Taxonomy of supervised machine learning for intrusion detection systems

Authors/Creators

Ahmed Ahmim
Mohamed A. Ferrag
Leandros Maglaras
Makhlouf Derdour
Helge Janicke, Edith Cowan UniversityFollow
George Drivas

Abstract

This paper presents a taxonomy of supervised machine learning techniques for intrusion detection systems (IDSs). Firstly, detailed information about related studies is provided. Secondly, a brief review of public data sets is provided, which are used in experiments and frequently cited in publications, including, IDEVAL, KDD CUP 1999, UNM Send-Mail Data, NSL-KDD, and CICIDS2017. Thirdly, IDSs based on supervised machine learning are presented. Finally, analysis and comparison of each IDS along with their pros and cons are provided.

Document Type

Conference Proceeding

Date of Publication

2020

Publication Title

Strategic Innovative Marketing and Tourism

Publisher

Springer

School

School of Science

RAS ID

44686

Comments

Ahmim, A., Ferrag, M. A., Maglaras, L., Derdour, M., Janicke, H., & Drivas, G. (2020). Taxonomy of supervised machine learning for intrusion detection systems. In Strategic Innovative Marketing and Tourism (pp. 619-628). Springer, Cham. https://doi.org/10.1007/978-3-030-36126-6_69

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