Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM ena...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of lightwave technology Vol. 37; no. 7; pp. 1742 - 1749
Main Authors:	Chen, Xiaoliang, Li, Baojia, Proietti, Roberto, Zhu, Zuqing, Yoo, S. J. Ben
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Anomalies Anomaly detection Artificial intelligence Artificial neural networks Clustering Clustering algorithms Cognition data clustering module Data clustering module (DCM) data regression and classification module data regression and classification module (DRCM) DCM DRCM ENGINEERING hybrid unsupervised and supervised machine learning Knowledge engineering Knowledge management Machine learning Modules Monitoring Optical communication Optical fiber networks Regression analysis self-taught anomaly detection
ISSN:	0733-8724, 1558-2213
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!