With the integration of internet of things (IoT) devices, cloud computing, and other digital technologies into chemical processes, the complexity and stealthiness of cyber-attacks have increased. To mitigate the impact of sensor cyber-attacks in chemical processes, this work presents a framework that develops physics-informed machine learning (PIML)-based detectors and resilient controllers for improving closed-loop performance of nonlinear system under cyber-attacks. The PIML detector is constructed through a customized loss function that integrates the domain knowledge of cyber-attacks into the training process. Additionally, upon detection of attacks, a knowledge-guided extended Kalman filter is developed to provide estimated states for resilient control prior to replacement by redundant sensors. A chemical process example is used to illustrate the application of the proposed PIML-based detection and resilient control methods to handle cyber-attacks.

中文翻译：

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网络攻击检测和化学过程弹性控制中的物理信息机器学习

随着物联网 (IoT) 设备、云计算和其他数字技术融入化学过程，网络攻击的复杂性和隐蔽性不断增加。为了减轻传感器网络攻击对化学过程的影响，这项工作提出了一个框架，开发基于物理信息机器学习（PIML）的探测器和弹性控制器，以提高非线性系统在网络攻击下的闭环性能。 PIML 检测器是通过定制的损失函数构建的，该函数将网络攻击的领域知识集成到训练过程中。此外，在检测到攻击时，开发了知识引导的扩展卡尔曼滤波器，以在被冗余传感器替换之前为弹性控制提供估计状态。使用化学过程示例来说明所提出的基于 PIML 的检测和弹性控制方法在处理网络攻击中的应用。