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Asian Journal of Research in Social Sciences and Humanities
Year : 2017, Volume : 7, Issue : 3
First page : ( 909) Last page : ( 921)
Online ISSN : 2249-7315.
Article DOI : 10.5958/2249-7315.2017.00216.7

A Hybrid Feature Selection Framework for Enhancing Network Intrusion Detection

Beulah J. Rene*, Punithavathani D. Shalini**

*Department of Computer Science & Engineering, Government College of Engineering, Tirunelveli, Tamilnadu, India. renebeulah@gmail.com

**Department of Computer Science & Engineering, Government College of Engineering, Tirunelveli, Tamilnadu, India

Online published on 23 March, 2017.


Internet has become an essential aspect of communication in the day to day life of everyone around the world. With the increased usage of Internet, attacks have also increased and the need for various levels of security is on the rise. Intrusion Detection System (IDS) has become a mandatory level of security for organizations. Improving the accuracy of IDS is crucial and it is the present focus of researchers. Feature selection has its role in enhancing accuracy by extracting the most relevant features. This study proposes a hybrid framework for feature selection that picks and combines the best features from different feature selection methods. This framework can be applied for feature reduction in any application domain. In this work, the proposed hybrid framework is employed for intrusion detection and 6 predominant features are picked from NSL-KDD dataset. An exhaustive performance investigation has proved that the features thus chosen improve the detection rate and accuracy of the intrusion detection system.



Intrusion Detection, Attribute Selection, WEKA, NSL-KDD Dataset, Performance Analysis, IHFS.


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