恶意数据欺骗攻击下网络群系统的弹性编队跟踪

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Yishi Liu, Wenxin Li, Xiwang Dong, Zhang Ren
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引用次数: 0

摘要

针对存在恶意欺骗攻击的leader-follow网络群系统,提出了一种分布式时变编队跟踪方法。攻击者在状态方程和输出方程中注入虚假数据,以防止编队跟踪信息的传输。实时攻击估计由异常估计器提供。为了设计一个事件触发的安全编队跟踪协议,使用状态预测器提供相邻状态。在安全控制协议中考虑了攻击估计和邻域误差,利用李雅普诺夫稳定性定理对其进行了调整。仿真结果验证了该方法在欺骗攻击下能够达到预期的队形跟踪效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Resilient Formation Tracking for Networked Swarm Systems Under Malicious Data Deception Attacks

A distributed time-varying formation tracking method for a leader-following networked swarm system in the presence of malicious deception attacks is proposed in this paper. Both state and output equations are injected with false data by adversaries to prevent formation tracking information transmission. The real-time attack estimations are provided by an anomaly estimator. To design an event-triggered secure formation tracking protocol, a state predictor is utilized to provide neighboring states. The attack estimations and neighboring error are considered in the secure control protocol, which is adjusted using the Lyapunov stability theorem. A simulation result is shown to validate that the proposed method can accomplish the desired formation tracking under deception attacks.

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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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