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Analysis of Feature Selection Techniques to Detect DoS Attacks Using Rule-Based Classifiers

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Applied Information Processing Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1354))

Abstract

Denial of Service (DoS) attacks are emerging as a security threat, which, when ignored, may result in enormous losses for the organizations. Such attacks lead to the unavailability of the services provided by the organizations to legitimate users. The detection of such attacks with lower computation and minimization of errors is an ongoing research area. This paper focuses on analyzing different feature selection methods for feature selection in the detection of DoS attacks. The analysis of feature selection methods provides relevant and noisy feature subsets based on the score obtained by each method. The obtained relevant feature subset is tested on the CICIDS-2017 DoS dataset and achieves higher accuracy of 99.9591% with the PART classifier.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering and IT, College of Engineering Pune, Pune, Maharashtra, India

    Atharva Vaidya & Deepak Kshirsagar

Authors
  1. Atharva Vaidya
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  2. Deepak Kshirsagar
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Corresponding author

Correspondence to Atharva Vaidya .

Editor information

Editors and Affiliations

  1. Department of Electronics and Telecommunications Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, India

    Brijesh Iyer

  2. Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Debashis Ghosh

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Vaidya, A., Kshirsagar, D. (2022). Analysis of Feature Selection Techniques to Detect DoS Attacks Using Rule-Based Classifiers. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9_30

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