Achieving Self-Configurable Runtime State Verification in Critical Cyber-Physical Systems

Abstract

Cyber-Physical Systems (CPS) commonly monitor and manage critical cyber-enabled services such as distributed power generation architectures. Traditional CPS consists of heterogeneous devices that monitor physical systems processes generally stochastic and complex to model through state-of-the-art methods such as time-series analysis. Due to the stochas-tic nature, assurance of continuous runtime state integrity is challenging. Furthermore, adversaries exploit the lack of robust security mechanisms to deploy false sequential attacks that target the physical state of system processes. Therefore, this work designs runtime-system-state integrity assurance techniques necessary to enhance the security of critical CPS such as Small Modular Reactors (SMR). In this work, we propose a Reinforce-ment Learning(RL)-based Runtime-system-state Integrity (RRI) framework that aims to enable self-configurable runtime-system-states in SMR. The RRI framework generally addresses false sequential attacks by enabling fine-grained detail continuous runtime state integrity assurance through state-of-the-art RL and Machine Learning (ML) methods. A proof-of-concept of the RRI framework has been evaluated in an emulated SMR. This work demonstrates the RRI framework's performance regarding the RL methods' convergence time. Overall, the state-of-the-art RL methods converge in 1,000 episodes. We implemented the emulated experimental SMR through the open-source OpenAI, scikit-learn, and Stable Baselines3 platforms. The open-source platforms enable the development and comparison of RL and ML methods by enabling standard communication between baselines algorithms and ecosystems. The experimental results discussed in this work provide essential information that help understand complex and stochastic environments. Furthermore, we demon-strated that the RRI framework could provide high-fidelity CPS models that can provide helpful insights into understanding the system-state behavior of complex system processes.