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A Detailed Analysis of Using Supervised Machine Learning for Intrusion Detection

In: Strategic Innovative Marketing and Tourism

Author

Listed:
  • 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)

Abstract

Machine learning is more and more used in various fields of the industry, which go from the self driving car to the computer security. Nowadays, with the huge network traffic, machine learning represents the miracle solution to deal with network traffic analysis and intrusion detection problems. Intrusion Detection Systems can be used as a part of a holistic security framework in different critical sectors like oil and gas industry, traffic management, water sewage, transportation, tourism and digital infrastructure. In this paper, we provide a comparative study between twelve supervised machine learning methods. This comparative study aims to exhibit the best machine learning methods relative to the classification of network traffic in specific type of attack or benign traffic, category of attack or benign traffic and attack or benign. CICIDS’2017 is used as data-set to perform our experiments, with Random Forest, Jrip, J48 showing better performance.

Suggested Citation

  • Ahmed Ahmim & Mohamed Amine Ferrag & Leandros Maglaras & Makhlouf Derdour & Helge Janicke, 2020. "A Detailed Analysis of Using Supervised Machine Learning for Intrusion Detection," Springer Proceedings in Business and Economics, in: Androniki Kavoura & Efstathios Kefallonitis & Prokopios Theodoridis (ed.), Strategic Innovative Marketing and Tourism, pages 629-639, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-36126-6_70
    DOI: 10.1007/978-3-030-36126-6_70
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    Cited by:

    1. Ranjit Panigrahi & Samarjeet Borah & Akash Kumar Bhoi & Muhammad Fazal Ijaz & Moumita Pramanik & Rutvij H. Jhaveri & Chiranji Lal Chowdhary, 2021. "Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research," Mathematics, MDPI, vol. 9(6), pages 1-32, March.

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