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Detecting Denial-of-Service (DoS) Attacks with Edge Machine Learning

In: Sustainability and Financial Services in the Digital Age

Author

Listed:
  • Sahar Yousif Mohammed

    (Anbar University)

  • Mohammed Aljanabi

    (Imam Ja’afar Al-Sadiq University
    Al-Iraqia University)

  • Maad M. Mijwil

    (Baghdad College of Economic Sciences University)

Abstract

There has been a growing interest in developing lightweight algorithms for implementing DoS attack mitigation on edge devices due to the increasing focus on edge cybersecurity. Several micro-controller boards are available for capturing network traffic and implementing lightweight machine learning models. These models can then analyze incoming data to identify signs of intrusion and potential attacks. The study involved conducting experiments with support vector machine and logistic regression models using real-time DoS attack scenario data and the CICIoT2023 dataset. This research presents a framework for capturing, processing, and analyzing data to generate edge machine learning models that can effectively mitigate DoS attacks.

Suggested Citation

  • Sahar Yousif Mohammed & Mohammed Aljanabi & Maad M. Mijwil, 2024. "Detecting Denial-of-Service (DoS) Attacks with Edge Machine Learning," Springer Proceedings in Business and Economics, in: Nadia Mansour & Lorenzo M. Bujosa Vadell (ed.), Sustainability and Financial Services in the Digital Age, pages 119-127, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-67511-9_8
    DOI: 10.1007/978-3-031-67511-9_8
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