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Decision Tree Based Intrusion Detection System for NSL-KDD Dataset

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Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2 ( ICTIS 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 84))

Abstract

In this paper, Decision Tree (DT) based IDS is proposed for NSL-KDD dataset. The proposed work uses Correlation Feature Selection (CFS) subset evaluation method for feature selection. Feature selection improves the prediction performance of DT based IDS. Performance is evaluated before feature selection and after feature selection for five class classification (normal and types of attack) and binary class classification (normal and attack). The obtained result is compared and analyzed with the other reported techniques. The analysis shows that the proposed DT based IDS provides high DR and accuracy. The overall result for binary class classification for the dataset is higher than five class classification.

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Authors and Affiliations

  1. Department of Electrical Engineering, NIT Raipur, Raipur, India

    Bhupendra Ingre, Anamika Yadav & Atul Kumar Soni

Authors
  1. Bhupendra Ingre
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  2. Anamika Yadav
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  3. Atul Kumar Soni
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Corresponding author

Correspondence to Anamika Yadav .

Editor information

Editors and Affiliations

  1. Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Sabar Institute of Technology for Girls, Ahmedabad, Gujarat, India

    Amit Joshi

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Ingre, B., Yadav, A., Soni, A.K. (2018). Decision Tree Based Intrusion Detection System for NSL-KDD Dataset. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-319-63645-0_23

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  • DOI: https://doi.org/10.1007/978-3-319-63645-0_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63644-3

  • Online ISBN: 978-3-319-63645-0

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