Physics-informed machine learning in cyber-attack detection and resilient control of chemical processes

Abstract

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.