Abstract
This study focuses on the issue of \({H_\infty }\) filtering for discrete-time semi-Markov jump systems encountering denial-ofservice jamming attacks. The semi-Markov kernel, which describes the switching of the system, is determined by the sojourn-time probability density functions and the transition probability. A Bernoulli distribution white sequence is introduced to describe the phenomenon while accounting for the DoS jamming attacks and channel inherent factors on packet dropout. Considering that the DoS jammer has limited energy, it adopts a random attack strategy, and each work cycle and attack period are different. Based on the mean-square stability criterion, a sufficient condition is provided, and the filter is designed to ensure that the filtering error system is stable with the \({H_\infty }\) performance index. Two examples, including a cognitive radio system, are provided to demonstrate the effectiveness of the proposed filtering method.
Similar content being viewed by others
Data Availability Statement
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
References
Y. Ali, Y. Xia, L. Ma, A. Hammad, Secure design for cloud control system against distributed denial of service attack. Control Theory Technol. 16, 14–24 (2018)
G.K. Befekadu, V. Gupta, P.J. Antsaklis, Risk-sensitive control under Markov modulated denial-of-service (DoS) attack strategies. IEEE Trans. Autom. Control 60, 3299–3304 (2015)
F.O. Catak, A. Mustacoglu, Distributed denial of service attack detection using autoencoder and deep neural networks. J. Intell. Fuzzy Syst. 37, 1–11 (2019)
B. Chen, Y. Niu, Y. Zou, Security control for Markov jump system with adversarial attacks and unknown transition rates via adaptive sliding mode technique. J. Franki. Inst. 356, 3333–3352 (2019)
D. Ding, Z. Wang, D.W.C. Ho, G. Wei, Observer-based event-triggering consensus control for multiagent systems with lossy sensors and cyber-attacks. IEEE Trans. Cybern. 47, 1936–1947 (2017)
K. Ding, Y. Li, D.E. Quevedo, S. Dey, L. Shi, A multi-channel transmission schedule for remote state estimation under DoS attacks. Automatica 78, 194–201 (2017)
J. Gao, Z. Zhao, J. Wang, T. Tan, M. Ma, Event-triggered output feedback control for discrete Markov jump systems under deception attack. J. Franki. Inst. 357, 6435–6452 (2020)
X. Gao, H. He, W. Qi, Admissibility analysis for discrete-time singular Markov jump systems with asynchronous switching. Appl. Math. Comput. 313, 431–441 (2017)
S. Ghosh, A. Gosavi, A semi-Markov model for post-earthquake emergency response in a smart city. Control Theory Technol. 15, 13–25 (2017)
M. Hua, Y. Qian, F. Deng, J. Fei, P. Cheng, H. Chen, Filtering for discrete-time Takagi-Sugeno fuzzy nonhomogeneous Markov jump systems with quantization effects. IEEE Trans. Cybern. 1–14(2020)
M. Hua, D. Zheng, F. Deng, Partially mode-dependent \({l_2}\)-\({l_\infty }\) filtering for discrete-time nonhomogeneous Markov jump systems with repeated scalar nonlinearities. Inf. Sci. 451–452, 223–239 (2018)
M. Hua, D. Zheng, F. Deng, J. Fei, P. Cheng, X. Dai, \({H_\infty }\) filtering for nonhomogeneous Markovian jump repeated scalar nonlinear systems with multiplicative noises and partially mode-dependent characterization. IEEE Trans. Syst. Man. Cybern. Syst. 51, 3180–3192 (2019)
J. Huang, Y. Shi, X. Zhang, Active fault tolerant control systems by the semi-Markov model approach. Int. J. Adapt. Control Signal Process. 28, 833–847 (2014)
B. Jiang, H.R. Karimi, Y. Kao, C. Gao, Takagi-Sugeno model based event-triggered fuzzy sliding-mode control of networked control systems with semi-Markovian switchings. IEEE Trans. Fuzzy Syst. 28, 673–683 (2020)
P. Kaliyar, W. Ben Jaballah, M. Conti, C. Lal, LiDL: localization with early detection of sybil and wormhole attacks in IoT networks. Comput. Secur. 94, 101849 (2020)
N. Katebi, F. Marzbanrad, L. Stroux, C.E. Valderrama, G.D. Clifford, Unsupervised hidden semi-Markov model for automatic beat onset detection in 1D Doppler ultrasound. Physiol. Meas. 41, 85007 (2020)
L. Li, M. Shen, G. Zhang, S. Yan, \({H_\infty }\) control of Markov jump systems with time-varying delay and incomplete transition probabilities. Appl. Math. Comput. 301, 95–106 (2017)
H. Liang, L. Zhang, Y. Sun, T. Huang, Containment control of semi-Markovian multiagent systems with switching topologies. IEEE Trans. Syst. Man, Cybern. Syst. 1-11 (2019)
X. Ma, S.M. Djouadi, H. Li, State estimation over a semi-Markov model based cognitive radio system. IEEE Trans. Wirel. Commun. 11, 2391–2401 (2012)
M.K. Maheshwari, A. Roy, N. Saxena, DRX over LAA-LTE-A new design and analysis based on semi-Markov model. IEEE Trans. Mob. Comput. 18, 276–289 (2019)
Z. Ning, L. Zhang, P. Colaneri, Semi-Markov jump linear systems with incomplete sojourn and transition information: analysis and synthesis. IEEE Trans. Autom. Control 65, 159–174 (2020)
H. Shen, F. Li, S. Xu, V. Sreeram, Slow state variables feedback stabilization for semi-Markov jump systems with singular perturbations. IEEE Trans. Autom. Control 63, 2709–2714 (2018)
H. Shen, Y. Zhu, L. Zhang, J.H. Park, Extended dissipative state estimation for Markov jump neural networks with unreliable links. IEEE Trans. Neural Netw. Learn. Syst. 28, 346–358 (2017)
H. Song, P. Shi, W. Zhang, C. Lim, L. Yu, Distributed \(H_\infty \) estimation in sensor networks with two-channel stochastic attacks. IEEE Trans. Cybern. 50, 465–475 (2020)
L. Su, D. Ye, A cooperative detection and compensation mechanism against denial-of-service attack for cyber-physical systems. Inf. Sci. 444, 122–134 (2018)
A.N. Vargas, G. Pujol, L. Acho, Stability of Markov jump systems with quadratic terms and its application to RLC circuits. J. Franki. Inst. 354, 332–344 (2017)
A. Vashist, A. Keats, S.M. Pudukotai Dinakarrao, A. Ganguly, Securing a wireless network-on-chip against jamming-based denial-of-service and eavesdropping attacks. IEEE Trans. Very Large Scale Integr. Syst. 27, 2781–2791 (2019)
J. Wang, S. Ma, C. Zhang, M. Fu, \({H_\infty }\) state estimation via asynchronous filtering for descriptor Markov jump systems with packet losses. Signal Process. 154, 159–167 (2019)
M. Wang, Y. Liu, B. Xu, Observer-based \({H_\infty }\) control for cyber-physical systems encountering DoS jamming attacks: an attack-tolerant approach. ISA Trans. 104, 1–14 (2020)
Z. Wu, S. Dong, H. Su, C. Li, Asynchronous dissipative control for fuzzy Markov jump systems. IEEE Trans. Cybern. 48, 2426–2436 (2018)
Z. Wu, P. Shi, Z. Shu, H. Su, R. Lu, Passivity-based asynchronous control for Markov jump systems. IEEE Trans. Autom. Control 62, 2020–2025 (2017)
Z. Wu, P. Shi, H. Su, J. Chu, Asynchronous \({l_2}\)-\({l_\infty }\) filtering for discrete-time stochastic Markov jump systems with randomly occurred sensor nonlinearities. Automatica 50, 180–186 (2014)
J. Xiong, J. Lam, Robust \({H_2}\) control of Markovian jump systems with uncertain switching probabilities. Int. J. Syst. Sci. 40, 255–265 (2009)
H. Yan, J. Sun, H. Zhang, X. Zhan, F. Yang, Event-triggered \(H_\infty \) state estimation of 2-DOF quarter-car suspension systems with nonhomogeneous Markov switching. IEEE Trans. Syst. Man Cybern. Syst. 50, 3320–3329 (2020)
H. Zhang, P. Cheng, L. Shi, J. Chen, Optimal denial-of-service attack scheduling with energy constraint. IEEE Trans. Autom. Control 60, 3023–3028 (2015)
L. Zhang, H. Liang, H. Ma, Q. Zhou, Fault detection and isolation for semi-Markov jump systems with generally uncertain transition rates based on geometric approach. Circuits Syst. Signal Process. 38, 1039–1062 (2019)
L. Zhang, Y. Leng, P. Colaneri, Stability and stabilization of discrete-time semi-Markov jump linear systems via semi-Markov kernel approach. IEEE Trans. Autom. Control 61, 503–508 (2016)
X. Zhong, I. Jayawardene, G.K. Venayagamoorthy, R. Brooks, Denial of service attack on tie-line bias control in a power system with PV plant. IEEE Trans. Emerg. Top. Comput. Intell. 1, 375–390 (2017)
F. Zhu, X. Liu, J. Wen, L. Xie, L. Peng, Distributed robust filtering for wireless sensor networks with Markov switching topologies and deception attacks. Sensors 20, 1948 (2020)
K. Zhu, T. Liu, Online tool wear monitoring via hidden semi-Markov model with dependent durations. IEEE Trans. Ind. Inf. 14, 69–78 (2018)
Acknowledgements
This research was funded by the National Key R&D Program of China under Grant 2018YFD0400902, the National Natural Science Foundation of China under Grant 61873112 and the Postgraduate Research & Practice Innovation Program of Jiangnan University under Grant JNKY19_043.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
No potential conflict of interest was reported by the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, Q., Zhu, F. & Peng, L. Robust \({H_\infty }\) Filtering for Semi-Markov Jump Systems Encountering Denial-of-Service Jamming Attacks. Circuits Syst Signal Process 41, 1453–1474 (2022). https://doi.org/10.1007/s00034-021-01853-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-021-01853-z