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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. 

In recent years, there has been an increasing need to understand the SCADA networks that oversee our essential infrastructures. While previous studies have focused on networks in a single sector, few have taken a comparative approach across multiple critical infrastructures. This paper dissects operational SCADA networks of three essential services: power grids, gas distribution, and water treatment systems. Our analysis reveals some distinct and shared behaviors of these networks, shedding light on their operation and network configuration. Our findings challenge some of the previous perceptions about the uniformity of SCADA networks and emphasize the need for specialized approaches tailored to each critical infrastructure. With this research, we pave the way for better network characterization for cybersecurity measures and more robust designs in intrusion detection systems. 

The widespread availability of vulnerable IoT devices has resulted in IoT botnets. A particularly concerning IoT botnet can be built around high-wattage IoT devices such as EV chargers because, in large numbers, they can abruptly change the electricity consumption in the power grid. These attacks are called Manipulation of Demand via IoT (MaDIoT) attacks. Previous research has shown that the existing power grid protection mechanisms prevent any large-scale negative consequences to the grid from MaDIoT attacks. In this paper, we analyze this assumption and show that an intelligent attacker with extra knowledge about the power grid and its state, can launch more sophisticated attacks. Rather than attacking all locations at random times, our adversary uses an instability metric that lets the attacker know the specific time and geographical location to activate the high-wattage bots. We call these new attacks MaDIoT 2.0.