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Smart homes are becoming more and more popular in the world, and they are mainly on Internet of Things (IoT) technologies to enable their functionality. However, since IoT devices have limited computing power and resources, implementing strong security measures is difficult, which makes the use of intrusion detection systems (IDS) an appropriate option. In this paper, we propose an optimized model with high performance for intrusion detection in Message Queue Telemetry Transport protocol (MQTT)-based IoT networks for smart homes. This is done by studying a total of 22 Machine Learning (ML) algorithms based on an extended two-stage evaluation approach that includes several aspects for optimizing and validating performance for finding the ideal model. Based on the empirical evaluation, the GLM classifier with the random over-sampling technique produces the best detection performance with 100% accuracy and an f-score of 100%, outperforming the previous studies. This paper also investigates the influence of automatic feature engineering technique on the performance of algorithms. With the automatic feature engineering technique, the performance increased by up to 38.9%, and the time consumed to classify attacks decreased by up to 67.7%. This shows that automatic feature engineering can improve performance and reduce detection time.

Protecting Smart-Home IoT Devices from MQTT Attacks: An Empirical Study of ML-Based IDS