IEEE - Institute of Electrical and Electronics Engineers, Inc. - Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

Author(s): Xiaoliang Chen ; Baojia Li ; Roberto Proietti ; Zuqing Zhu ; S. J. Ben Yoo
Sponsor(s): IEEE Lasers and Electro-Optics Society
Publisher: IEEE - Institute of Electrical and Electronics Engineers, Inc.
Publication Date: 1 April 2019
Volume: 37
Page(s): 1,742 - 1,749
ISSN (Electronic): 1558-2213
ISSN (Paper): 0733-8724
DOI: 10.1109/JLT.2019.2902487
Regular:

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... View More

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