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2020 | OriginalPaper | Chapter

Taxonomy of Supervised Machine Learning for Intrusion Detection Systems

Authors : Ahmed Ahmim, Mohamed Amine Ferrag, Leandros Maglaras, Makhlouf Derdour, Helge Janicke, George Drivas

Published in: Strategic Innovative Marketing and Tourism

Publisher: Springer International Publishing

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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.

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previous chapter Residents’ Perceptions of Tourism Impact on Local Economic Development During the Economic Crisis: The Case of Kavala
next chapter A Detailed Analysis of Using Supervised Machine Learning for Intrusion Detection
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Metadata
Title
Taxonomy of Supervised Machine Learning for Intrusion Detection Systems
Authors
Ahmed Ahmim
Mohamed Amine Ferrag
Leandros Maglaras
Makhlouf Derdour
Helge Janicke
George Drivas
Copyright Year
2020
Book
Strategic Innovative Marketing and Tourism

Print ISBN: 978-3-030-36125-9

Electronic ISBN: 978-3-030-36126-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-36126-6_69