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

Sahar Yousif Mohammed (), Mohammed Aljanabi () and Maad M. Mijwil ()
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Sahar Yousif Mohammed: Anbar University
Mohammed Aljanabi: Imam Ja’afar Al-Sadiq University
Maad M. Mijwil: Baghdad College of Economic Sciences University

A chapter in Sustainability and Financial Services in the Digital Age, 2024, pp 119-127 from Springer

Abstract: 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.

Keywords: Denial-of-service (DoS); Machine learning; TinyML; Microcontroller; Edge computing (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-67511-9_8

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DOI: 10.1007/978-3-031-67511-9_8

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