An ensemble feature selection approach using hybrid kernel based SVM for network intrusion detection system
Feature selection is a process of identifying relevant feature subset that leads to the machine learning algorithm in a well-defined manner. In this paper, anovel ensemble feature selection approach that comprises of Relief Attribute Evaluation and hybrid kernel-based support vector machine (HK-SVM...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-07-01.
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| LEADER | 02218 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs23294_15224 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Gopal, Gaddam Venu |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Babu, Gatram Rama Mohan |e author |
| 245 | 0 | 0 | |a An ensemble feature selection approach using hybrid kernel based SVM for network intrusion detection system |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-07-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23294 | ||
| 520 | |a Feature selection is a process of identifying relevant feature subset that leads to the machine learning algorithm in a well-defined manner. In this paper, anovel ensemble feature selection approach that comprises of Relief Attribute Evaluation and hybrid kernel-based support vector machine (HK-SVM) approach is proposed as a feature selection method for network intrusion detection system (NIDS). A Hybrid approach along with the combination of Gaussian and Polynomial methods is used as a kernel for support vector machine (SVM). The key issue is to select a feature subset that yields good accuracy at a minimal computational cost. The proposed approach is implemented and compared with classical SVM and simple kernel. Kyoto2006+, a bench mark intrusion detection dataset,is used for experimental evaluation and then observations are drawn. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science and Engineering and Machine Learning | ||
| 690 | |a Feature selection; Hybrid kernel; Support vector machine; Kyoto 2006+; Intrusion detection system | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 23, No 1: July 2021; 558-565 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v23.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23294/15224 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23294/15224 |z Get fulltext |