在DoS攻击下通过强化学习来对多代理系统进行规定的时间的人在循环中最佳同步控制.
概括
本研究介绍了面临拒绝服务 (DoS) 攻击的多代理系统 (MAS) 的循环控制. 一个新的观察者和Q学习方法在规定的时间内实现最佳同步,确保系统的稳定性和性能.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 多代理系统 (MAS) 容易受到拒绝服务 (DoS) 攻击的攻击,破坏通信链路.
- 人在循环 (HiTL) 控制提供了增强的系统治理,但需要强大的同步策略.
- 规定的时间 (PT) 控制旨在在用户定义的有限时间内实现趋同,这对于时间敏感的应用程序至关重要.
研究的目的:
- 针对基于链接的DoS攻击下MAS的规定的时间 (PT) 最佳同步控制.
- 开发一个分布式观察器,能够在切换拓学下估计PT内部的领导输出.
- 实现无模型的Q学习算法,以实现最佳的政策学习,并减少计算负载.
主要方法:
- 对于追随者代理来说,提出了一个完全分布的观察者,具有规定的有限时间函数.
- 增强系统将追随者动态与观察者结合起来进行稳定性分析.
- 一个单关键神经网络 (NN) Q学习算法,通过最小方形进行训练,用于政策优化.
主要成果:
- 拟议的观察员保证了全球实际的PT收与有限的收益,独立于全球拓.
- 该Q学习算法证明了Q函数的融合,使得最优的同步政策学习.
- 模拟结果验证了开发的控制方案对DoS攻击的有效性.
结论:
- 该研究成功开发了一个PT HiTL最佳同步控制在DoS攻击下的MAS.
- 拟议的观察者和Q学习方法提供了一个强大的和计算效率高的解决方案.
- 这些发现有助于提高MAS在敌对环境中的弹性和性能.
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