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Pragmatic Analysis of Machine Learning Techniques in Network Based IDS

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Advanced Informatics for Computing Research (ICAICR 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1075))

Abstract

In providing defense to computer networks the network intrusion detection system (NIDS) plays a very essential role. To cope up with the demands of contemporary networks various concerns like performance evaluation and others related to the networks should be taken under consideration. Proposed work presents a pragmatic analysis of machine learning techniques for network based IDS. The performance analysis over two benchmark datasets i.e. KDD-Cup’99 and NSL-KDD by using five supervised machine learning techniques (RFC, Naïve bayes, J48, Bayes Net and SVM) has been prepared. To assess the performance network based intrusion detection system various metrics such as accuracy, recall, F1-score and precision has been computed and analyzed. Therefore, the summary of the work suggests that no single technique is smart enough to identify all attack classes to conventional levels. Most of the techniques provided poor results for minority attack class(es). To estimate and assess the supervised classifier a blind set of investigation with 10-fold cross validation has been performed. The results achieved are promising and provides a new direction to researchers of the intrusion detection domain.

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Authors and Affiliations

  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Divya Nehra, Krishan Kumar & Veenu Mangat

Authors
  1. Divya Nehra
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  2. Krishan Kumar
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  3. Veenu Mangat
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Corresponding author

Correspondence to Divya Nehra .

Editor information

Editors and Affiliations

  1. Papua New Guinea University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. Computer Science Department, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

  4. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  5. Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Canada

    Pawan Lingras

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Nehra, D., Kumar, K., Mangat, V. (2019). Pragmatic Analysis of Machine Learning Techniques in Network Based IDS. In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-15-0108-1_39

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  • DOI: https://doi.org/10.1007/978-981-15-0108-1_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0107-4

  • Online ISBN: 978-981-15-0108-1

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