Cybersecurity in process control: Machine-learning detection and encrypted control

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The security of chemical process control systems has become crucially important since networked control systems are vulnerable to cyber-attacks. The failure to ensure cyber-security can lead to unsafe and potentially catastrophic consequences in chemical process operations, causing environmental damage, capital loss, and human injuries. In recent years, cyber-attacks have been designed by sophisticated adversaries to modify the actuator, the sensor, or the control action, yet remain undetectable by classical detection methods. Therefore, real-time detection of cyber-attacks and mitigation of their effects is an important research problem whose solution could directly impact the safety and security of the chemical process industries. Motivated by these considerations, the goal of this research program is to develop the theory and computational methods needed for detecting and handling intelligent cyber-attacks on process control systems for broad classes of nonlinear processes based on machine learning techniques and encryption tools incorporated within the framework of model-based control methods. User-friendly software will be developed and integrated into the most widely used chemical process CAD software; short courses and workshops will be created to disseminate these computational tools. Furthermore, the research results will be incorporated within the undergraduate process control and senior process design and economics course curricula at UCLA to introduce applications of machine learning techniques in accordance with departmental ABET goals as well as UCLA campus goals on the implementation of a Data Science minor. Finally, the involvement of a diverse group of undergraduate and graduate students in the research through participation in the Center for Engineering Education and Diversity at UCLA, and outreach to Community Colleges by offering summer internships to highly qualified students, will be pursued. The focus of this research program is on the design and implementation of computational methods to prevent cyber-attacks that can compromise data integrity, closed-loop stability, and process operational safety of industrial process control systems. This will be carried out by developing a data-based detection approach using machine learning algorithms to solve classification problems of cyber-attacks using time-series measurement data. Central to this approach is the design of encryption-based model predictive control (MPC) schemes that can operate the process safely and with minimal performance degradation in the presence of cyber-attacks. This will be accomplished through the development of a novel cyber-secure MPC stability-performance control architecture that can detect cyber-attacks and isolate the affected sensors and actuators in the control network while maintaining closed-loop stability. The effectiveness of the resulting cyber-security framework will be demonstrated through applications to high-fidelity, large-scale chemical process networks in an ASPEN simulation environment incorporating industrial process variability data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.