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基于两种采样机制的异构多智能体系统在混合网络攻击下的领导者-跟随者安全输出一致性

Leader-Following Secure Output Consensus of Heterogeneous Multiagent Systems Based on Two Sampling Mechanisms Under Hybrid Cyber-Attacks.

作者信息

Jia Xinchun, Li Hongpeng, Chi Xiaobo, Li Bin

出版信息

IEEE Trans Cybern. 2024 Dec;54(12):7826-7838. doi: 10.1109/TCYB.2024.3422232. Epub 2024 Nov 27.

DOI:10.1109/TCYB.2024.3422232
PMID:39042552
Abstract

This article investigates the leader-following secure output consensus (LFSOC) problem of the heterogeneous multiagent systems (MASs) under the hybrid cyber-attacks. A novel hybrid cyber-attack model consisting of aperiodic additive deception (AAD) attacks and aperiodic denial of service (ADoS) attacks is proposed for characterizing cyber-attacks in a real network, where the aperiodicity is reflected in the fact that the duration of each cyber-attack can be different. First, a compensator is introduced for each agent to estimate the leader's state. Second, two sampling mechanisms consisting of a multirate sampling (MRS) mechanism and a periodic sampling mechanism are employed for heterogeneous MASs. The MRS mechanism is used to obtain real-time sampled data on the different physical variables of each agent. The periodic sampling mechanism is applied to sample the agents' compensators and the sampled data are broadcast to their neighbors immediately through a network. By selecting an appropriate sampling period (i.e., a communication period of the compensators), the robustness of heterogeneous MASs against hybrid cyber-attacks can be enhanced. Then, the appropriate communication period is selected for the compensators in different network environments by taking into consideration the cyber-attack parameters. Under these two sampling mechanisms, a sync controller is developed to achieve the LFSOC of heterogeneous MASs. Finally, an example is presented to verify the effectiveness of the proposed approach.

摘要

本文研究了混合网络攻击下异构多智能体系统(MASs)的领导者-跟随者安全输出一致性(LFSOC)问题。提出了一种由非周期加性欺骗(AAD)攻击和非周期拒绝服务(ADoS)攻击组成的新型混合网络攻击模型,用于刻画真实网络中的网络攻击,其中非周期性体现在每次网络攻击的持续时间可以不同这一事实上。首先,为每个智能体引入一个补偿器来估计领导者的状态。其次,针对异构MASs采用了由多速率采样(MRS)机制和周期采样机制组成的两种采样机制。MRS机制用于获取每个智能体不同物理变量的实时采样数据。周期采样机制用于对智能体的补偿器进行采样,并且采样数据通过网络立即广播给其邻居。通过选择合适的采样周期(即补偿器的通信周期),可以增强异构MASs对混合网络攻击的鲁棒性。然后,考虑网络攻击参数,为不同网络环境中的补偿器选择合适的通信周期。在这两种采样机制下,开发了一个同步控制器来实现异构MASs的LFSOC。最后,给出一个例子来验证所提方法的有效性。

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