IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-030-36126-6_69.html
   My bibliography  Save this book chapter

Taxonomy of Supervised Machine Learning for Intrusion Detection Systems

In: Strategic Innovative Marketing and Tourism

Author

Listed:
  • Ahmed Ahmim

    (University of Larbi Tebessi)

  • Mohamed Amine Ferrag

    (Guelma University)

  • Leandros Maglaras

    (De Montfort University
    Ministry of Digital Policy, Telecommunications and Media)

  • Makhlouf Derdour

    (University of Larbi Tebessi)

  • Helge Janicke

    (De Montfort University)

  • George Drivas

    (Ministry of Digital Policy, Telecommunications and Media
    University of Piraeus)

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.

Suggested Citation

  • Ahmed Ahmim & Mohamed Amine Ferrag & Leandros Maglaras & Makhlouf Derdour & Helge Janicke & George Drivas, 2020. "Taxonomy of Supervised Machine Learning for Intrusion Detection Systems," Springer Proceedings in Business and Economics, in: Androniki Kavoura & Efstathios Kefallonitis & Prokopios Theodoridis (ed.), Strategic Innovative Marketing and Tourism, pages 619-628, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-36126-6_69
    DOI: 10.1007/978-3-030-36126-6_69
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Oyeniyi Akeem Alimi & Khmaies Ouahada & Adnan M. Abu-Mahfouz & Suvendi Rimer & Kuburat Oyeranti Adefemi Alimi, 2021. "A Review of Research Works on Supervised Learning Algorithms for SCADA Intrusion Detection and Classification," Sustainability, MDPI, vol. 13(17), pages 1-19, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:prbchp:978-3-030-36126-6_69. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.