Securing MQTT protocol for IoT environment using IDS based on ensemble learning

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments

“…Table 10 shows the results of papers [ 22 , 25 ] and compares our proposed method using the following evaluation metrics: accuracy, precision, recall, F1-score, ROC, and performance time for each chosen approach.…”

“…As shown in Table 10 , paper [ 22 ] uses 33 features, and paper [ 25 ] uses 31; our model uses 10 features. This indicates that our scheme focused on the top 10 features for intrusion detection, maintained high classification accuracy of the model, and has superior performance in all evaluation metrics.…”

“…The main observation is that our approach significantly reduced training and testing times, while [ 22 , 25 ] have longer times; therefore, our method is better suited for applications that require real-time processing. It is also better at optimizing resources, improving efficiency, responding faster to preventing or underestimating the impact of attacks, and for scalability of intrusion detection models.…”

“…These elements make our approach a robust and effective solution for intrusion detection in the MQTT IoT traffic network. Paper [ 25 ] achieved better results in accuracy than paper [ 22 ] using all the ML methods. The authors did not use hyperparameter tuning in paper [ 22 ], while this was not reported for some MLs in [ 25 ].…”

“…Zeghida et al [ 25 ] proposed ensemble learning for an intrusion detection model based on the MQTT protocol to enhance the IoT security environment. They discussed utilizing multiple ML methods to detect intrusion in the MQTT IoT structure.…”