Deploying Machine Learning in Distributed Sensing to Increase Resilience of Fiber Optic Infrastructure

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148652" target="_blank" >RIV/00216305:26220/23:PU148652 - isvavai.cz</a>
Result on the web
<a href="https://opg.optica.org/abstract.cfm?uri=CLEO_SI-2023-JW2A.102" target="_blank" >https://opg.optica.org/abstract.cfm?uri=CLEO_SI-2023-JW2A.102</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Deploying Machine Learning in Distributed Sensing to Increase Resilience of Fiber Optic Infrastructure
Original language description
We report a novel approach to the security of fiber optic infrastructures utilizing state of polarization analyzes or Mach-Zehnder interferometry and using supervised or unsupervised machine-learning models for unauthorized cable manipulation detection.
Czech name
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Czech description
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Project
<a href="/en/project/VK01030048" target="_blank" >VK01030048: Anomaly detection in critical infrastructures using machine learning</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 Conference on Lasers and Electro-Optics (CLEO)
ISBN
978-1-957171-25-8
ISSN
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e-ISSN
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Number of pages
2
Pages from-to
„“-„“
Publisher name
Optica Publishing Group
Place of publication
San Jose, CA, USA
Event location
San Jose, CA
Event date
May 7, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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Result description