Publication: Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges
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Date
2021
Authors
Ridwan M.A.
Radzi N.A.M.
Abdullah F.
Jalil Y.E.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of software-defined networks and 5G. � 2013 IEEE.
Description
Intrusion detection; Machine learning; Quality of service; Complex problems; Computational model; Future challenges; Improve performance; Ml algorithms; Performance issues; Resource management; Rule based algorithms; Complex networks