Integrating machine learning detection and encrypted control for enhanced cybersecurity of nonlinear processes

Abstract

This study presents an encrypted two-tier control architecture integrated with a machine learning (ML) based cyberattack detector to enhance the operational safety, cyber-security, and performance of nonlinear processes. The upper tier of this architecture employs an encrypted nonlinear Lyapunov-based model predictive controller (LMPC) to enhance closed-loop performance, while the lower tier utilizes an encrypted set of linear controllers to stabilize the process. Encrypted signals from the sensors are decrypted at the upper tier for plain text control input computation, while the lower tier computes control inputs in an encrypted space, due to its exclusive use of linear operations. While this design enhances closed-loop performance, it exposes the upper tier to potential cyberattacks. To mitigate this risk, an ML-based detector is developed in the form of a feed-forward neural network, utilizing sensor-derived data for attack detection. Upon attack detection, the control system logic deactivates the performance-enhancing upper tier and relies solely on the cybersecure lower tier for system stabilization. The study also includes a comprehensive stability analysis of the two-tier control structure, establishing error bounds related to quantization and sample-and-hold controller implementations. The proposed control framework can be extended to any nonlinear process that is controlled by a combination of linear and nonlinear controllers to enhance the system cybersecurity. Guidelines such as quantization parameter selection, cyberattack detector development, and sampling time criteria are included to facilitate practical implementation. Simulation results of a nonlinear chemical process network demonstrated the robustness of the encrypted control architecture and cyberattack detector, as well as its ability to detect previously unseen attack patterns.