. Mesbahi, “Controllability and observability of network-of-networks via Cartesian products,” IEEE Trans. Autom. Control, vol. 59, no. 10, pp. 2668–2679, Oct. 2014. doi: 10.1109/TAC.2014.2328757
    [138]
    B. B. Wang, L. Gao, Y. Gao, Y. Deng, and Y. Wang, “Controllability and observability analysis for vertex domination centrality in directed networks,” Sci. Rep., vol. 4, no. 1, pp. 1–10, Jun. 2014.
    [139]
    T. Zhou, “On the controllability and observability of networked dynamic systems,” Automatica, vol. 52, pp. 63–75, Feb. 2015. doi: 10.1016/j.automatica.2014.10.121
    [140]
    L. W. An and G. H. Yang, “Opacity enforcement for confidential robust control in linear cyber-physical systems,” IEEE Trans. Autom. Control, vol. 65, no. 3, pp. 1234–1241, Mar. 2020. doi: 10.1109/TAC.2019.2925498
    [141]
    S. Yang, J. Y. Hou, X. Yin, and S. Y. Li, “Opacity of networked supervisory control systems over insecure communication channels,” IEEE Trans. Control Netw. Syst., vol. 8, no. 2, pp. 884–896, Jun. 2021. doi: 10.1109/TCNS.2021.3050131
    [142]
    X. Yin, Z. J. Li, W. L. Wang, and S. Y. Li, “Infinite-step opacity and K-step opacity of stochastic discrete-event systems,” Automatica, vol. 99, pp. 266–274, Jan. 2019. doi: 10.1016/j.automatica.2018.10.049
    [143]
    X. Yin and S. Y. Li, “Verification of opacity in networked supervisory control systems with insecure control channels,” in Proc. IEEE Conf. Decision and Control, Miami, FL, USA, 2018, pp. 4851−4856.
    [144]
    Y. Tong, Z. W. Li, C. Seatzu, and A. Giua, “Current-state opacity enforcement in discrete event systems under incomparable observations,” Discrete Event Dyn. Syst., vol. 28, no. 2, pp. 161–182, Jun. 2018. doi: 10.1007/s10626-017-0264-7
    [145]
    R. Jacob, J. J. Lesage, and J. M. Faure, “Overview of discrete event systems opacity: Models, validation, and quantification,” Annu. Rev. Control, vol. 41, pp. 135–146, Jun. 2016. doi: 10.1016/j.arcontrol.2016.04.015
    [146]
    J. L. Liu, Y. D. Wang, J. D. Cao, D. Yue, and X. P. Xie, “Secure adaptive-event-triggered filter design with input constraint and hybrid cyber attack,” IEEE Trans. Cybern., vol. 51, no. 8, pp. 4000–4010, Aug. 2021. doi: 10.1109/TCYB.2020.3003752
    [147]
    C. Q. Yang, Z. G. Shi, H. Zhang, J. F. Wu, and X. F. Shi, “Multiple attacks detection in cyber-physical systems using random finite set theory,” IEEE Trans. Cybern., vol. 50, no. 9, pp. 4066–4075, Sep. 2020. doi: 10.1109/TCYB.2019.2912939
    [148]
    X. Zhao, C. S. Liu, and E. G. Tian, “Finite-horizon tracking control for a class of stochastic systems subject to input constraints and hybrid cyber attacks,” ISA Trans., vol. 104, pp. 93–100, Sep. 2020. doi: 10.1016/j.isatra.2019.02.025
    [149]
    B. A. S. Al-Rimy, M. A. Maarof, and S. Z. M. Shaid, “Ransomware threat success factors, taxonomy, and countermeasures: A survey and research directions,” Comput. Secur., vol. 74, pp. 144–166, May 2018. doi: 10.1016/j.cose.2018.01.001
    [150]
    H. Oz, A. Aris, A. Levi, and A. S. Uluagac, “A survey on ransomware: Evolution, taxonomy, and defense solutions,” arXiv: 2102.06249, 2021.
    [151]
    S. Y. Xiao, X. H. Ge, Q. L. Han, and Y. J. Zhang, “Secure distributed adaptive platooning control of automated vehicles over vehicular ad-hoc networks under denial-of-service attacks,” IEEE Trans. Cybern., 2021, DOI: 10.1109/TCYB.2021.3074318.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(3)  / Tables(4)

    Article Metrics

    Article views (3423) PDF downloads(926) Cited by()

    Highlights

    • A comprehensive survey is provided to identify current concerns, technologies and future research for cyber attacks on cyber physical systems from the perspective of control theory
    • Current studies on availability, integrity and confidentiality attacks are analyzed based on time-driven and event-driven systems
    • Comparisons among various studies are provided

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return