Defending UAV Networks Against Covert Attacks Using Auxiliary Signal Injections

Unmanned aerial vehicle (UAV) networks, which carry vital information, are prone to various attacks, and hence security issues are a major concern. In this paper, we design and implement a novel covert attack detection and secure control scheme, which operates between the UAV (the physical layer) and ground control station (GCS) (the cyber layer). Covert attacks can alter the UAV states, and yet cause the signals seen by the controller to appear unchanged, resulting in these attacks being more difficult to detect, and hence more dangerous compared to other types of attacks. To unmask the covert attacks, we construct and inject auxiliary signals to both the controller output and the UAV input. The auxiliary signals cause information of the attack to appear in the controller input, which is then fed to a detection observer to detect the attack. Next, we propose an integrated estimation and secure control scheme, comprising a reconstruction observer (which is a sliding mode observer (SMO)) that estimates the system states and attack signal, and an output-feedback controller that utilizes the estimated signals. We perform a series of transformations to the system, such that the design parameters of both reconstruction observer and secure controller are placed in a framework that is solvable using Linear Matrix Inequalities (LMIs). We also prove that the proposed integrated secure controller causes the output tracking errors to satisfy an ${{\mathcal {H}}}_{\infty }$ performance index. We also rigorously analyze the system performance, and present the necessary conditions for the scheme to be feasible. Finally, simulations are conducted to verify the effectiveness of the proposed scheme. Note to Practitioners—This paper presents a method to detect covert attacks in the UAV network, and to mitigate against those attacks. Covert attacks are more malicious since they are difficult to detect. The proposed method in this paper consists of auxiliary signal injection and a detection observer that will expose and detect the attacks, and a reconstruction observer and secure controller that will estimate the attacks and mitigate its effect on the plant. The controller and observers are designed using Linear Matrix Inequalities to minimize the ${{\mathcal {H}}}_{\infty }$ gain from the attack on the plant performance. In addition, this paper also investigates the conditions that the plant must satisfy such that the proposed scheme is feasible, and presents them in an easily verifiable form. Finally, the paper also analyses the performance of the proposed scheme in all scenarios - namely before the attack occurs, when the attack occurs but is not yet detected, and after the attack is detected.