# Secure Beamforming Design for UAV-Empowered Integrated Sensing and Communication
###### Abstract
RIS is employed to optimize transmission beamforming weights and phase shifts, aiming to maximize the perceptual beam pattern gain irrespective of the eavesdropper's channel state knowledge [19]. Research on the security of UAV-assisted ISAC systems has just begun. In [20], the Stackelberg game model was utilized to analyze the power strategy game between UAVs and eavesdroppers, considering scenarios where the eavesdropper's channel state is both known and unknown. A distance-based UAV positioning optimization strategy aimed at maximizing the number of users receiving secure communication services was conducted in [21]. However, ref. [20] focused solely on a single-antenna model, whereas [21] implemented maximum ratio combining for transmit beamforming. Addressing the scenario in which multiple eavesdropping targets and communication users coexist, this paper proposes a joint optimization algorithm for UAV transmit beamforming and position. The algorithm employs the SDR and SCA methods for iterative optimization. We seek to minimize the maximum eavesdropping SINR while maintaining communication performance for users and sensing accuracy for targets, thereby bolstering the system's security. The principal contributions of this paper are outlined as follows:  
1. Given the security concerns of the UAV-assisted ISAC system, we tackle the challenge of detecting eavesdroppers while facilitating communication with legitimate users. Under the constraints of transmit power, legitimate communication quality of service, and sensing beam pattern gain, the problem is formulated as a joint optimization of UAV positioning and communication and sensing beamforming, aiming to minimize the maximum eavesdropping SINR. 2. Owing to the coupling between UAV communication and sensing beamforming, coupled with the intricate relationship between UAV positioning and steering vectors,the problem is inherently non-convex. To address this, the problem is decomposed into two sub-problems: beamforming optimization and UAV positioning optimization. For the UAV positioning sub-problem, a SCA algorithm, leveraging the first-order Taylor expansion, is employed to transform the non-convex problem into a convex one. This problem is subsequently resolved using the CVX toolbox. 3. For the beamforming optimization sub-problem, the max-min problem is addressed by introducing slack variables, and the fractional optimization problem is solved using the Dinkelbach method. The optimal beamforming is then determined using the semidefinite relaxation algorithm by ignoring the rank-one constraint and solving through eigenvalue decomposition. Simulation results indicate that the proposed algorithm substantially improves the security performance of the ISAC system, concurrently maintaining robust communication and sensing capabilities. This paper is structured as follows: Section 2 outlines the system model and the problem formulation; Section 3 details the joint optimization algorithm; Section 4 provides an analysis of the simulation results and performance; and Section 5 offers the conclusions. For ease of reference, essential notations employed in this paper are compiled in Table 1.