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)
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Abstract
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Classification; False alert; INARA; Intrusion |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering Institute of Postgraduate Studies College of Engineering Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 03 Nov 2022 03:11 |
| Last Modified: | 03 Nov 2022 03:11 |
| URI: | http://umpir.ump.edu.my/id/eprint/34915 |
| Download Statistic: | View Download Statistics |
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