Publication detail

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

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

Original Title

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

Type

conference paper

Language

English

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.

Keywords

Fiber optic communications;Mach Zehnder interferometers;Neural networks;Optical networks;Phase modulation;Polarization

Authors

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

Released

7. 5. 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"
}