Denial-of Service (DoS) Attack Detection Using Edge Machine Learning Conference

Huynh, NS, De La Cruz, S, Perez-Pons, A. (2023). Denial-of Service (DoS) Attack Detection Using Edge Machine Learning . 1741-1745. 10.1109/ICMLA58977.2023.00264

cited authors

  • Huynh, NS; De La Cruz, S; Perez-Pons, A

authors

abstract

  • Developing lightweight algorithms to implement DoS attack mitigation on edge devices is a growing interest in edge cybersecurity. Various types of micro-controller boards can be programmed to capture network traffic and implement lightweight machine learning models to analyze the supplied traffic data for signs of intrusion and attacks. This study experimented with building Support Vector Machine and Logistic Regression models on real-time DoS attack scenario data and the CICIoT2023 dataset. The main contribution of this study is to propose a framework for data capturing, processing, and analysis to produce edge machine learning models for DoS attack mitigation

publication date

  • January 1, 2023

Digital Object Identifier (DOI)

start page

  • 1741

end page

  • 1745