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Detection of Cyberattacks in IoT Networks Using Artificial Intelligence: A Comparative Study

FIGUEIREDO, MATHEUS; SODRE, DAR'C PABLA; MEDEIROS, RENAN; Lucena, Vicente F.; BESSA, IURY

2024 • 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)

Resumo

Resumo

The use of Internet of Things (IoT) technologies has become more readily available with the advent of cyber-physical systems. This context motivates concerns about cybersecurity and the occurrence of malicious attacks in IoT networks. This paper investigates the problem of the automatic detection of cyberattacks in MQTT-based IoT networks by deploying artificial intelligence algorithms for processing traffic data and indicating whether an attack is occurring or not. Thus, this paper trains different artificial intelligence binary classifiers based on machine learning and compares their performance for malicious attack detection in cyber-physical systems with MQTT-based IoT networks. For training and testing the classifiers, we employ the MQTTset dataset which contains many labelled samples with both legitimate traffic and observations under attack occurrence. By analyzing data features and preprocessing, the algorithms achieved good performance in classifying network traffic, contributing to the security of cyber-physical systems.

Autoria

Autores do grupo (3)

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palavras-chave

Palavras-chave

cyberattack detectionInternet of Things securityMQTTnetwork traffic classificationbinary classifierscyber-physical systemsmalicious traffic detection

Status

Published

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Trabalho em anais de evento