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安全控制马尔科夫跳跃网络物理系统受到恶意攻击:一个弹性混合学习方案
IEEE transactions on cybernetics
|September 6, 2024
概括
这项研究引入了弹性混合学习,用于在网络物理系统中对攻击进行安全控制. 它开发基于模型和无模型的方法来解决复杂的游戏方程,确保系统安全.
科学领域:
- 控制系统工程 控制系统工程
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 网络物理系统 (CPS) 容易受到恶意攻击,危及安全控制.
- 马尔科夫跳跃系统引入了由于状态依赖动态的复杂性.
- 在敌对条件下安全控制是一个关键的挑战.
研究的目的:
- 开发一种新的弹性混合学习方案,用于在恶意攻击下的离散时间马尔科夫跳转CPS.
- 为了应对与合项解决合的代数里卡蒂方程的挑战.
- 为安全控制提出基于模型和无模型的学习方法.
主要方法:
- 在零和游戏框架内制定安全控制问题.
- 开发一个并行的强化学习框架.
- 设计基于模型的弹性混合学习方案,需要系统动态.
- 提出一个在线无模型的弹性混合学习方案,将价值和政策代结合起来.
主要成果:
- 成功地将安全控制问题转换为解决游戏合的代数里卡蒂方程.
- 开发并分析了基于模型和无模型的混合学习方案的融合.
- 使用反转的摆形模型证明了拟议的算法的有效性.
结论:
- 拟议的弹性混合学习方案有效地解决了马尔科夫跳CPS的安全控制问题.
- 基于模型和无模型的方法都提供了可行的解决方案,用于加强系统安全,防止恶意攻击.
- 开发的方法为复杂的网络物理系统的安全控制提供了强大的框架.
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