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Conferences > OFC > 2024 > M1H > M1H.1

Machine Learning-Based Polarization Signature Analysis for Detection and Categorization of Eavesdropping and Harmful Events

Leyla Sadighi, Stefan Karlsson, Carlos Natalino, and Marija Furdek

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Abstract

We propose a methodology that uses polarization state changes and machine learning to detect and classify eavesdropping, harmful, and non-harmful events in the optical fiber network. Our solution achieves 92.3% accuracy over 13 experimental scenarios.

© 2024 The Author(s)

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