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In the realm of unmanned aerial vehicle (UAV) communication, the utilization of UAVs as aerial relays for ground nodes (GNs) introduces strategic flexibility, especially in scenarios where ground base stations may experience unforeseen impairments. However, this form of communication is vulnerable to eavesdropping by malicious entities due to the broadcast nature of wireless channels. In this paper, we tackle this problem by introducing a spatiotemporal diversification-based artificial noise (AN) injection strategy, known as moving target defense (MTD), aiming to confuse potential attackers without compromising legitimate communications. The proposed approach targets maximizing the average secrecy rate (ASR) by jointly optimizing the UAV's trajectory and transmit power, aligned with optimizing the MTD transmit power splitting factor between legitimate and AN signals at the GN source. The formulated problem is a non-convex mixed integer nonlinear programming (MINLP) problem due to the non-convexity of the secrecy rate. To solve it, we formulate our system as a Markov decision process, and then we propose a novel deep reinforcement learning (DRL)-based approach to enhance the ASR under various system constraints. Numerical results demonstrate the superiority of the proposed algorithm over benchmarks in terms of ASR and intercept probability, showcasing its effectiveness in enhancing the security of UAV-assisted communications.