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Feature Reduction-Based DoS Attack Detection System

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Next Generation Information Processing System

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1162 ))

Abstract

Denial-of-service (DoS) has still been popularly used by attackers. It can be seen in China and the USA who are major victims of DoS attacks in recent years. For this reason, the development of an intelligent intrusion detection system (IDS) remains a challenging task. This study proposes a system for the detection of DoS attacks with feature reduction using a rule-based PART classifier. The reduced feature set is identified based on the combination of information gain and correlation attribute evaluation methods. The system is implemented and tested on CICIDS 2017 dataset. Finally, the proposed system provides an accuracy of 99.9871% for the detection of DoS attacks with 56 reduced features.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering & IT, College of Engineering Pune (COEP), Pune, India

    Jahed Momin Shaikh & Deepak Kshirsagar

Authors
  1. Jahed Momin Shaikh
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  2. Deepak Kshirsagar
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Corresponding author

Correspondence to Jahed Momin Shaikh .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra, India

    Prachi Deshpande

  2. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  3. Department of Electronics and Telecommunication Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra, India

    Brijesh Iyer

  4. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

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Momin Shaikh, J., Kshirsagar, D. (2021). Feature Reduction-Based DoS Attack Detection System . In: Deshpande, P., Abraham, A., Iyer, B., Ma, K. (eds) Next Generation Information Processing System. Advances in Intelligent Systems and Computing, vol 1162 . Springer, Singapore. https://doi.org/10.1007/978-981-15-4851-2_18

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