IDENTIFICATION OF MOBILE DEVICES BY CORRELATION FEATURES OF THEIR SIGNAL SPECTRA

Context. The mass spread of Wi-Fi networks is facilitated by the simplicity of their deployment, high speed, universality, and convenience of use. The development and dissemination of these networks continue despite a number of shortcomings. One of the shortcomings is their vulnerability to various types of attacks, including those based on the forgery (imitation) of identification data. At the same time, there are physical layer characteristics, knowledge of which expands the understanding of the network’s state, can contribute to increasing the reliability of network subscriber identification, and thus prevent a number of attacks. This research is aimed at the theoretical and practical substantiation of the possibility of their application. Objective. The aim of the study is to assess the application of detailed analysis of signal spectra emitted by devices connected to wireless Wi-Fi networks for their identification. To achieve this goal, it is necessary to analyze the experimentally measured spectra of wireless devices connected to the Wi-Fi network and evaluate the possibility of using the spectrum for the identification of mobile devices. Method. This work proposes a method for processing the results of measuring the spectra of Wi-Fi device emissions by evaluating the asymmetry coefficient of the Wi-Fi device spectrum’s cross-correlation function. Mathematical modeling was used to assess the effectiveness of the method. Results. The research results show that the minimum value of the asymmetry coefficient when comparing the template with different positions of one’s own device, and large values of the asymmetry coefficient when comparing templates with foreign spectra. Therefore, this characteristic can also be used for the identification of Wi-Fi devices. Conclusions. The research results suggest the possibility of applying the proposed method for the identification of mobile devices, which will qualitatively complement existing security models with another feature for detecting unauthorized access.