Network Intrusion Detection Systems Using Supervised Machine Learning Classification and Dimensionality Reduction Techniques: A Systematic Review

Protecting the confidentiality, integrity, and availability of cyberspace and network (NW) assets has become an increasing concern. The rapid increase in the Internet size and the presence of new computing systems (like Cloud) are creating great incentives for intruders. Therefore, security engineer...

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Autores principales:	Zein Ashi, Laila Aburashed, Mahmoud Al-Qudah, Abdallah Qusef
Formato:	article
Lenguaje:	EN
Publicado:	Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT) 2021
Materias:	network intrusion detection machine learning supervised learning dimensionality systematic review Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/4c3adaf2461e41e4a12e79870c9e8177
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spelling	oai:doaj.org-article:4c3adaf2461e41e4a12e79870c9e81772021-12-03T07:32:06ZNetwork Intrusion Detection Systems Using Supervised Machine Learning Classification and Dimensionality Reduction Techniques: A Systematic Review2413-935110.5455/jjcit.71-1629527707https://doaj.org/article/4c3adaf2461e41e4a12e79870c9e81772021-12-01T00:00:00Zhttp://www.ejmanager.com/fulltextpdf.php?mno=113547https://doaj.org/toc/2413-9351Protecting the confidentiality, integrity, and availability of cyberspace and network (NW) assets has become an increasing concern. The rapid increase in the Internet size and the presence of new computing systems (like Cloud) are creating great incentives for intruders. Therefore, security engineers have to develop new technologies to match growing threats to NWs. New and advanced technologies have emerged to create more efficient intrusion detection systems using machine learning (ML) and dimensionality reduction techniques, to help security engineers bolster more effective NW Intrusion Detection Systems (NIDS). This systematic review provides a comprehensive review of the most recent NIDS using the supervised ML classification and dimensionality reduction techniques, it shows how the used ML classifiers, dimensionality reduction techniques, and evaluating metrics have improved NIDS construction. The key point of this study is to provide up-to-date knowledge for new interested researchers. [JJCIT 2021; 7(4.000): 373-390]Zein AshiLaila AburashedMahmoud Al-QudahAbdallah QusefScientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)articlenetwork intrusion detectionmachine learningsupervised learningdimensionalitysystematic reviewInformation technologyT58.5-58.64Electronic computers. Computer scienceQA75.5-76.95ENJordanian Journal of Computers and Information Technology , Vol 7, Iss 4, Pp 373-390 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	network intrusion detection machine learning supervised learning dimensionality systematic review Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
spellingShingle	network intrusion detection machine learning supervised learning dimensionality systematic review Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95 Zein Ashi Laila Aburashed Mahmoud Al-Qudah Abdallah Qusef Network Intrusion Detection Systems Using Supervised Machine Learning Classification and Dimensionality Reduction Techniques: A Systematic Review
description	Protecting the confidentiality, integrity, and availability of cyberspace and network (NW) assets has become an increasing concern. The rapid increase in the Internet size and the presence of new computing systems (like Cloud) are creating great incentives for intruders. Therefore, security engineers have to develop new technologies to match growing threats to NWs. New and advanced technologies have emerged to create more efficient intrusion detection systems using machine learning (ML) and dimensionality reduction techniques, to help security engineers bolster more effective NW Intrusion Detection Systems (NIDS). This systematic review provides a comprehensive review of the most recent NIDS using the supervised ML classification and dimensionality reduction techniques, it shows how the used ML classifiers, dimensionality reduction techniques, and evaluating metrics have improved NIDS construction. The key point of this study is to provide up-to-date knowledge for new interested researchers. [JJCIT 2021; 7(4.000): 373-390]
format	article
author	Zein Ashi Laila Aburashed Mahmoud Al-Qudah Abdallah Qusef
author_facet	Zein Ashi Laila Aburashed Mahmoud Al-Qudah Abdallah Qusef
author_sort	Zein Ashi
title	Network Intrusion Detection Systems Using Supervised Machine Learning Classification and Dimensionality Reduction Techniques: A Systematic Review
title_short	Network Intrusion Detection Systems Using Supervised Machine Learning Classification and Dimensionality Reduction Techniques: A Systematic Review
title_full	Network Intrusion Detection Systems Using Supervised Machine Learning Classification and Dimensionality Reduction Techniques: A Systematic Review
title_fullStr	Network Intrusion Detection Systems Using Supervised Machine Learning Classification and Dimensionality Reduction Techniques: A Systematic Review
title_full_unstemmed	Network Intrusion Detection Systems Using Supervised Machine Learning Classification and Dimensionality Reduction Techniques: A Systematic Review
title_sort	network intrusion detection systems using supervised machine learning classification and dimensionality reduction techniques: a systematic review
publisher	Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)
publishDate	2021
url	https://doaj.org/article/4c3adaf2461e41e4a12e79870c9e8177
work_keys_str_mv	AT zeinashi networkintrusiondetectionsystemsusingsupervisedmachinelearningclassificationanddimensionalityreductiontechniquesasystematicreview AT lailaaburashed networkintrusiondetectionsystemsusingsupervisedmachinelearningclassificationanddimensionalityreductiontechniquesasystematicreview AT mahmoudalqudah networkintrusiondetectionsystemsusingsupervisedmachinelearningclassificationanddimensionalityreductiontechniquesasystematicreview AT abdallahqusef networkintrusiondetectionsystemsusingsupervisedmachinelearningclassificationanddimensionalityreductiontechniquesasystematicreview
_version_	1718373809961041920

Network Intrusion Detection Systems Using Supervised Machine Learning Classification and Dimensionality Reduction Techniques: A Systematic Review

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