Publication detail

Deep Learning-Based Radio Frequency Identification of False Base Stations

BOLCEK, J. KUFA, J. HARVÁNEK, M. POLÁK, L. KRÁL, J. MARŠÁLEK, R.

Original Title

Deep Learning-Based Radio Frequency Identification of False Base Stations

Type

conference paper

Language

English

Original Abstract

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.

Keywords

5G Open RAN, 4G/5G SRS RAN, Deep Learning, RF measurement, I/Q-data

Authors

BOLCEK, J.; KUFA, J.; HARVÁNEK, M.; POLÁK, L.; KRÁL, J.; MARŠÁLEK, R.

Released

21. 11. 2023

Publisher

IEEE

Location

Riga, Latvia

ISBN

979-8-3503-9349-1

Book

2023 Workshop on Microwave Theory and Technology in Wireless Communications (MTTW)

Pages from

45

Pages to

49

Pages count

5

URL

BibTex

@inproceedings{BUT185786,
  author="Jan {Bolcek} and Jan {Kufa} and Michal {Harvánek} and Ladislav {Polák} and Jan {Král} and Roman {Maršálek}",
  title="Deep Learning-Based Radio Frequency Identification of False Base Stations",
  booktitle="2023 Workshop on Microwave Theory and Technology in Wireless Communications (MTTW)",
  year="2023",
  pages="45--49",
  publisher="IEEE",
  address="Riga, Latvia",
  doi="10.1109/MTTW59774.2023.10320078",
  isbn="979-8-3503-9349-1",
  url="https://ieeexplore.ieee.org/document/10320078"
}