Deep Learning-Based Radio Frequency Identification of False Base Stations

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149881" target="_blank" >RIV/00216305:26220/23:PU149881 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10320078" target="_blank" >https://ieeexplore.ieee.org/document/10320078</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MTTW59774.2023.10320078" target="_blank" >10.1109/MTTW59774.2023.10320078</a>

Result language
angličtina
Original language name
Deep Learning-Based Radio Frequency Identification of False Base Stations
Original language description
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.
Czech name
—
Czech description
—

Project
<a href="/en/project/VK01030166" target="_blank" >VK01030166: Identity falsification of base stations in Open RAN context: Cybersecurity of 5G networks based on physical layer parameters</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2023 Workshop on Microwave Theory and Technology in Wireless Communications (MTTW)
ISBN
979-8-3503-9349-1
ISSN
—
e-ISSN
—
Number of pages
5
Pages from-to
45-49
Publisher name
IEEE
Place of publication
Riga, Latvia
Event location
Riga
Event date
Oct 4, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)