Privacy-Preserving Distributed Resilient Event-Triggered Platoon Control Under Hybrid Cyber Attacks

IF 8.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Ying Wan;Mingyang Yu;Junjie Fu;Guanghui Wen
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引用次数: 0

Abstract

This article investigates the distributed platoon control problem of connected automated vehicles (CAVs) under hybrid cyber attacks, which include false data injection (FDI) and eavesdropping attacks simultaneously. To mitigate the impact of hybrid cyber attacks on vehicle information exchange, an event-triggered dual-layer control strategy with a hidden layer and competitive interconnection structure is proposed. Specifically, to address malicious data injection from FDI attacks, the proposed framework achieves the objective of vehicle platooning through dynamic interaction between the physical and hidden layers only at triggered time instants. Meanwhile, FDI attacks detection and privacy preservation can also be realized through this strategy. Furthermore, a Zeno-free event-triggered mechanism (ETM) is employed to improve the communication efficiency and reduce resource consumption by avoiding unnecessary state transmissions. Sufficient conditions for control parameters are derived to guarantee resilient platoon control objectives under hybrid cyber attacks. Finally, numerical simulations validate the effectiveness of the proposed approach.
混合网络攻击下保持隐私的分布式弹性事件触发排控制
本文研究了同时存在虚假数据注入(FDI)和窃听攻击的混合网络攻击下的车联网分布式排控制问题。为了减轻混合网络攻击对车辆信息交换的影响,提出了一种具有隐藏层和竞争互联结构的事件触发双层控制策略。具体而言,为了解决FDI攻击的恶意数据注入问题,该框架仅在触发时刻通过物理层和隐藏层之间的动态交互来实现车辆队列的目标。同时,FDI攻击检测和隐私保护也可以通过该策略实现。此外,采用无芝诺事件触发机制(ETM),避免了不必要的状态传输,提高了通信效率,减少了资源消耗。推导了控制参数的充分条件,以保证混合网络攻击下的排控目标具有弹性。最后,通过数值仿真验证了该方法的有效性。
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
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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