Intelligent Threat Determination and PM in Unmanned Aerial Vehicles: A Review of Deep Learning Perspectives - Sajjad, Ahmed; Ahmad Nizar, Yaakub; Syahrul Nizam, Junaini (World Scientific Publishing Co Pte Ltd, 2025)
This study delves into the impact of Deep Learning (DL) on UAV advancements from 2015 to 2025, focusing on early threat detection, anomaly recognition, and Predictive Maintenance (PM). By reviewing state-of-the-art DL models, we assess their role in mitigating cybersecurity threats, detecting flight anomalies, and optimizing UAV reliability. Public datasets such as ALFA and UAV Attack Dataset have been instrumental in evaluating these models for real-world applications. CNNs excel in spatial threat detection, LSTMs in sequential anomaly recognition, and Transformers in multi-modal sensor fusion. Despite these advancements, computational constraints, adversarial vulnerabilities, and real-time processing challenges persist. Future research must focus on energy-efficient AI, explainable models, and swarm intelligence to enhance UAV autonomy. This review provides a comprehensive decade-long perspective on DL-driven UAV innovations, shaping future developments in security and PM.