# An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks
## 4 Experimental Results and Discussions
### Experimental Results Based on the Proposed Model  
The experimental findings for per-device datasets are presented in this section. The 70-30 train-test data split ratio was applied to all five ensemble models used in the study. The final result was calculated and displayed as the mean value of all evaluation methods. Table 4 presents the mean values of the accuracy, F1-score, precision, and recall metrics for the proposed ensemble models applied to the IIoT_Fridge sensor device dataset. For this dataset, the XGBoost classifier outperforms all other models, with 0.9873 for accuracy, 0.9801 for precision, 0.9942 for recall, and 0.9869 for F1_Score, respectively. Conversely,  
the worst classifier is the AdaBoost 'Ada', with 0.4909 for accuracy, 0.1299 for precision, 0.2589 for recall, and 0.1728 for F1-Score, respectively. Table 5 demonstrates the mean values of the accuracy, F1-score, precision, and recall metrics for the proposed ML-based ensemble models applied to the IIoT_ Thermostat sensor device dataset. For this dataset, the XGBoost classifier outperforms all other models, with 0.9883 for accuracy, 0.9882 for precision, 0.9950 for recall, and 0.9915 for F1_Score, respectively. Conversely, the worst classifier is the Ada, with 0.5305 for accuracy, 0.3557 for precision, 0.3130 for recall, and 0.2837 for F1-Score, respectively. Table 6 presents the mean values of the accuracy, F1-score, precision, and recall metrics for the candidate ML models applied to the IIoT_GPS_Tracker sensor device dataset. For this dataset, the XGBoost classifier outperforms all other models, with 0.9869 accuracies, 0.9780 precision, 0.9895 recall, and 0.9836 of F1_Score metrics. Conversely, the Ada classifier is the worst of all the models, with 0.4738 accuracy, 0.0592 precision, 0.1250 recall, and 0.0804 of F1_Score, respectively. Table 7 illustrates the mean values of the performance metrics evaluation for the ML-based ensemble models applied to the IIoT_Modbus sensor device dataset. For this dataset, the XGBoost classifier still performs excellently when compared with other classifiers, with an accuracy of 0.9913, precision of 0.9864, recall of 0.9895, and F1_Score of 0.9879, respectively, for all the performance metrics used in the study. The worst classifier is the Ada, with an accuracy of 0.6292, precision of 0.1049, recall of 0.1667, and F1_Score of 0.1287, respectively. Table 8 presents the mean values of the performance metrics evaluation for the ML-based ensemble models applied to the Motion_Light device dataset. For this dataset, the XGBoost classifier still performs excellently when compared with other classifiers, with an accuracy of 0.9719, precision of 0.9531, recall of 0.8957, and F1_Score of 0.9030, respectively. The worst classifier is the Ada, with an accuracy of 0.4695, precision of 0.1144, recall of 0.2241, and F1_Score of 0.1515. Table 9 shows the results of the performance evaluation metrics in terms of mean values for the ML-based ensemble models applied to the IIoT Garage_Door sensor device dataset. For this dataset, the XGBoost classifier outperforms all other models, with an accuracy of 0.9846, precision of 0.9796, recall of 0.9902, and F1_Score of 0.9847, respectively. The worst classifier is the Ada, with an accuracy of 0.4715, precision of 0.0589, recall of 0.1250, and F1_Score of 0.0801, respectively. Table 10 presents the mean values of the accuracy, F1-score, precision, and recall assessor for the candidate ML models applied to the Weather device dataset. The XGBoost classifier outperforms all other models for this dataset, with an accuracy of 0.9878, precision of 0.9788, recall of 0.9896, and F1_Score of 0.9840, respectively. The Ada classifier is the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Models** & **Accuracy** & **Precision** & **Recall** & **F1\_Score** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Ada** \\ **Model**worst among all the classifiers, with an accuracy of 0.5311, precision of 0.0664, recall of 0.1250, and F1_Score of 0.0867 metrics, respectively.