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Cyber-physical systems tightly integrate computational resources with physical processes through sensing and actuating, widely penetrating various safety-critical domains, such as autonomous driving, medical monitoring, and industrial control. Unfortunately, they are susceptible to assorted attacks that can result in injuries or physical damage soon after the system is compromised. Consequently, we require mechanisms that swiftly recover their physical states, redirecting a compromised system to desired states to mitigate hazardous situations that can result from attacks. However, existing recovery studies have overlooked stochastic uncertainties that can be unbounded, making a recovery infeasible or invalidating safety and real-time guarantees. This paper presents a novel recovery approach that achieves the highest probability of steering the physical states of systems with stochastic uncertainties to a target set rapidly or within a given time. Further, we prove that our method is sound, complete, fast, and has low computational complexity if the target set can be expressed as a strip. Finally, we demonstrate the practicality of our solution through the implementation in multiple use cases encompassing both linear and nonlinear dynamics, including robotic vehicles, drones, and vehicles in high-fidelity simulators. 

Anomaly detection can ensure the operational integrity of control systems by identifying issues such as faulty sensors and false data injection attacks. At the same time, we need privacy to protect personal data and limit the information attackers can get about the operation of a system. However, anomaly detection and privacy can sometimes be at odds, as monitoring the system’s behavior is impeded by data hiding. Cryptographic tools such as garbled circuits and homomorphic encryption can help, but each of these is best suited for certain different types of computation. Control with anomaly detection requires both types of computations so a naive cryptographic implementation might be inefficient. To address these challenges, we propose and implement protocols for privacy-preserving anomaly detection in a linear control system using garbled circuits, homomorphic encryption, and a combination of the two. In doing so, we show how to distribute private computations between the system and the controller to reduce the amount of computation–in particular at the low-power system. Finally, we systematically compare our proposed protocols in terms of precision, computation, and communication costs. 

This paper takes a first look at the potential consequences of cyberattacks against structural control systems. We design algorithms and implement them in a testbed and on well-known benchmark models for buildings and bridges. Our results show that attacks to structures equipped with semi-active and active vibration control systems can let the attacker oscillate the building or bridge at the resonance frequency, effectively generating threats to the structure and the people using it. We also implement and test the effectiveness of attack-detection systems.