Machine Learning-Based Polarization Signature Analysis for Detection and Categorization of Eavesdropping and Harmful Events
Paper in proceeding, 2024

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.

Author

Leyla Sadighi

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Stefan Karlsson

Swedish Defence Materiel Administration

Carlos Natalino Da Silva

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Marija Furdek Prekratic

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 - Proceedings


9781957171326 (ISBN)

2024 Optical Fiber Communications Conference and Exhibition, OFC 2024
San Diego, USA,

Providing Resilient & secure networks [Operating on Trusted Equipment] to CriTical infrastructures (PROTECT)

VINNOVA (2020-03506), 2021-02-01 -- 2024-01-31.

Safeguarding optical communication networks from cyber-security attacks

Swedish Research Council (VR) (2019-05008), 2020-01-01 -- 2023-12-31.

Subject Categories

Communication Systems

Computer Systems

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1364/OFC.2024.M1H.1

More information

Latest update

10/28/2024