Lyapunov-Based Economic Model Predictive Control for Detecting and Handling Actuator and Simultaneous Sensor/Actuator Cyberattacks on Process Control Systems

Abstract

The controllers for a cyber-physical system may be impacted by sensor measurement cyberattacks, actuator signal cyberattacks, or both types of attacks. Prior work in our group has developed a theory for handling cyberattacks on process sensors. However, sensor and actuator cyberattacks have a different character from one another. Specifically, sensor measurement attacks prevent proper inputs from being applied to the process by manipulating the measurements that the controller receives, so that the control law plays a role in the impact of a given sensor measurement cyberattack on a process. In contrast, actuator signal attacks prevent proper inputs from being applied to a process by bypassing the control law to cause the actuators to apply undesirable control actions. Despite these differences, this manuscript shows that we can extend and combine strategies for handling sensor cyberattacks from our prior work to handle attacks on actuators and to handle cases where sensor and actuator attacks occur at the same time. These strategies for cyberattack-handling and detection are based on the Lyapunov-based economic model predictive control (LEMPC) and nonlinear systems theory. We first review our prior work on sensor measurement cyberattacks, providing several new insights regarding the methods. We then discuss how those methods can be extended to handle attacks on actuator signals and then how the strategies for handling sensor and actuator attacks individually can be combined to produce a strategy that is able to guarantee safety when attacks are not detected, even if both types of attacks are occurring at once. We also demonstrate that the other combinations of the sensor and actuator attack-handling strategies cannot achieve this same effect. Subsequently, we provide a mathematical characterization of the “discoverability” of cyberattacks that enables us to consider the various strategies for cyberattack detection presented in a more general context. We conclude by presenting a reactor example that showcases the aspects of designing LEMPC.

Detection of Multiplicative False Data Injection Cyberattacks on Process Control Systems via Randomized Control Mode Switching

“…Due to the interconnected nature of a cyber-physical system like a PCS, a comprehensive solution to enhancing the cybersecurity of PCSs may involve adopting a multi-faceted approach that involves both information technology-based and operational technologybased approaches. The approaches proposed in the literature broadly include those that reinforce information technology-based infrastructure (e.g., firewalls [3] and network design-based approaches [4,5]), approaches that involve cybersecure architecture design (e.g., [6]), approaches for process equipment design to mitigate adverse impacts of a cyberattack (e.g., [7,8]), and the design of operational technology (OT)-based approaches for detecting, identifying, and successfully recovering from an ongoing cyberattack [6,[9][10][11][12][13][14][15][16][17][18][19][20].…”

“…Machine learning-based approaches have also been used as methods for cyberattack detection [6,17]. Other examples include residual-based detection strategies that are designed to detect an attack if the attack causes the process states to leave a safe operating region [10][11][12]. Attack detection approaches that utilize random authentication signals to the manipulated input to add a perturbation to the process and reveal the presence of an attack [21][22][23][24][25] have also been proposed.…”