Optical Network Security Management: Requirements, Architecture and Efficient Machine Learning Models for Detection of Evolving Threats [Invited]
Journal article, 2021

As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating machine learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing a novel functional block called Security Operation Center (SOC), describing its architecture, specifying key requirements on the supported functionalities and providing guidelines on its integration with optical layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between ML accuracy and run time complexity.

Learning systems

Economic and social effects

Fiber optic networks

Infrastructure as a service (IaaS)

Dimensionality reduction

Semi-supervised learning

Network architecture


Optical communication


Marija Furdek Prekratic

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Carlos Natalino Da Silva

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Andrea Di Giglio

Telecom Italia S.P.A

Marco Schiano

Telecom Italia S.P.A

Journal of Optical Communications and Networking

1943-0620 (ISSN)

Vol. 13 2 A144-A155 9308059

Safeguarding optical communication networks from cyber-security attacks

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

Areas of Advance

Information and Communication Technology

Subject Categories

Communication Systems

Computer Science

Computer Systems



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