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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| Main Authors: | , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2021-07-01.
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| Subjects: | |
| Online Access: | Get fulltext |
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| Summary: | 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. |
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| Item Description: | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23294 |