Comparative study for feature selection algorithms in intrusion detection system
- 作者: Anusha K.1, Sathiyamoorthy E.1
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隶属关系:
- School of Information Technology and Engineering
- 期: 卷 50, 编号 1 (2016)
- 页面: 1-9
- 栏目: Article
- URL: https://journals.rcsi.science/0146-4116/article/view/174331
- DOI: https://doi.org/10.3103/S0146411616010028
- ID: 174331
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详细
The Intrusion Detection System (IDS) deals with the huge amount of network data that includes redundant and irrelevant features causing slow training and testing procedure, higher resource usage and poor detection ratio. Feature selection is a vital preprocessing step in intrusion detection. Hence, feature selec-tion is an essential issue in intrusion detection and need to be addressed by selec-ting the appropriate feature selection algorithm. A major challenge to select the optimal feature selection methods can precisely calculate the relevance of fea-tures to the detection process and the redundancy among features. In this paper, we study the concepts and algorithms used for feature selection algorithms in the IDS. We conclude this paper by identifying the best feature selection algorithm to select the important and useful features from the network dataset.
作者简介
K. Anusha
School of Information Technology and Engineering
编辑信件的主要联系方式.
Email: knu.anusha@gmail.com
印度, Vellore, Tamil Nadu, 632014
E. Sathiyamoorthy
School of Information Technology and Engineering
Email: knu.anusha@gmail.com
印度, Vellore, Tamil Nadu, 632014
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