Radar Spectrum Analysis and Machine Learning-Based Classification for Identity-Based Unmanned Aerial Vehicles Detection and Authentication

The significant use of Unmanned Aerial Vehicles (UAVs) in commercial and civilian applications presents various cybersecurity challenges, particularly in detection and authentication. Unauthorized UAVs can be very harmful to the people on the ground, the infrastructure, the right to privacy, and other UAVs. Moreover, using the internet for UAV communication may expose authorized ones to attacks, causing a loss of confidentiality, integrity, and information availability. This paper introduces radar-based UAV detection and authentication using Micro-Doppler (MD) signal analysis. The study provides a unique dataset comprising radar signals from three distinct UAV models captured under varying operational conditions. The dataset enables the analysis of specific features and classification through machine learning models, including k-nearest Neighbor (k-NN), Random Forest, and Support Vector Machine (SVM). The approach leverages radar signal processing to extract MD signatures for accurate UAV identification, enhancing detection and authentication processes. The result indicates that Random Forest achieved the highest accuracy of 100%, with high classification accuracy and zero false alarms, demonstrating its suitability for real-time monitoring. This also highlights the potential of radar-based MD analysis for UAV detection, and it establishes a foundational approach for developing robust UAV monitoring systems, with potential applications in aviation military surveillance, public safety, and regulatory compliance. Future work will focus on expanding the dataset and integrating Remote Identification (RID) policy. A policy that mandates UAVs to disclose their identity upon approaching any territory, this will help to enhance security and scalability of the system.