Detection and Classification of Eavesdropping and Mechanical Vibrations in Fiber Optical Networks by Analyzing Polarization Signatures Over a Noisy Environment
Year: 2024
Type: article
Abstract: 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.
Authors: Sadighi, Leyla, Karlsson, Stefan, Natalino Da Silva, Carlos, Wosinska, Lena, Ruffini, Marco, Furdek Prekratic, Marija
Language: English
Topic: Optical Network Technologies
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funders: Trinity College Dublin, Vetenskapsrådet
OpenAlex ID: W6981067777