Machine Learning Analysis of Polarization Signatures for Distinguishing Harmful from Non-harmful Fiber Events
Paper in proceeding, 2024
harmful vibration
eavesdropping
polarization signatures
Polarization state movement
machine learning
Author
Leyla Sadighi
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Stefan Karlsson
Swedish Defence Materiel Administration
Lena Wosinska
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Marija Furdek Prekratic
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
International Conference on Transparent Optical Networks
21627339 (ISSN)
9798350377309 (ISBN)
Bari, Italy,
Providing Resilient & secure networks [Operating on Trusted Equipment] to CriTical infrastructures (PROTECT)
VINNOVA (2020-03506), 2021-02-01 -- 2024-01-31.
InfraTrust: Enabling trustworthy services over vulnerable physical network infrastructure
Swedish Research Council (VR) (2023-05249), 2024-01-01 -- 2027-12-31.
5G Trusted And seCure network servICes (5G-TACTIC)
European Commission (EC) (EC/H2020/101127973), 2023-12-01 -- 2026-11-30.
Gender Initiative for Excellence (Genie)
The Chalmers University Foundation, 2019-01-01 -- 2028-12-31.
Areas of Advance
Information and Communication Technology
Subject Categories (SSIF 2011)
Telecommunications
Communication Systems
Computer Systems
DOI
10.1109/ICTON62926.2024.10648140