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The Zigbee protocol, designed for low-power personal area wireless networks, is a technology widely used on the Internet of Things. This paper presents a study on the detection of denial-of-service attacks in Zigbee networks using supervised classification algorithms. Three techniques are evaluated: Logistic Regression, K-Nearest Neighbors and Support Vector Machines. A generated dataset is used for the analysis, and the results show that the K-Nearest Neighbors and Support Vector Machines approach achieves high performance and low computational demand. This methodology offers a promising strategy for security in Zigbee networks.

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DoS Attack Detection and Identification over Zigbee Environments Using Supervised Classification Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • Lawrence Nforh,
  • Álvaro Michelena,
  • Jose Aveleira-Mata,
  • María Teresa García-Ordás,
  • Francisco Zayas-Gato,
  • Esteban Jove,
  • Héctor Alaiz-Moretón

摘要

The Zigbee protocol, designed for low-power personal area wireless networks, is a technology widely used on the Internet of Things. This paper presents a study on the detection of denial-of-service attacks in Zigbee networks using supervised classification algorithms. Three techniques are evaluated: Logistic Regression, K-Nearest Neighbors and Support Vector Machines. A generated dataset is used for the analysis, and the results show that the K-Nearest Neighbors and Support Vector Machines approach achieves high performance and low computational demand. This methodology offers a promising strategy for security in Zigbee networks.