Application of data mining technology in detecting network intrusion and security maintenance

In order to correct the deficiencies of intrusion detection technology, the entire computer and network security system are needed to be more perfect. This work proposes an improved k-means algorithm and an improved Apriori algorithm applied in data mining technology to detect network intrusion and...

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Autores principales:	Zhu Yongkuan, Gaba Gurjot Singh, Almansour Fahad M., Alroobaea Roobaea, Masud Mehedi
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	data mining intrusion detection k-means improved algorithm security maintenance Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/2c2e7184b68d40cf82bd25d616c8b3c7
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spelling	oai:doaj.org-article:2c2e7184b68d40cf82bd25d616c8b3c72021-12-05T14:10:51ZApplication of data mining technology in detecting network intrusion and security maintenance2191-026X10.1515/jisys-2020-0146https://doaj.org/article/2c2e7184b68d40cf82bd25d616c8b3c72021-05-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0146https://doaj.org/toc/2191-026XIn order to correct the deficiencies of intrusion detection technology, the entire computer and network security system are needed to be more perfect. This work proposes an improved k-means algorithm and an improved Apriori algorithm applied in data mining technology to detect network intrusion and security maintenance. The classical KDDCUP99 dataset has been utilized in this work for performing the experimentation with the improved algorithms. The algorithm’s detection rate and false alarm rate are compared with the experimental data before the improvement. The outcomes of proposed algorithms are analyzed in terms of various simulation parameters like average time, false alarm rate, absolute error as well as accuracy value. The results show that the improved algorithm advances the detection efficiency and accuracy using the designed detection model. The improved and tested detection model is then applied to a new intrusion detection system. After intrusion detection experiments, the experimental results show that the proposed system improves detection accuracy and reduces the false alarm rate. A significant improvement of 90.57% can be seen in detecting new attack type intrusion detection using the proposed algorithm.Zhu YongkuanGaba Gurjot SinghAlmansour Fahad M.Alroobaea RoobaeaMasud MehediDe Gruyterarticledata miningintrusion detectionk-means improved algorithmsecurity maintenanceScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 664-676 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data mining intrusion detection k-means improved algorithm security maintenance Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	data mining intrusion detection k-means improved algorithm security maintenance Science Q Electronic computers. Computer science QA75.5-76.95 Zhu Yongkuan Gaba Gurjot Singh Almansour Fahad M. Alroobaea Roobaea Masud Mehedi Application of data mining technology in detecting network intrusion and security maintenance
description	In order to correct the deficiencies of intrusion detection technology, the entire computer and network security system are needed to be more perfect. This work proposes an improved k-means algorithm and an improved Apriori algorithm applied in data mining technology to detect network intrusion and security maintenance. The classical KDDCUP99 dataset has been utilized in this work for performing the experimentation with the improved algorithms. The algorithm’s detection rate and false alarm rate are compared with the experimental data before the improvement. The outcomes of proposed algorithms are analyzed in terms of various simulation parameters like average time, false alarm rate, absolute error as well as accuracy value. The results show that the improved algorithm advances the detection efficiency and accuracy using the designed detection model. The improved and tested detection model is then applied to a new intrusion detection system. After intrusion detection experiments, the experimental results show that the proposed system improves detection accuracy and reduces the false alarm rate. A significant improvement of 90.57% can be seen in detecting new attack type intrusion detection using the proposed algorithm.
format	article
author	Zhu Yongkuan Gaba Gurjot Singh Almansour Fahad M. Alroobaea Roobaea Masud Mehedi
author_facet	Zhu Yongkuan Gaba Gurjot Singh Almansour Fahad M. Alroobaea Roobaea Masud Mehedi
author_sort	Zhu Yongkuan
title	Application of data mining technology in detecting network intrusion and security maintenance
title_short	Application of data mining technology in detecting network intrusion and security maintenance
title_full	Application of data mining technology in detecting network intrusion and security maintenance
title_fullStr	Application of data mining technology in detecting network intrusion and security maintenance
title_full_unstemmed	Application of data mining technology in detecting network intrusion and security maintenance
title_sort	application of data mining technology in detecting network intrusion and security maintenance
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/2c2e7184b68d40cf82bd25d616c8b3c7
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_version_	1718371662773092352

Application of data mining technology in detecting network intrusion and security maintenance

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