Development of an Intelligent Classifier Model for Denial of Service Attack Detection
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Development of an Intelligent Classifier Model for Denial of Service Attack DetectionAuthor(s)
Date
2023Citation
Álvaro Michelena, Jose Aveleira-Mata, Esteban Jove, Héctor Alaiz-Moretón, Héctor Quintián, José Luis Calvo-Rolle (2023). "Development of an Intelligent Classifier Model for Denial of Service Attack Detection", International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, issue Special Issue on Practical Applications of Agents and Multi-Agent Systems, no. 3, pp. 33-42. https://doi.org/10.9781/ijimai.2023.08.003
Abstract
[Abstract] 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.
Keywords
Cybersecurity
Feature extraction
MQTT
Soft computing
Supervised classifiers.
DoS attacks
Feature extraction
MQTT
Soft computing
Supervised classifiers.
DoS attacks
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Rights
Atribución 3.0 España
ISSN
1989 - 1660