MODELS AND METHODS FOR WIRELESS DEVICE IDENTIFICATION BASED ON RADIO FREQUENCY CHARACTERISTICS: A COMPREHENSIVE ANALYSIS OF MODERN RF-FINGERPRINTING APPROACHES
DOI:
https://doi.org/10.28925/2663-4023.2025.31.1012Keywords:
RF-fingerprinting, cybersecurity, threat detection, radio signal, attacks, radio-frequency identification, machine learning, signal processing, wireless security, physical-layer authentication, deep learning, spectral analysis, device classification.Abstract
This article presents a comprehensive analysis of state-of-the-art models and methods for wireless device identification based on their unique radio frequency characteristics (RF-fingerprinting). The fundamental principles of RF fingerprint formation, which arise from the intrinsic hardware imperfections of transmitter components, are examined in detail. The study analyzes the primary sources of signal uniqueness, including variations in oscillator parameters, power amplifier behavior, modulator nonlinearities, and antenna system characteristics. Existing RF-fingerprinting approaches are systematized according to feature extraction methodology, applied classification algorithms, and practical application domains. Special attention is devoted to signal processing techniques in the time, frequency, and time–frequency domains, including transient behavior analysis, spectral feature characterization, and wavelet transforms. The article provides a structured classification of machine learning algorithms used for device identification, ranging from traditional statistical models to advanced deep learning architectures. Hybrid approaches that combine multiple methodologies to improve identification accuracy and robustness are highlighted. The impact of environmental factors—such as multipath propagation, interference, and dynamic channel variation—on the effectiveness of RF-fingerprinting systems is thoroughly examined. Practical aspects of deploying RF-fingerprinting in cybersecurity systems, access control mechanisms, and counterfeit device detection are discussed. Key challenges and limitations of existing methods are identified, including scalability, adaptability to emerging device types, and resilience to intentional adversarial attacks. Finally, promising directions for the development of RF-fingerprinting technology are proposed, with particular emphasis on integration with artificial intelligence systems and emerging quantum-enabled signal processing methods.
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