Autonomous aerial vehicles (UAVs) are widespread in Internet of Things (IoT) systems applications. However, UAVs suffer cyberspace security threats, especially physical attacks that leverage physics signals to deceive sensors, disrupt missions, and potentially crash the UAVs. Such attack detection demands not only guaranteeing detection accuracy and timeliness but also robustness. Prior studies consider physical laws (Invariant) to detect inconsistency, but they sacrifice robustness requirements of uncertainties and lack theoretical guarantees of detection performance. To achieve the sensitivity-specificity tradeoff, this article proposesRobIn, a Robust Invariant-based physical attacks detector design incorporating scenario optimization. The key idea behindRobInis robustifying the invariant model via scenario optimization theory to ensure modality untouched and trustworthy detection. With an offline robustification scheme and an onboard detection algorithm,RobIncan balance attack sensitivity and robustness to uncertainties. We theoretically provide the detection specificity lower bound guarantees under highlighting sensitivity in finite scenarios. We evaluateRobInin both four virtual and three real UAVs, achieving 96.2% detection rates and 1.6% false alarm rates against 6 types of existing attacks, with only 3.82% runtime overhead (on average). Moreover, we illustrate the resilience ofRobInagainst the worst-case attack.

With the high proportion integration of photovoltaic power, the grid-tie inverter as a power electronic device has become one of the mainstream solutions. Considering that the sensors of the grid-tie inverter are vulnerable to exploitation by cyber and physical attacks, this article conducts a synthetic analysis of sensor attacks from the perspective of locations, strategies, and consequences. We find that sensor attacks in the low-frequency domain can cause a range of damages, including damping the output power, reducing power quality, and even burning out the inverter. To detect sensor attacks in changing environments, we propose a robust detector in the finite frequency domain, while the unknowns of varying system dynamics are decoupled. The detector design is formulated as a tractable optimization problem that can be solved numerically. To support real-time detection, an analytical solution form based on a quadratic programming reformulation with relaxed constraints is constructed. To the best of our knowledge, this is the first attempt to conduct the synthetic analysis of sensor attacks on the grid-tie inverter and address its robust detector design in the finite frequency domain under the parameter-varying model. Numerical simulations show that sensor attacks could bring severe damage but our detector can detect them effectively.

It has shown that with perfect knowledge of the system model and the capability to manipulate a certain number of measurements, the false data injection (FDI) attacks, as a class of data integrity attacks, can coordinate measurements corruption to keep stealth against the bad data detection schemes. However, a more realistic attack is essentially an attack with limited adversarial knowledge of the system model and limited attack resources due to various reasons. In this paper, we generalize the data attacks that they can be pure FDI attacks or combined with availability attacks (e.g., DoS attacks) and analyze the attacks with limited adversarial knowledge or limited attack resources. The attack impact is evaluated by the proposed metrics and the detection probability of attacks is calculated using the distribution property of data with or without attacks. The analysis is supported with results from a power system use case. The results show how important the knowledge is to the attacker and which measurements are more vulnerable to attacks with limited resources.