Abstract
Recently, the security of cyber-physical systems is paid more attention gradually. In this paper, we consider the optimal denial-of-service attack scheduling problems under uncertain channel conditions and the security issues of cyber-physical systems are analyzed from the perspective of attackers. The goal of attackers is to design an attack scheduling to maximize the linear cost function while maintaining the stability of systems. To solve this scheduling problem, the Markov decision process is formulated. Since the channel parameters are unknown, the Q-learning algorithm is proposed to solve the associated optimality Bellman equations. Some simulation results are presented to show the effectiveness of the obtained results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, Y., Wei, Y., Liu, X., Zhou, N., Cassandras, C.G.: Optimal persistent monitoring using second-order agents with physical constraints. IEEE Trans. Autom. Control. 64(8), 3239–3252 (2019)
Guo, Z., Shi, D., Johansson, K., Shi, L.: Optimal linear cyber-attack on remote state estimation. IEEE Trans. Control Netw. Syst. 4(1), 4–13 (2017)
Teixeira, A., Shames, I., Sandberg, H., Johansson, K.H.: A secure control framework for resource-limited adversaries. Automatica 51, 135–148 (2015)
Zhang, H., Cheng, P., Shi, L., Chen, J.: Optimal DoS attack scheduling in wireless networked control system. IEEE Trans. Control Syst. Technol. 24(3), 843–852 (2016)
Ding, K., Li, Y., Quevedo, D.E., Dey, S., Shi, L.: A multi-channel transmission schedule for remote state estimation under DoS attacks. Automatica 78, 194–201 (2017)
Qin, J., Li, M., Wang, J., Shi, L., Kang, Y., Zheng, W.: Optimal denial-of-service attack energy management against state estimation over an SINR-based network. Automatica 119, 1–15 (2020)
Li, Y., Chen, C., Wong, W.: Power control for multi-sensor remote state estimation over interference channel. Syst. Control Lett. 126, 1–7 (2019)
Ding, K., Li, Y., Dey, S., Shi, L.: Multi-sensor transmission management for remote state estimation under coordination. IFAC PapersOnline 50(1), 3829–3834 (2017)
Wu, S., Han, D., Cheng, P., Shi, L.: Optimal scheduling of multiple sensors over lossy and bandwidth limited channels. IEEE Trans. Control Netw. Syst. 7(3), 1188–1200 (2020)
Li, Y., Mehr, A.S., Chen, T.: Multi-sensor transmission power control for remote estimation through a SINR-based communication channel. Automatica 101, 78–86 (2019)
Wu, S., Ren, X., Jia, Q., Johansson, K.H., Shi, L.: Learning optimal scheduling policy for remote state estimation under uncertain channel condition. IEEE Trans. Control Netw. Syst. 7(2), 579–591 (2020)
Leong, A.S., Ramaswamy, A., Quevedo, D.E., Karl, H., Shi, L.: Deep reinforcement learning for wireless sensor scheduling in cyber-physical systems. Automatica 113, 1–8 (2020)
Liu, R., Hao, F., Yu, H.: Optimal SINR-based DoS attack scheduling for remote state estimation via adaptive dynamic programming approach, IEEE Trans. Syst. Man Cybern. Syst. (2020). https://doi.org/10.1109/TSMC.2020.2981478
Mesquita, A.R., Hespanha, J.P., Nair, G.N.: Redundant data transmission in control/estimation over lossy networks. Automatica 48, 1612–1620 (2012)
Lewis, F.L., Vrabie, D., Vamvoudakis, K.G.: Reinforcement learning and feedback control: using natural decision methods to design optimal adaptive controllers. IEEE Control Syst. Mag. 32(6), 76–105 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, R., Hao, F. (2022). Learning Optimal DoS Attack Scheduling for Remote State Estimation Under Uncertain Channel Conditions. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 803. Springer, Singapore. https://doi.org/10.1007/978-981-16-6328-4_53
Download citation
DOI: https://doi.org/10.1007/978-981-16-6328-4_53
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6327-7
Online ISBN: 978-981-16-6328-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)