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Introducing a Classification Model Based on SVM for Network Intrusion Detection

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Advances in Soft Computing (MICAI 2017)

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Abstract

Intrusion Detection Systems are designed to provide security into computer networks. In this article, we used rough sets theory for feature selection to enhance support vector machine in intrusion detection. Testing and evaluation of the proposed method has been performed mainly on NSL-KDD data sets as a corrected version of KDD-CUP99. Experimental results indicate that the proposed method shows a good performance in providing high precision, intrusion detection readout, less error notification rate and more detailed detection compared to its basic and simpler methods.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad, Mamasani, Iran

    Ghodratolah Dastfal & Hamid Parvin

  2. Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Samad Nejatian

  3. Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Samad Nejatian & Vahideh Rezaie

  4. Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad, Mamasani, Iran

    Hamid Parvin

  5. Department of Mathematic, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Vahideh Rezaie

Authors
  1. Ghodratolah Dastfal
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  2. Samad Nejatian
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  3. Hamid Parvin
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  4. Vahideh Rezaie
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Corresponding author

Correspondence to Samad Nejatian .

Editor information

Editors and Affiliations

  1. Universidad Autónoma del Estado de Hidalgo, Pachuca, Mexico

    Félix Castro

  2. INFOTEC Aguascalientes, Aguascalientes, Mexico

    Sabino Miranda-Jiménez

  3. Tecnológico de Monterrey, Atizapán de Zaragoza, Mexico

    Miguel González-Mendoza

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Dastfal, G., Nejatian, S., Parvin, H., Rezaie, V. (2018). Introducing a Classification Model Based on SVM for Network Intrusion Detection. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Soft Computing. MICAI 2017. Lecture Notes in Computer Science(), vol 10632. Springer, Cham. https://doi.org/10.1007/978-3-030-02837-4_5

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  • DOI: https://doi.org/10.1007/978-3-030-02837-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02836-7

  • Online ISBN: 978-3-030-02837-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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