Integrated cyberattack detection and handling for nonlinear systems with evolving process dynamics under Lyapunov-based economic model predictive control

Rangan, Keshav Kasturi; Oyama, Henrique; Durand, Helen

doi:10.1016/j.cherd.2021.03.024

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…If the detection scheme would then be set up to compare expectations under the old and new process models, the change in process dynamics may be erroneously flagged as a cyberattack. In Rangan et al (2021), for example, we provide a two-fold control/ detection mechanism to prevent false attack detection when the variations in the process dynamics are considered. • Though methods for making cyberattacks discoverable might benefit from the knowledge of the distribution of the noise and disturbances (to better distinguish Eqs 76, 77), the various detection strategies developed in this work make no consideration for statistics; they look only at the bounds on disturbances and sensor measurements.…”

Section: Cyberattack Discoverability For Nonlinear Systemsmentioning

confidence: 99%

“…Prior work in our group has explored the interaction between cyberattack detection strategies, MPC/EMPC design, and stability guarantees. In particular, our prior works have primarily focused on studying and developing control/detection mechanisms for scenarios in which either actuators or sensors are attacked (Oyama and Durand, 2020;Rangan et al, 2021;Oyama et al, 2021;Durand and Wegener, 2020). For example, Oyama and Durand (2020) proposed three cyberattack detection concepts that are integrated with the control framework Lyapunov-based EMPC (Heidarinejad et al, 2012a).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Oyama and Durand (2020) proposed three cyberattack detection concepts that are integrated with the control framework Lyapunov-based EMPC (Heidarinejad et al, 2012a). Advancing this work, Rangan et al (2021) and Oyama et al (2021) proposed ways to consider cyberattack detection strategies and the challenges in cyberattackhandling for nonlinear processes whose dynamics change with time. In the present manuscript, we extend our prior work (which covered sensor measurement cyberattack-handling with controltheoretic guarantees and actuator cyberattack-handling without guarantees) to develop strategies for maintaining safety when actuator attacks are not detected (assuming that no attack occurs on the sensors).…”

Section: Introductionmentioning

confidence: 99%

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

Oyama

Messina²,

Rangan³

et al. 2022

Front. Chem. Eng.

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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.

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Section: Cyberattack Discoverability For Nonlinear Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Oyama

Messina²,

Rangan³

et al. 2022

Front. Chem. Eng.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Narasimhan,

Ellis,

El-Farra

2024

Processes

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A fundamental problem at the intersection of process control and operations is the design of detection schemes monitoring a process for cyberattacks using operational data. Multiplicative false data injection (FDI) attacks modify operational data with a multiplicative factor and could be designed to be detection evading without in-depth process knowledge. In a prior work, we presented a control mode switching strategy that enhances the detection of multiplicative FDI attacks in processes operating at steady state (when process states evolve within a small neighborhood of the steady state). Control mode switching on the attack-free process at steady-state may induce transients and generate false alarms in the detection scheme. To minimize false alarms, we subsequently developed a control mode switch-scheduling condition for processes with an invertible output matrix. In the current work, we utilize a reachable set-based detection scheme and use randomized control mode switches to augment attack detection capabilities. The detection scheme eliminates potential false alarms occurring from control mode switching, even for processes with a non-invertible output matrix, while the randomized switching helps bolster the confidentiality of the switching schedule, preventing the design of a detection-evading “smart” attack. We present two simulation examples to illustrate attack detection without false alarms, and the merits of randomized switching (compared with scheduled switching) for the detection of a smart attack.

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Handling of stealthy sensor and actuator cyberattacks on evolving nonlinear process systems

Oyama

Rangan

Durand

2021

J Adv Manuf & Process

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Cyberattacks on control systems in the chemical process industries cause concern regarding how they can impact finances, safety, and production levels of companies. A key practical challenge for cyberattack detection and handling using process information is that process behavior evolves over time. Conceivably, changes in process dynamics might cause some detection strategies to flag a change in the dynamics as an attack due to the new data appearing abnormal compared to data from before the dynamics changed. In this work, we utilize several case studies to probe the question of what might be the impacts, benefits, and limitations of cyberattack detection and handling policies when the process dynamics change over time. The goal of this work is to characterize, through simulation studies, characteristics, which might be desirable and undesirable in cyberattack detection and handling procedures when

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Integrated cyberattack detection and handling for nonlinear systems with evolving process dynamics under Lyapunov-based economic model predictive control

Cited by 13 publications

References 29 publications

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

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

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

Handling of stealthy sensor and actuator cyberattacks on evolving nonlinear process systems

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