DDoS攻击下网络物理系统的加性内存动态事件控制器

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Qing Zhu, Kaibo Shi, Xiao Cai, Da Chen, Jin Yang
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引用次数: 0

摘要

本文针对分布式拒绝服务(DDoS)攻击下的网络物理系统(cps)设计了一种内存动态事件触发输出反馈控制器。首先,我们考虑了混合时间延迟对通信网络的影响,并重点关注了可以持续瘫痪网络的DDoS攻击。其次,在有限的带宽通信资源下,提出了一种内存动态事件触发方案(MDETS),该方案有效地利用了带宽资源,并减轻了网络引起的现象。此外,为了保证cps的安全运行,根据测量通道中的时延状态设计了带记忆的输出反馈控制器。然后,选取合适的多区域Lyapunov-Krasovskii泛函(LKFs),给出了充分的指数均方H∞稳定性条件$$ {H}_{\infty } $$判据。最后,通过实例验证了所设计策略的有效性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Memory-Dynamic Event-Based Controller With Additive-Memory Time-Delay for Cyber-Physical Systems Under DDoS Attacks

This paper tackles the challenge of designing a memory-dynamic event-triggered output feedback controller for cyber-physical systems (CPSs) under distributed denial of service (DDoS) attacks. First, we consider the impact of hybrid time delays on communication networks and focus on DDoS attacks that can continuously paralyze the network. Second, with limited bandwidth communication resources, a memory-dynamic event-triggered scheme (MDETS) that efficiently utilizes the bandwidth resources as well as mitigates the network-induced phenomena is proposed. In addition, to ensure the safe operation of CPSs, an output feedback controller with memory is designed based on time-delay states in the measurement channel. Then, selecting the proper multi-area Lyapunov-Krasovskii functionals (LKFs), criteria for sufficient exponentially mean-square H $$ {H}_{\infty } $$ stability condition is formulated. Finally, an illustrative example is used to demonstrate the effectiveness and applicability of the designed strategies.

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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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