Detection and Classification of Eavesdropping and Mechanical Vibrations in Fiber Optical Networks by Analyzing Polarization Signatures Over a Noisy Environment
Paper in proceeding, 2024
We propose a machine-learning-based method to detect and classify eavesdropping and mechanical vibrations in an optical network based on state of polarization variations. Tests in two real-world installations with links of different lengths demonstrate an accuracy of 86.5% in 7 distinct normal and malicious scenarios.
Author
Leyla Sadighi
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Stefan Karlsson
Carlos Natalino Da Silva
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Lena Wosinska
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Marco Ruffini
Marija Furdek Prekratic
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
European Conference on Optical Communication, ECOC
Tu4E.5
2024 European Conference on Optical Communications (ECOC)
Frankfurt, Germany,
Frankfurt, Germany,
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VINNOVA (2020-03506), 2021-02-01 -- 2024-01-31.
Areas of Advance
Information and Communication Technology
Subject Categories
Communication Systems
Electrical Engineering, Electronic Engineering, Information Engineering
Signal Processing