Post cyber-attack state reconstruction for nonlinear processes using machine learning

Abstract

Abstract This work proposes state-reconstruction strategies to effectively regain and/or maintain controllability of the system following the detection of cyber-attacks on sensor measurements. Working with a general class of nonlinear systems, of which the sensor measurements may be subject to cyber-attacks, robust control frameworks have been previously proposed to maintain the stability of the process in the presence of cyber-attacks. Moreover, machine-learning-based detection mechanisms could be employed to effectively detect the presence of and distinguish the particular types of cyber-attacks. This work further explores recuperation measures to be taken after the detection of cyber-attacks to mitigate their impact, and proposes a machine-learning-based state reconstruction approach to provide estimated state measurements based on the falsified state measurements. This approach ensures stable operation of the process before reliable sensor measurements are installed back online.