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Introducing a Classification Model Based on SVM for Network Intrusion Detection

  • Ghodratolah Dastfal
  • Samad NejatianEmail author
  • Hamid Parvin
  • Vahideh Rezaie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10632)

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.

Keywords

Intrusion detection Support vector machine Data size reduction Feature selection IDS 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ghodratolah Dastfal
    • 1
  • Samad Nejatian
    • 2
    • 3
    Email author
  • Hamid Parvin
    • 1
    • 4
  • Vahideh Rezaie
    • 3
    • 5
  1. 1.Department of Computer Engineering, Nourabad Mamasani BranchIslamic Azad UniversityNourabad, MamasaniIran
  2. 2.Department of Electrical Engineering, Yasooj BranchIslamic Azad UniversityYasoojIran
  3. 3.Young Researchers and Elite Club, Yasooj BranchIslamic Azad UniversityYasoojIran
  4. 4.Young Researchers and Elite Club, Nourabad Mamasani BranchIslamic Azad UniversityNourabad, MamasaniIran
  5. 5.Department of Mathematic, Yasooj BranchIslamic Azad UniversityYasoojIran

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