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| Title | Advancing Uav Security With Artificial Intelligence: A Comprehensive Survey Of Techniques And Future Directions |
|---|---|
| ID_Doc | 6708 |
| Authors | Tlili F.; Ayed S.; Chaari Fourati L. |
| Year | 2024 |
| Published | Internet of Things (Netherlands), 27 |
| DOI | http://dx.doi.org/10.1016/j.iot.2024.101281 |
| Abstract | Unmanned Aerial Vehicles (UAVs) have become an integral part of modern smart cities and systems. However, the proliferation of UAVs has also brought a significant security concern. Therefore, UAVs security has attracted the interest of researchers in the field. To safeguard these leading devices, researchers and developers adopted artificial intelligence (AI) to improve the security system. Besides, emerging AI frameworks have entirely changed the landscape of UAVs quality of experience (QoE) by contributing to diverse issues. Meanwhile, owing to the novelty of the subject, AI-based frameworks for UAVs security were not carefully surveyed. The purpose of this study is to present a comprehensive survey of AI-based solutions for UAVs security provisioning through different techniques and algorithms. We assess and identify the recent AI solutions and the open research problems in the emerging field of UAV security. The study begins with an overview of UAV basics, their primary components, and the different communication links among networked UAVs. In addition, the paper explores UAVs security issues, threats, and attacks. This led to a discussion of proposed AI-based solutions for prevention and detection. Moreover, the paper categorizes AI-based solutions for UAV security and proposes a taxonomy to organize these approaches effectively. Finally, the review identifies open issues and future directions, offering valuable insights for researchers. The study methodology includes an extensive review of relevant publications in the past five years across UAVs, cyber-attacks, and AI. Therefore, this review presents an essential resource for understanding the current landscape of AI-based solutions for UAV security. © 2024 Elsevier B.V. |
| Author Keywords | AI-based UAV; Artificial intelligence; Deep learning; Machine learning; Reinforcement learning; Security; UAV |
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