@inproceedings{f5ea52fa848948cf9b1bc3ee976771fc,
title = "Learning Optimal DoS Attack Scheduling for Remote State Estimation Under Uncertain Channel Conditions",
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.",
keywords = "Cyber-physical systems, Q-learning algorithm, Security issues",
author = "Ruirui Liu and Fei Hao",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 17th Chinese Intelligent Systems Conference, CISC 2021 ; Conference date: 16-10-2021 Through 17-10-2021",
year = "2022",
doi = "10.1007/978-981-16-6328-4\_53",
language = "英语",
isbn = "9789811663277",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "502--511",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu and Zhiyuan Yu and Song Zheng",
booktitle = "Proceedings of 2021 Chinese Intelligent Systems Conference",
address = "德国",
}