Machine Learning for Optical Network Security Monitoring: A Practical Perspective
Review article, 2020

In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This paper focuses on the challenges related to the performance of ML-based approaches for detection
and localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs).

We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL) and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false negatives is addressed by a newly proposed Window-based Attack Detection (WAD) approach. We provide practical implementationĀ guidelines for the integration of the framework into the NMS and evaluate its performance in an experimental network testbed subjected to attacks, resulting with the largest optical-layer security experimental dataset reported to date.


optical network security

attack detection

machine learning


Marija Furdek Prekratic

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Carlos Natalino Da Silva

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Fabian Lipp

Infosim GmbH & Co. KG

David Hock

Infosim GmbH & Co. KG

Andrea Di Giglio

Telecom Italia S.P.A

Marco Schiano

Telecom Italia S.P.A

Journal of Lightwave Technology

0733-8724 (ISSN) 1558-2213 (eISSN)

Vol. 38 11 2860-2871 9064530

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

Driving Forces

Sustainable development

Subject Categories




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