Information-extreme identification of unmanned aerial vehicles

Igor Shelehov, Dmytro Prylepa, Dmytro Olefirenko, Roman Krytskyi

Abstract


This research aims to detect and identify unmanned aerial vehicles (UAVs) by analyzing the network traffic they transmit to their ground control station. The relevance of this topic arises from the need to develop highly efficient UAV identification systems, given their widespread use across the military, civilian, and commercial sectors. Effective solutions for detecting, identifying, and classifying these devices are required. This study aims to develop models and methods that enable machine-learning-based UAV identification systems by analyzing incoming and outgoing data traffic using an extremely intelligent information technology. A methodology is proposed for applying extreme information technology to UAV identification. This methodology involves employing pattern recognition techniques and designing intelligent information systems. The scientific novelty lies in introducing an approach to modeling an intelligent system capable of learning to identify UAVs based on traffic characteristics analysis. The research methodology includes the following steps: (1) forming a training dataset based on UAV traffic parameters, (2) building a UAV identification system using an information-extreme approach, and (3) training the system under standard operating conditions of digital radio communication channels used by UAVs. The results demonstrate that the proposed intelligent information system provides high UAV identification accuracy. Testing achieved an average identification accuracy of 86%. Conclusions. The proposed UAV identification system is based on an innovative approach to network traffic analysis using information-extreme intelligent technology. The results confirm its effectiveness for identification tasks under standard regulated traffic characteristics. The obtained results have practical significance for developing monitoring and protection systems in various fields against potential threats associated with UAV usage.

Keywords


unmanned aerial vehicles; information-extreme machine learning; information criterion; functional-categorical model; traffic analysis; container of the recognition class

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DOI: https://doi.org/10.32620/reks.2025.4.10

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