rq - stop:拒绝服务攻击下弹性量化协同事件触发平均一致采样数据框架

Amir Amini, A. Asif, Arash Mohammadi
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引用次数: 7

摘要

这篇文章被称为rq - stop,提出了一个弹性框架,用于在多代理系统中遭受拒绝服务(DoS)攻击的量化、事件触发(ET)、采样数据、平均共识。DoS攻击通常试图阻断网络中的测量和通信通道。基于ET阈值是依赖还是独立于状态动态,在rq - stop中考虑了两种不同的ET方法。对于每种方法,我们解析推导出采样周期和ET设计参数的运行条件(界),以保证网络在DoS攻击下的输入到状态稳定性(ISS)。此外,还导出了DoS攻击持续时间和频率的上限,在该上限内网络仍可运行。对于每种方法,从平均共识值推导出最大可能误差。两种rq - stop方法对DoS攻击的弹性,以及它们的稳态共识错误和传输节省进行了分析和模拟比较。
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RQ-CEASE: A Resilient Quantized Collaborative Event-Triggered Average-Consensus Sampled-Data Framework Under Denial of Service Attack
Referred to as the RQ-CEASE, this article proposes a resilient framework for quantized, event-triggered (ET), sampled-data, average consensus in multiagent systems subject to denial of service (DoS) attacks. The DoS attacks typically attempt to block the measurement and communication channels in the network. Two different ET approaches are considered in RQ-CEASE based on whether the ET threshold is dependent or independent of the state dynamics. For each approach, we analytically derive operating conditions (bounds) for the sampling period and ET design parameter guaranteeing the input-to-state stability (ISS) of the network under DoS attacks. In addition, upper bounds for duration and frequency of DoS attacks are derived within which the network remains operational. For each approach, the maximum possible error from the average consensus value is derived. The resilience of the two RQ-CEASE approaches to DoS attacks, as well as their steady-state consensus error, and transmission savings are compared both analytically and using simulations.
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期刊介绍: The scope of the IEEE Transactions on Systems, Man, and Cybernetics: Systems includes the fields of systems engineering. It includes issue formulation, analysis and modeling, decision making, and issue interpretation for any of the systems engineering lifecycle phases associated with the definition, development, and deployment of large systems. In addition, it includes systems management, systems engineering processes, and a variety of systems engineering methods such as optimization, modeling and simulation.
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