Machine Learning for Optical Network Security Monitoring: A Practical Perspective
Reviewartikel, 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 implementation 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

machine learning

monitoring

attack detection

Författare

Marija Furdek Prekratic

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Carlos Natalino Da Silva

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Fabian Lipp

Infosim GmbH & Co. KG

David Hock

Infosim GmbH & Co. KG

Andrea Di Giglio

Telecom Italia

Marco Schiano

Telecom Italia

Journal of Lightwave Technology

0733-8724 (ISSN)

Vol. 38 11 2860-2871 9064530

Skydda optiska kommunikationsnätverk från cyber-säkerhetsattacker

Vetenskapsrådet (VR), 2020-01-01 -- 2023-12-31.

Styrkeområden

Informations- och kommunikationsteknik

Drivkrafter

Hållbar utveckling

Ämneskategorier

Telekommunikation

DOI

10.1109/JLT.2020.2987032

Mer information

Senast uppdaterat

2020-06-29