Deep Learning-Based Radio Frequency Identification of False Base Stations

Advances in mobile and wireless communications allow to handle the continuously increasing demands on the data volume and connectivity of users. The 5G Open Radio Access Network (RAN) concept offers a flexible and inter-operable solution enabling network operators to select equipment from different...

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Bibliographic Details
Published in2023 Workshop on Microwave Theory and Technology in Wireless Communications (MTTW) pp. 45 - 49
Main Authors Bolcek, Jan, Kufa, Jan, Harvanek, Michal, Polak, Ladislav, Kral, Jan, Marsalek, Roman
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.10.2023
Subjects
4G/5G SRS RAN
5G mobile communication
5G Open RAN
Base stations
Computer architecture
Deep Learning
I/Q-data
Microwave measurement
Object recognition
Radio frequency
RF measurement
Wireless communication
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Summary:Advances in mobile and wireless communications allow to handle the continuously increasing demands on the data volume and connectivity of users. The 5G Open Radio Access Network (RAN) concept offers a flexible and inter-operable solution enabling network operators to select equipment from different vendors. However, such a step can potentially increase security risks due to emergence of the false base stations (FBS) operated with a purpose to steal private information about mobile equipment users. In this paper, we introduce a simple deep-learning (DL) based classification method, working directly with In-phase and Quadrature (I/Q) data of a radio frequency (RF) signal, to identify a device working as FBS. To operate the legitimate as well as the FBS, the srsRAN open-source software suite from Software Radio Systems (SRS), connected to three distinct software defined radio (SDR) devices, is used.
DOI:10.1109/MTTW59774.2023.10320078