  The rapid development of the Internet of Things (IoT) environment has
introduced unprecedented levels of connectivity and automation. The Message
Queuing Telemetry Transport (MQTT) protocol has become recognized in IoT
applications due to its lightweight and efficient features; however, this
simplicity also renders MQTT vulnerable to multiple attacks that can be
launched against the protocol, including denial of service (DoS) and
brute-force attacks. This study aims to improve the detection of intrusion DoS
and brute-force attacks in an MQTT traffic intrusion detection system (IDS).
Our approach utilizes the MQTT dataset for model training by employing
effective feature engineering and ensemble learning techniques. Following our
analysis and comparison, we identified the top 10 features demonstrating the
highest effectiveness, leading to improved model accuracy. We used supervised
machine learning models, including Random Forest, Decision Trees, k-Nearest
Neighbors, and XGBoost, in combination with ensemble classifiers. Stacking,
voting, and bagging ensembles utilize these four supervised machine-learning
methods to combine models. This study's results illustrate the proposed
technique's efficacy in enhancing the accuracy of detecting DoS and brute-force
attacks in MQTT traffic. Stacking and voting classifiers achieved the highest
accuracy of 0.9538. Our approach outperforms the most recent study that
utilized the same dataset.
