Classification of Events Violating the Safety of Physical Layers in Fiber-optic Network Infrastructures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146420" target="_blank" >RIV/00216305:26220/22:PU146420 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/1424-8220/22/23/9515" target="_blank" >https://www.mdpi.com/1424-8220/22/23/9515</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s22239515" target="_blank" >10.3390/s22239515</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification of Events Violating the Safety of Physical Layers in Fiber-optic Network Infrastructures

Popis výsledku v původním jazyce

Fiber-optic network infrastructures are crucial for the transmission of data over long and short distances. Fiber optics are also preferred for the infrastructure of in-building data communications. In this study, we use polarization analysis to ensure the security of the optical fiber/cables of the physical layer. This method exploits the changes induced by mechanical vibrations to polarization states, which can be easily detected using a polarization beam splitter and a balancing photodetector. We use machine learning to classify selected events that violate the safety of the physical layer, such as manipulation or temporary disconnection of connectors. The results show the resting state can be accurately distinguished from selected security breaches for a fiber route subjected to environmental disturbances, where individual events can be classified with nearly 99 % accuracy.

Název v anglickém jazyce

Classification of Events Violating the Safety of Physical Layers in Fiber-optic Network Infrastructures

Popis výsledku anglicky

Fiber-optic network infrastructures are crucial for the transmission of data over long and short distances. Fiber optics are also preferred for the infrastructure of in-building data communications. In this study, we use polarization analysis to ensure the security of the optical fiber/cables of the physical layer. This method exploits the changes induced by mechanical vibrations to polarization states, which can be easily detected using a polarization beam splitter and a balancing photodetector. We use machine learning to classify selected events that violate the safety of the physical layer, such as manipulation or temporary disconnection of connectors. The results show the resting state can be accurately distinguished from selected security breaches for a fiber route subjected to environmental disturbances, where individual events can be classified with nearly 99 % accuracy.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

SENSORS

ISSN

1424-8220

e-ISSN

1424-3210

Svazek periodika

22

Číslo periodika v rámci svazku

23

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

13

Strana od-do

„“-„“

Kód UT WoS článku

000897511800001

EID výsledku v databázi Scopus

2-s2.0-85143831976