Identity-based radar spectrum analysis for UAV detection, classification, and authentication using machine learning and blockchain
Aminu Abdulkadir Mahmoud1, 2, Sofia Najwa Ramli3, Mohd Aifaa Mohd Ariff4, Umar Shafiu Haruna2 and Aisha Umar Suleiman2
Department of Cybersecurity,Faculty of Computing, Northwest University,Kano,Nigeria2
Centre for Cybersecurity and Data Intelligence,Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia and Etienne Innovation Sdn. Bhd., Batu Pahat,Johor,Malaysia3
Faculty of Electrical and Electronic Engineering,Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia and Etienne Innovation Sdn. Bhd., Batu Pahat,Johor,Malaysia4
Corresponding Author : Aminu Abdulkadir Mahmoud
Recieved : 23-February-2025; Revised : 16-March-2026; Accepted : 17-April-2026
Abstract
The growing use of unmanned aerial vehicles (UAVs) has heightened security concerns related to identity verification and authentication. Existing systems can classify UAVs using micro-Doppler (m-D) signatures but lack reliable mechanisms to prevent spoofing and impersonation. Similarly, current authentication methods including remote identification (RID), blockchain, and cryptographic schemes face cybersecurity and scalability challenges. This study proposes a novel identity-based authentication model that integrates m-D signature analysis with blockchain technology. Radar systems extract eleven discriminative UAV features, which are classified using k-nearest neighbors (kNN), support vector machine (SVM), and random forest (RF) algorithms. The classified identities are cross-verified with blockchain-stored RID data to ensure tamper-resistant authentication. Experiments conducted on ten UAV models, including Da-Jiang innovations (DJI) and Autel series, achieved 99.9% accuracy using the RF classifier. Scalability is maintained through a practical Byzantine fault tolerance (PBFT) consensus mechanism in Hyperledger Fabric, enabling sub-250 ms authentication latency and throughput exceeding 1200 transactions per second, outperforming proof-of-work–based systems. The proposed model effectively combines radar-based m-D analysis with blockchain verification, ensuring secure, scalable, and tamper-proof UAV identification compliant with RID regulations, thereby strengthening airspace security and UAV communication integrity.
Keywords
Unmanned aerial vehicles (UAVs), Micro-doppler signatures, Blockchain authentication, Radar-based UAV identification, Machine learning classification, Secure UAV communication.
Cite this article
Mahmoud AA, Ramli SN, Ariff MAM, Haruna US, Suleiman AU. Identity-based radar spectrum analysis for UAV detection, classification, and authentication using machine learning and blockchain. International Journal of Advanced Technology and Engineering Exploration. 2026;13(137):512-540. DOI : 10.19101/IJATEE.2025.121220273
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