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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CITATION STYLE
Ahmim, A., Ferrag, M. A., Maglaras, L., Derdour, M., Janicke, H., & Drivas, G. (2020). Taxonomy of Supervised Machine Learning for Intrusion Detection Systems. In Springer Proceedings in Business and Economics (pp. 619–628). Springer Science and Business Media B.V. https://doi.org/10.1007/978-3-030-36126-6_69
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