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A Novel Algorithm for Feature Selection Used in Intrusion Detection

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Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 612))

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Abstract

Intrusion detection systems play an important role in securing computer networks. The existing methods for intrusion detection deal with huge amount of data which contains irrelevant or redundant features. Accordingly, feature selection is critical for improving classification accuracy in an intrusion detection system. In this paper, we proposed a novel algorithm combining a variety of feature selection methods based on majority voting rule, and used the SVM as the basic classification algorithm. Experiments on NSL-KDD dataset indicate that the proposed algorithm selects superior feature subset than the state-of-the-art feature selection approaches used in the field of intrusion detection.

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Notes

  1. 1.

    http://www.unb.ca/cic/research/datasets/nsl.html.

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Author information

Authors and Affiliations

  1. China Information Technology Security Evaluation Center, Beijing, China

    Yongle Hao & Longjie Li

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Yongle Hao

  3. Lanzhou University, Lanzhou, China

    Ying Hou & Longjie Li

Authors
  1. Yongle Hao
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  2. Ying Hou
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  3. Longjie Li
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Corresponding author

Correspondence to Yongle Hao .

Editor information

Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Rissho University, Tokyo, Japan

    Tomoya Enokido

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Hao, Y., Hou, Y., Li, L. (2018). A Novel Algorithm for Feature Selection Used in Intrusion Detection. In: Barolli, L., Enokido, T. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2017. Advances in Intelligent Systems and Computing, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-319-61542-4_98

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  • DOI: https://doi.org/10.1007/978-3-319-61542-4_98

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61541-7

  • Online ISBN: 978-3-319-61542-4

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