Taxonomy of Supervised Machine Learning for Intrusion Detection Systems

Ahmed Ahmim, Mohamed Amine Ferrag, Leandros Maglaras (), Makhlouf Derdour, Helge Janicke and George Drivas
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Ahmed Ahmim: University of Larbi Tebessi
Mohamed Amine Ferrag: Guelma University
Leandros Maglaras: De Montfort University
Makhlouf Derdour: University of Larbi Tebessi
Helge Janicke: De Montfort University
George Drivas: Ministry of Digital Policy, Telecommunications and Media

A chapter in Strategic Innovative Marketing and Tourism, 2020, pp 619-628 from Springer

Abstract: 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.

Keywords: Machine learning; Intrusion detection; Cyber analytics (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-36126-6_69

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