Demonstration of machine-learning-assisted security monitoring in optical networks

2019 ◽  
Author(s):  
M. Furdek ◽  
C. Natalino ◽  
F. Lipp ◽  
D. Hock ◽  
N. Aerts ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Security Monitoring

scholarly journals Enhancing Lightpath QoT Computation With Machine Learning in Partially Disaggregated Optical Networks

2021 ◽  
Vol 2 ◽  
pp. 564-574
Author(s):  
Andrea D'Amico ◽  
Stefano Straullu ◽  
Giacomo Borraccini ◽  
Elliot London ◽  
Stefano Bottacchi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Networks

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

scholarly journals Decentralizing machine-learning-based QoT estimation for sliceable optical networks

2020 ◽  
Vol 12 (7) ◽  
pp. 146 ◽  
Author(s):  
Tania Panayiotou ◽  
Giannis Savva ◽  
Ioannis Tomkos ◽  
Georgios Ellinas
Keyword(s):  
Machine Learning ◽  
Optical Networks

scholarly journals Overview on routing and resource allocation based machine learning in optical networks

2020 ◽  
Vol 60 ◽  
pp. 102355
Author(s):  
Yongjun Zhang ◽  
Jingjie Xin ◽  
Xin Li ◽  
Shanguo Huang
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Optical Networks

scholarly journals Experimental Study of Machine-Learning-Based Detection and Identification of Physical-Layer Attacks in Optical Networks

2019 ◽  
Vol 37 (16) ◽  
pp. 4173-4182 ◽  
Author(s):  
Carlos Natalino ◽  
Marco Schiano ◽  
Andrea Di Giglio ◽  
Lena Wosinska ◽  
Marija Furdek
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Optical Networks ◽  
Physical Layer ◽  
Detection And Identification
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