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MÜNSTER, P.; TOMAŠOV, A.; DEJDAR, P.; HORVÁTH, T.
Original Title
Deploying Machine Learning in Distributed Sensing to Increase Resilience of Fiber Optic Infrastructure
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
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.
English abstract
Keywords
Fiber optic communications;Mach Zehnder interferometers;Neural networks;Optical networks;Phase modulation;Polarization
Key words in English
Authors
RIV year
2024
Released
07.05.2023
Publisher
Optica Publishing Group
Location
San Jose, CA, USA
ISBN
978-1-957171-25-8
Book
2023 Conference on Lasers and Electro-Optics (CLEO)
Pages count
2
URL
https://opg.optica.org/abstract.cfm?uri=CLEO_SI-2023-JW2A.102
BibTex
@inproceedings{BUT184183, author="Petr {Münster} and Adrián {Tomašov} and Petr {Dejdar} and Tomáš {Horváth}", title="Deploying Machine Learning in Distributed Sensing to Increase Resilience of Fiber Optic Infrastructure", booktitle="2023 Conference on Lasers and Electro-Optics (CLEO)", year="2023", pages="2", publisher="Optica Publishing Group", address="San Jose, CA, USA", isbn="978-1-957171-25-8", url="https://opg.optica.org/abstract.cfm?uri=CLEO_SI-2023-JW2A.102" }