Development of an Intelligent Classifier Model for Denial of Service Attack Detection

  1. Álvaro Michelena 1
  2. Jose Aveleira-Mata 2
  3. Esteban Jove 1
  4. Héctor Alaiz-Moretón 2
  5. Héctor Quintián 1
  6. José Luis Calvo-Rolle 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

  2. 2 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Aldizkaria:
IJIMAI

ISSN: 1989-1660

Argitalpen urtea: 2023

Alea: 8

Zenbakia: 3

Orrialdeak: 33-42

Mota: Artikulua

DOI: 10.9781/IJIMAI.2023.08.003 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Beste argitalpen batzuk: IJIMAI

Garapen Iraunkorreko Helburuak

Laburpena

The prevalence of Internet of Things (IoT) systems deployment is increasing across various domains, from residential to industrial settings. These systems are typically characterized by their modest computationa requirements and use of lightweight communication protocols, such as MQTT. However, the rising adoption of IoT technology has also led to the emergence of novel attacks, increasing the susceptibility of these systems to compromise. Among the different attacks that can affect the main IoT protocols are Denial of Service attacks (DoS). In this scenario, this paper evaluates the performance of six supervised classification techniques (Decision Trees, Multi-layer Perceptron, Random Forest, Support Vector Machine, Fisher Linear Discriminant and Bernoulli and Gaussian Naive Bayes) combined with the Principal Component Analysis (PCA) feature extraction method for detecting DoS attacks in MQTT networks. For this purpose, a real dataset containing all the traffic generated in the network and many attacks executed has been used. The results obtained with several models have achieved performances above 99% AUC.

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