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An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval

Currently, many companies use data mining for various implementations. One form of implementation is intrusion detection system (IDS). In IDS, the main problem for nuisance network administrators in detecting attacks is false alerts. Regardless of the methods implemented by this system, eliminating...

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Main Authors:	Slamet, Slamet, Izzeldin, Ibrahim Mohamed Abdelaziz
格式:	Article
語言:	English
出版:	Institute of Advanced Engineering and Science 2022
主題:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
在線閱讀:	http://umpir.ump.edu.my/id/eprint/34915/1/An%20enhanced%20classification%20framework%20for%20intrusions%20detection%20system%20using%20intelligent%20exoplanet%20atmospheric%20retrieval%20algorithm.pdf http://umpir.ump.edu.my/id/eprint/34915/ https://doi.org/10.11591/eei.v11i2.3308 https://doi.org/10.11591/eei.v11i2.3308
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id	my.ump.umpir.34915
record_format	eprints
spelling	my.ump.umpir.349152022-11-03T03:11:54Z http://umpir.ump.edu.my/id/eprint/34915/ An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval Slamet, Slamet Izzeldin, Ibrahim Mohamed Abdelaziz T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Currently, many companies use data mining for various implementations. One form of implementation is intrusion detection system (IDS). In IDS, the main problem for nuisance network administrators in detecting attacks is false alerts. Regardless of the methods implemented by this system, eliminating false alerts is still a huge problem. To describe data traffic passing through the network, a database of the network security layer (NSL) knowledge discovery in database (KDD) dataset is used. The massive traffic of data sent over the network contains excessive and duplicated amounts of information. This causes the classifier to be biased, reduce classification accuracy, and increase false alert. To that end, we proposed a model that significantly improve the accuracy of the intrusion detection system by eliminating false alerts, whether they are false negative or false positive negative alerts. The results show that the proposed intelligent exoplanet atmospheric retrieval (INARA) algorithm has improved accuracy and is able to detect new attack types efficiently. Institute of Advanced Engineering and Science 2022-04 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/34915/1/An%20enhanced%20classification%20framework%20for%20intrusions%20detection%20system%20using%20intelligent%20exoplanet%20atmospheric%20retrieval%20algorithm.pdf Slamet, Slamet and Izzeldin, Ibrahim Mohamed Abdelaziz (2022) An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval. Bulletin of Electrical Engineering and Informatics, 11 (2). pp. 1018-1025. ISSN 2089-3191. (Published) https://doi.org/10.11591/eei.v11i2.3308 https://doi.org/10.11591/eei.v11i2.3308
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Slamet, Slamet Izzeldin, Ibrahim Mohamed Abdelaziz An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval
description	Currently, many companies use data mining for various implementations. One form of implementation is intrusion detection system (IDS). In IDS, the main problem for nuisance network administrators in detecting attacks is false alerts. Regardless of the methods implemented by this system, eliminating false alerts is still a huge problem. To describe data traffic passing through the network, a database of the network security layer (NSL) knowledge discovery in database (KDD) dataset is used. The massive traffic of data sent over the network contains excessive and duplicated amounts of information. This causes the classifier to be biased, reduce classification accuracy, and increase false alert. To that end, we proposed a model that significantly improve the accuracy of the intrusion detection system by eliminating false alerts, whether they are false negative or false positive negative alerts. The results show that the proposed intelligent exoplanet atmospheric retrieval (INARA) algorithm has improved accuracy and is able to detect new attack types efficiently.
format	Article
author	Slamet, Slamet Izzeldin, Ibrahim Mohamed Abdelaziz
author_facet	Slamet, Slamet Izzeldin, Ibrahim Mohamed Abdelaziz
author_sort	Slamet, Slamet
title	An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval
title_short	An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval
title_full	An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval
title_fullStr	An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval
title_full_unstemmed	An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval
title_sort	enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval
publisher	Institute of Advanced Engineering and Science
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/34915/1/An%20enhanced%20classification%20framework%20for%20intrusions%20detection%20system%20using%20intelligent%20exoplanet%20atmospheric%20retrieval%20algorithm.pdf http://umpir.ump.edu.my/id/eprint/34915/ https://doi.org/10.11591/eei.v11i2.3308 https://doi.org/10.11591/eei.v11i2.3308
_version_	1822922825246179328
score	13.251813

An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval

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