在未知的混合攻击下,网络系统的无模型输出调节
IEEE transactions on cybernetics
|September 23, 2025
概括
本研究介绍了一种基于学习的控制方法,用于调节面临混合网络攻击的未知系统. 该方法有效地管理系统输出,尽管传感器和执行器数据受到损害.
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:
- 离散时间系统容易受到复杂的网络攻击,包括拒绝服务,重播和欺骗攻击.
- 攻击可以发生在多个通道上,例如传感器控制器和控制器执行器,从而损害系统完整性.
- 现有的控制方法往往需要精确的系统知识或直接的干扰测量,限制了它们的适用性.
研究的目的:
- 为在混合网络攻击下对未知的离散时间系统开发强大的输出调节策略.
- 设计一种不需要先前了解系统动态或外部干扰测量的控制方法.
- 为了确保系统的执行能力保持不变,尽管传感器-控制器和控制器-执行器通道上的数据受到损害.
主要方法:
- 提出了一种基于学习的回退视界控制策略,利用历史输出信号.
- 基于马尔科夫参数的时间序列控制方法被推广为处理两种通信通道上的混合攻击.
- 开发了一种混合攻击检测程序和恢复程序,以区分未受损的预测输入.
主要成果:
- 提出的方法有效地调节系统输出,而不需要精确的系统动态知识.
- 它成功地抵消了混合攻击对执行器性能的不利影响,即使有被改的传感器数据.
- 一般化的时间序列控制和恢复程序解决了在两个道上混合攻击所带来的挑战.
结论:
- 基于学习的回退视界控制为混合网络攻击下的未知系统的输出调节提供了强大的解决方案.
- 这种方法提供了一个无模型的方法来克服由复杂的攻击引起的严重数据完整性问题.
- 开发的技术提高了控制系统在敌对环境中的弹性和可靠性.
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