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A Hybrid Feature Selection Method for Improved Detection of Wired/Wireless Network Intrusions

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Abstract

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, both in wired and wireless environments. Intrusion detection system (IDS) has become a mandatory level of security for organizations to protect themselves from intruders. 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 method for feature selection that picks and combines the best features from different feature selection methods. This method can be applied for feature reduction in any application domain. In this work, the proposed hybrid method is employed for intrusion detection and six predominant features are picked from NSL-KDD dataset. An exhaustive performance investigation has proved that the proposed feature selection method increases the detection rate by 5% thereby improving the accuracy of intrusion detection system by 3%.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, Tamilnadu, India

    J. Rene Beulah & D. Shalini Punithavathani

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  1. J. Rene Beulah
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  2. D. Shalini Punithavathani
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Correspondence to J. Rene Beulah.

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Rene Beulah, J., Shalini Punithavathani, D. A Hybrid Feature Selection Method for Improved Detection of Wired/Wireless Network Intrusions. Wireless Pers Commun 98, 1853–1869 (2018). https://doi.org/10.1007/s11277-017-4949-x

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  • DOI: https://doi.org/10.1007/s11277-017-4949-x

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