事件触发安全控制设计,以防止通过基于Lyapunov的神经网络的虚假数据注入攻击
Neslihan Karas Kutlucan1, Levent Ucun1, Janset Dasdemir2
1Control and Automation Engineering, Yildiz Technical University, 34220 Istanbul, Türkiye.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究引入了一种使用混合观察者和事件触发控制 (ETC) 的安全控制框架,以打击虚假数据注入 (FDI) 攻击. 该方法确保了关键应用程序中的系统稳定性和资源效率.
科学领域:
- 控制系统工程 控制系统工程
- 网络安全 网络安全
- 机器人技术 机器人技术 机器人技术
背景情况:
- 假数据注入 (FDI) 攻击对控制系统的完整性和可靠性构成重大威胁.
- 现有的安全控制方法可能不是资源高效,导致不必要的计算开销和控制行动.
研究的目的:
- 开发一个安全,资源高效的控制框架,能够抵御外国直接投资的攻击.
- 将混合观察员与事件触发控制 (ETC) 战略集成,以提高运营性能.
主要方法:
- 结合卡尔曼波器和神经网络 (NN) 的混合观察器被设计为实时的外国直接投资检测和补偿.
- 基于Lyapunov的更新规律是为NN权重推导的,以保证系统的稳定性.
- 实施了事件触发控制 (ETC) 机制,以尽量减少控制操作和资源使用.
- 为了稳定性分析和触发值的确定,制定了线性矩阵不等式 (LMI) 条件.
主要成果:
- 拟议的框架有效地检测和补偿了各种外国直接投资攻击场景.
- 事件触发机制显著减少了控制冗余和计算负载.
- 基于Lyapunov的稳定性分析证实了估计和系统错误的局限性.
结论:
- 开发的安全控制框架为安全关键应用提供了强大且资源意识的解决方案.
- 混合观察员和ETC的整合提供了对外国直接投资攻击的弹性,同时优化了系统性能.
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