Thursday, August 20, 2020

International Journal of Network Security & Its Applications (IJNSA)

ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)

http://airccse.org/journal/ijnsa.html

A PROPOSED MODEL FOR DIMENSIONALITY REDUCTION TO IMPROVE THE CLASSIFICATION CAPABILITY OF INTRUSION PROTECTION SYSTEMS

Hajar Elkassabi, Mohammed Ashour and Fayez Zaki
Department of Electronics & Communication Faculty of Engineering, Mansoura University, Mansoura, Egypt

ABSTRACT
Over the past few years, intrusion protection systems have drawn a mature research area in the field of computer networks. The problem of excessive features has a significant impact on intrusion detection performance. The use of machine learning algorithms in many previous researches has been used to identify network traffic, harmful or normal. Therefore, to obtain the accuracy, we must reduce the dimensionality of the data used. A new model design based on a combination of feature selection and machine learning algorithms is proposed in this paper. This model depends on selected genes from every feature to increase the accuracy of intrusion detection systems. We selected from features content only ones which impact in attack detection. The performance has been evaluated based on a comparison of several known algorithms. The NSL-KDD dataset is used for examining classification. The proposed model outperformed the other learning approaches with accuracy 98.8 %.

Original Source URL: https://aircconline.com/ijnsa/V12N4/12420ijnsa02.pdf

Volume Link: http://airccse.org/journal/jnsa20_current.html



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International Journal of Network Security & Its Applications (IJNSA) - ERA, WJCI Indexed

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