Machine Learning for Optical Network Security Management
Paper in proceeding, 2020
We discuss the role of supervised, unsupervised and semi-supervised learning techniques in identification of optical network security breaches. The applicability, performance and challenges related to practical deployment of these techniques are examined.
Author
Marija Furdek Prekratic
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Carlos Natalino Da Silva
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Conference on Optical Fiber Communication, Technical Digest Series
21622701 (eISSN)
Vol. Part F174-OFC 2020 M4E.4978-194358071-2 (ISBN)
Optical Fiber Communication Conference & Exhibition (OFC)
San Diego, USA,
San Diego, USA,
Safeguarding optical communication networks from cyber-security attacks
Swedish Research Council (VR) (2019-05008), 2020-01-01 -- 2023-12-31.
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 2025)
Security, Privacy and Cryptography
Subject Categories (SSIF 2011)
Communication Systems
Information Science
Computer Systems
DOI
10.1364/OFC.2020.M4E.4