Model of RF Fingerprinting of Wireless Devices Based on Signal Correlation

Abstract

This article proposes a novel approach to the identification of wireless devices using a radio frequency fingerprinting (RFF) model based on correlation functions. With the rise of Internet of Things (IoT) technologies and the increasing number of wireless devices, the task of ensuring their security and reliable identification has become increasingly relevant. Traditional cryptographic methods often prove unsuitable for low-budget devices with limited resources. This research focuses on the analysis of specific distortions in radio frequency signals that arise from hardware defects. The developed mathematical model enables the assessment of operability of devices and facilitates their identification. The study examines the challenges associated with measuring the parameters of wireless devices and presents a formalized approach that combines theoretical and practical aspects of radio frequency fingerprinting. Special attention is given to the formation of feature space and its impact on classification accuracy. As a result, the proposed RFF model demonstrates the capability for effective identification of IoT devices through the analysis of correlations between distorted and reference signals, offering a new pathway for the diagnosis and monitoring of the condition of wireless devices. The findings of this research promise significant improvements in the security and reliability of IoT-based systems, opening up new opportunities for the development of cost-effective and efficient solutions for the identification and protection of wireless technologies.