Optimal DoS Attack Policy Against Periodically Switched System With Two-Hop Relay Networks

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Yu Zhu, Heng Zhang, Engang Tian, Weisheng Si, Weixing Zheng
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引用次数: 0

Abstract

Periodically switched systems (PSS) are an important class of hybrid dynamic systems. This paper studies the security of PSS with two-hop relay networks, from which the results can be extended to multiple hops. From the perspective of an attacker with limited energy, our objective is to find the optimal trade-off between energy allocation and channel coverage. If the attacker allocates more energy each time, the packet loss rate of the channel increases, leading to a decrease in system performance. Our contributions are threefold. First, the stability conditions of PSS are provided based on the initial values of prior estimation errors in both specific and general cases. Second, we give system stability conditions in the presence of an external attacker, for the case that the relay node adopts the Direct Forwarding Strategy (DFS). Finally, the attack strategy optimization is cast as a Markov decision process (MDP) problem. A Q-learning algorithm is designed to find the optimal attack policy to reduce the estimation performance of the estimator. The effectiveness of the designed algorithm is demonstrated by a simulation study involving an undamped oscillator model.

针对两跳中继网络周期交换系统的最优DoS攻击策略
周期切换系统(PSS)是一类重要的混合动态系统。本文研究了带有两跳中继网络的PSS的安全性,其结果可以推广到多跳。从能量有限的攻击者的角度来看,我们的目标是在能量分配和信道覆盖之间找到最优权衡。如果攻击者每次分配更多的能量,则通道丢包率会增加,导致系统性能下降。我们的贡献是三重的。首先,在特定情况和一般情况下,根据先验估计误差的初始值给出了PSS的稳定性条件。其次,给出了中继节点采用直接转发策略(Direct Forwarding Strategy, DFS)时存在外部攻击者时系统的稳定性条件。最后,将攻击策略优化转化为马尔可夫决策过程(MDP)问题。设计了一种q学习算法来寻找最优攻击策略,以降低估计器的估计性能。通过无阻尼振荡器模型的仿真研究,验证了所设计算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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