Diagnostics Using Nuclear Plant Cyber Attack Analysis Toolkit

Abstract

A Python interface is developed for the GPWR Simulator to automatically simulate cyber-spoofing of different steam generator parameters and plant operation. Specifically, steam generator water level, feedwater flowrate, steam flowrate, valve position, and steam generator controller parameters, including controller gain and time constant, can be directly attacked using command inject, denial of service, and man-in-the-middle type attacks. Plant operation can be initialized to any of the initial conditions provided by the GPWR simulator. Several different diagnostics algorithms have been implemented for anomaly detection, including physics-based diagnostics with Kalman filtering, data-driven diagnostics, noise profiling, and online sensor validation. Industry-standard safety analysis code RELAP5 is also available as a part of the toolkit. Diagnostics algorithms are analyzed based on accuracy and efficiency. Our observations indicate that physics-based diagnostics with Kalman filtering are the most robust. An experimental quantum kernel has been added to the framework for preliminary testing. Our first impressions suggest that while quantum kernels can be accurate, just like any other kernels, their applicability is problem/data dependent, and can be prone to overfitting.