Publication detail

Utilizing a State of Polarization Change Detector and Machine Learning for Enhanced Security in Fiber-Optic Networks

TOMAŠOV, A. DEJDAR, P. MÜNSTER, P. HORVÁTH, T.

Original Title

Utilizing a State of Polarization Change Detector and Machine Learning for Enhanced Security in Fiber-Optic Networks

Type

conference paper

Language

English

Original Abstract

The paper presents a novel method for securing fiber-optic infrastructures using a state of polarization analyzer combined with machine learning algorithms. The proposed system detects vibrations indicative of security breaches, achieving an F1-score above 95.65 %.}

Keywords

Fiber Bragg gratings; Fiber networks; Machine learning; Neural networks; Optical fibers; Polarization

Authors

TOMAŠOV, A.; DEJDAR, P.; MÜNSTER, P.; HORVÁTH, T.

Released

5. 5. 2024

Publisher

Optica Publishing Group

ISBN

978-1-957171-39-5

Book

CLEO: Applications and Technology 2024

Pages count

2

URL

https://opg.optica.org/abstract.cfm?uri=CLEO_AT-2024-JTu2A.217

BibTex

@inproceedings{BUT189498,
  author="Adrián {Tomašov} and Petr {Dejdar} and Petr {Münster} and Tomáš {Horváth}",
  title="Utilizing a State of Polarization Change Detector and Machine Learning for Enhanced Security in Fiber-Optic Networks",
  booktitle="CLEO: Applications and Technology 2024",
  year="2024",
  pages="2",
  publisher="Optica Publishing Group",
  doi="10.1364/CLEO\{_}AT.2024.JTu2A.217",
  isbn="978-1-957171-39-5",
  url="https://opg.optica.org/abstract.cfm?uri=CLEO_AT-2024-JTu2A.217"
}