Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

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...

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Autores principales:	M. A. Ridwan, N. A. M. Radzi, F. Abdullah, Y. E. Jalil
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Machine learning algorithms communication network intrusion detection routing quality of service Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/63544c09bdc64bcf9e95ad6150a1a539
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spelling	oai:doaj.org-article:63544c09bdc64bcf9e95ad6150a1a5392021-11-19T00:05:28ZApplications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges2169-353610.1109/ACCESS.2021.3069210https://doaj.org/article/63544c09bdc64bcf9e95ad6150a1a5392021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9388670/https://doaj.org/toc/2169-3536Communication 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.M. A. RidwanN. A. M. RadziF. AbdullahY. E. JalilIEEEarticleMachine learning algorithmscommunication networkintrusion detectionroutingquality of serviceElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 52523-52556 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning algorithms communication network intrusion detection routing quality of service Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Machine learning algorithms communication network intrusion detection routing quality of service Electrical engineering. Electronics. Nuclear engineering TK1-9971 M. A. Ridwan N. A. M. Radzi F. Abdullah Y. E. Jalil Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges
description	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.
format	article
author	M. A. Ridwan N. A. M. Radzi F. Abdullah Y. E. Jalil
author_facet	M. A. Ridwan N. A. M. Radzi F. Abdullah Y. E. Jalil
author_sort	M. A. Ridwan
title	Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges
title_short	Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges
title_full	Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges
title_fullStr	Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges
title_full_unstemmed	Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges
title_sort	applications of machine learning in networking: a survey of current issues and future challenges
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/63544c09bdc64bcf9e95ad6150a1a539
work_keys_str_mv	AT maridwan applicationsofmachinelearninginnetworkingasurveyofcurrentissuesandfuturechallenges AT namradzi applicationsofmachinelearninginnetworkingasurveyofcurrentissuesandfuturechallenges AT fabdullah applicationsofmachinelearninginnetworkingasurveyofcurrentissuesandfuturechallenges AT yejalil applicationsofmachinelearninginnetworkingasurveyofcurrentissuesandfuturechallenges
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Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

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