Skip to main content
Log in

Robust \({H_\infty }\) Filtering for Semi-Markov Jump Systems Encountering Denial-of-Service Jamming Attacks

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

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

  1. 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)

    Article  MathSciNet  Google Scholar 

  2. 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)

    Article  MathSciNet  Google Scholar 

  3. 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)

    Google Scholar 

  4. 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)

    Article  MathSciNet  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. 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)

    Article  MathSciNet  Google Scholar 

  7. 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)

    Article  MathSciNet  Google Scholar 

  8. 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)

    MathSciNet  MATH  Google Scholar 

  9. S. Ghosh, A. Gosavi, A semi-Markov model for post-earthquake emergency response in a smart city. Control Theory Technol. 15, 13–25 (2017)

    Article  MathSciNet  Google Scholar 

  10. 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)

  11. 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)

    Article  Google Scholar 

  12. 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)

    Google Scholar 

  13. 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)

    Article  MathSciNet  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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)

    Article  MathSciNet  Google Scholar 

  18. 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)

  19. 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)

    Article  Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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)

    Article  MathSciNet  Google Scholar 

  22. 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)

    Article  MathSciNet  Google Scholar 

  23. 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)

    Article  MathSciNet  Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. 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)

    Article  MathSciNet  Google Scholar 

  26. 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)

    Article  MathSciNet  Google Scholar 

  27. 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)

    Article  Google Scholar 

  28. 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)

    Article  Google Scholar 

  29. 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)

    Article  Google Scholar 

  30. Z. Wu, S. Dong, H. Su, C. Li, Asynchronous dissipative control for fuzzy Markov jump systems. IEEE Trans. Cybern. 48, 2426–2436 (2018)

    Article  Google Scholar 

  31. 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)

    Article  MathSciNet  Google Scholar 

  32. 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)

    Article  MathSciNet  Google Scholar 

  33. J. Xiong, J. Lam, Robust \({H_2}\) control of Markovian jump systems with uncertain switching probabilities. Int. J. Syst. Sci. 40, 255–265 (2009)

    Article  Google Scholar 

  34. 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)

    Article  Google Scholar 

  35. 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)

    Article  MathSciNet  Google Scholar 

  36. 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)

    Article  Google Scholar 

  37. 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)

    MathSciNet  MATH  Google Scholar 

  38. 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)

    Article  Google Scholar 

  39. 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)

    Article  Google Scholar 

  40. K. Zhu, T. Liu, Online tool wear monitoring via hidden semi-Markov model with dependent durations. IEEE Trans. Ind. Inf. 14, 69–78 (2018)

    Article  Google Scholar 

Download references

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

Authors

Corresponding author

Correspondence to Li Peng.

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-021-01853-z

Keywords

Navigation