A survey of machine learning for network fault management

Mourad Nouioua; Philippe Fournier-Viger; Ganghuan He; Farid Nouioua; Zhou Min

doi:10.1007/978-3-030-66288-2_1

A survey of machine learning for network fault management

Mourad Nouioua, Philippe Fournier-Viger^*, Ganghuan He, Farid Nouioua, Zhou Min

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

11 Scopus citations

Abstract

Telecommunication networks play a major role in today's society as they support the transmission of information between businesses, governments, and individuals. Hence, ensuring excellent service quality and avoiding service disruptions are important. For this purpose, fault management is critical. It consists of detecting, isolating, and fixing network problems, a task that is complex for large networks, and typically requires considerable resources. As a result, an emerging research area is to develop machine learning and data mining-based techniques to improve various aspects of the fault management process. This chapter provides a survey of data mining and machine learning-based techniques for fault management, including a description of their characteristics, similarities, differences, and shortcomings.

Original language	English
Title of host publication	Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics
Subtitle of host publication	Theories and Applications
Publisher	Springer International Publishing
Pages	1-27
Number of pages	27
ISBN (Electronic)	9783030662882
ISBN (Print)	9783030662875
DOIs	https://doi.org/10.1007/978-3-030-66288-2_1
State	Published - 1 Apr 2021
Externally published	Yes

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

ASJC Scopus subject areas

General Engineering
General Computer Science
General Economics, Econometrics and Finance
General Business, Management and Accounting

Access to Document

10.1007/978-3-030-66288-2_1

Cite this

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