A Lightweight Machine Learning Based Physical Layer Authentication Design for UAV Communications
Nurzhaubayeva G. Dashtifard N. Yedilkhan D. Mahmoud H. Cebecioglu B.B. Mi D.
2025Institute of Electrical and Electronics Engineers Inc.
IEEE European Technology and Engineering Management Summit, E-TEMS
2025Issue 2025340 - 345 pp.
The rapid development and deployment of Unmanned Aerial Vehicles (UAV)-enabled wireless networks, while promising capabilities in providing ubiquitous connectivity and extended coverage, introduces critical security vulnerabilities that demand robust countermeasures. This paper presents a comprehensive framework for Physical Layer Authentication (PLA) in UAV communications, leveraging machine learning techniques to enhance authentication and threat detection mechanisms. We propose a novel approach that integrates Support Vector Machine (SVM) classification with traditional PLA methods to identify and mitigate security threats in real time. Our framework exploits the inherent properties of wireless channels and signal characteristics in UAV communications to establish reliable authentication protocols. Experimental results demonstrate that our proposed system achieves a 97% detection accuracy for various attack vectors, including eavesdropping and attacking attempts, while maintaining low computational overhead. The frameworks effectiveness is validated through extensive simulations in diverse UAV communication scenarios, showcasing its potential for the advancement of secure UAV communications by establishing a robust and adaptive data-driven security paradigm.
authentication protocols , machine learning , physical layer authentication (PLA) , support vector machine (SVM) , unmanned aerial vehicles (UAV) , wireless security
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Astana IT University, Astana, Kazakhstan
Birmingham City University, College of Computing, Birmingham, United Kingdom
Astana IT University
Birmingham City University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026