Deep Learning-Based Radio Frequency Identification of False Base Stations

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Learning-Based Radio Frequency Identification of False Base Stations

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Deep Learning-Based Radio Frequency Identification of False Base Stations

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/VK01030166" target="_blank" >VK01030166: Krádeže identity základnových stanic v kontextu Open RAN: Kybernetická odonost 5G sítí založená na ověřování parametrů fyzické vrstvy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

979-8-3503-9349-1

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

45-49

Název nakladatele

IEEE

Místo vydání

Riga, Latvia

Místo konání akce

Riga

Datum konání akce

4. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—