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Industries are embracing information technology and constructing more robust machines known as Cyber-Physical Systems(CPS) to automate processes. CPSs are envisioned to be pervasive, coordinating, and integrating computation, sensing, actuation, and physical processes. CPSs have various applications in life-critical scenarios, where their performance and reliability can have direct impacts on human safety and well-being. However, CPSs are vulnerable to malicious attacks, and researchers have developed detectors to identify such attacks in different contexts. Surprisingly, little work has been done to detect attacks on the actuators of CPS. Furthermore, actuators face a high risk of optimal hidden attacks designed by powerful attackers, which can push them into an unsafe state without detection. To the best of our knowledge, no such attacks on actuators have been developed yet. In this paper, we design an optimal hidden attack for actuators and evaluate its effectiveness. First, we develop a mathematical model for actuators and then create a linear program for convex optimization. Second, we solve the optimization problem and simulate the optimal attack. 

Cyber-physical systems (CPS) are susceptible to physical attacks, and researchers are exploring ways to detect them. One method involves monitoring the system for a set duration, known as the time-window, and identifying residual errors that exceed a predetermined threshold. However, this approach means that any sensor attack alert can only be triggered after the time-window has elapsed. The length of the time-window affects the detection delay and the likelihood of false alarms, with a shorter time-window leading to quicker detection but a higher false positive rate, and a longer time-window resulting in slower detection but a lower false positive rate. While researchers aim to choose a fixed time-window that balances a low false positive rate and short detection delay, this goal is difficult to attain due to a trade-off between the two. An alternative solution proposed in this paper is to have a variable time-window that can adapt based on the current state of the CPS. For instance, if the CPS is heading towards an unsafe state, it is more crucial to reduce the detection delay (by decreasing the time-window) rather than reducing the false alarm rate, and vice versa. The paper presents a sensor attack detection framework that dynamically adjusts the time-window, enabling attack alerts to be triggered before the system enters dangerous regions, ensuring timely detection. This framework consists of three components: attack detector, state predictor, and window adaptor. We have evaluated our work using real-world data, and the results demonstrate that our solution improves the usability and timeliness of time-window-based attack detectors.