异步最佳 DoS 攻击时间表下异构多代理系统的弹性共识

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yan Xie , Lianghao Ji , Xing Guo , Huaqing Li
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引用次数: 0

摘要

本研究旨在解决异构多代理系统(MAS)在异步最优拒绝服务(DoS)攻击调度下实现弹性共识的难题。考虑到对手在 MAS 不同通信渠道的开始时间和持续时间各不相同,我们主要关注能量有限的对手如何选择渠道发动攻击,从而对系统性能造成最大破坏。基于动态编程,我们得到了一种异步最优 DoS 攻击时间表生成算法。最优 DoS 攻击时间表为设计更有效的安全控制方法提供了基础。然后,我们提出了一种分布式弹性控制策略,利用基于观测器的方法在异构 MAS 中实现异步最优 DoS 攻击时间表下的输出共识。此外,我们还建立了有关 DoS 攻击持续时间和频率的条件。最后,我们进行了两次模拟,以展示我们所提方法的功效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Resilient consensus of heterogeneous multi-agent systems under asynchronous optimal DoS attack schedules
This study aims to address the challenge of achieving resilient consensus in heterogeneous multi-agent systems (MASs) under asynchronous optimal Denial of Service (DoS) attack schedules. Considering the start and duration of adversaries vary from different communication channels of MASs, we mainly focus on how adversaries with limited energy choose channels to launch attacks to cause the greatest damage to system performance. Based on dynamic programming, an asynchronous optimal DoS attack schedules generation algorithm is obtained. Optimal DoS attack schedules provides a basis for designing more effective secure control methods. Then, a distributed resilient control strategy that utilizes an observer-based approach to achieve output consensus in heterogeneous MASs under asynchronous optimal DoS attack schedules is proposed. Additionally, we establish conditions regarding the durations and frequencies of DoS attacks as well. Finally, two simulations are performed to showcase the efficacy of our proposed method.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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