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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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/34915/ http://umpir.ump.edu.my/id/eprint/34915/1/An%20enhanced%20classification%20framework%20for%20intrusions%20detection%20system%20using%20intelligent%20exoplanet%20atmospheric%20retrieval%20algorithm.pdf |
| _version_ | 1848824636753575936 |
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| author | Slamet, Slamet Izzeldin, Ibrahim Mohamed Abdelaziz |
| author_facet | Slamet, Slamet Izzeldin, Ibrahim Mohamed Abdelaziz |
| author_sort | Slamet, Slamet |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:16:11Z |
| format | Article |
| id | ump-34915 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:16:11Z |
| publishDate | 2022 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | 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 |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/34915/ http://umpir.ump.edu.my/id/eprint/34915/ http://umpir.ump.edu.my/id/eprint/34915/ http://umpir.ump.edu.my/id/eprint/34915/1/An%20enhanced%20classification%20framework%20for%20intrusions%20detection%20system%20using%20intelligent%20exoplanet%20atmospheric%20retrieval%20algorithm.pdf |