# An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks
## 5 Conclusions  
In order to protect the IIoT environment from outside attackers and intruders, several IDSs techniques, linked with IIoT-based network traffic, have been proposed and have emerged as essential parts of the technology for proper protection from outsiders. When used, in conjunction with ML-based classifiers, big data as a potent tool for studying massive amounts of data to safeguard IIoT equipment. The technologies have shown to be beneficial for IioT-based systems security measures. Industrial Automation and Control Systems and conventional IT systems are fundamentally different in how they counter  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**References** & **Model** & **Fridge** & **Garage\_Door** & **GPS\_Tracker** & **Modbus** & **Motion\_Light** & **Thermostat** & **Weather** \\ \hline \multirow{4}{*}{[61]} & DT, RF, Ada, & & & & & & & \\  & XGBoost, & & & & & & & \\  & ANN, MLP & & & & & & & \\  & VC, RF, ANN, & & & & & & & \\  & ID CNN & & & & & & & \\  & CB, RF, Gboost, & & & & & & \\  & HBboost & & & & & & & \\  & LR, LDA, & & & & & & & \\  & k-NN, RF, & & & & & & & \\  & CART, NB, & & & & & & & \\  & SVM, LSTM & & & & & & & \\  & XGBoost, Bag, & & & & & & & \\  & RF, ET, Ada & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 13: Accuracy comparison of existing results based on ToN-IoT datasets.  
cyberattacks, yet these differences are distinct. Security for the IioT must, therefore, be given specific consideration. Therefore, this study attempts to build an efficient multiclass IDS system based on ML-based ensemble models: XGBoost, Bagging, Random Forest, Extra Trees, and AdaBoost, based on seven (7) paremeters, were used for the Telemetry dataset of ToN_IoT datasets. An empirical experiment is performed on the dataset. The outcome, based on a comparative study, indicates that the proposed model performs excellently, and XGBoost performed superior to other models. The outcomes from the analysis showed that the proposed system could effectively and accurately classify different attacks. One of the major limitations of the proposed model is the inability to deal with the class imbalance that arises from the datasets used to test the performance of the proposed model. Therefore, future work will make use of imbalanced algorithms to balance the dataset. This will enable us to know if the imbalance will affect the performance of the proposed model. Future work will further focus on applying deep learning models to optimize their hyper-parameters to improve the dataset classification performance for the IDS. The proposed model will be applied to other IIoT-based datasets.  
The manuscript was written through the contributions of all authors. Conceptualization,J.B.A., S.O.F. and J.O.O.; methodology, J.B.A., S.O.F. and J.O.O.; software, J.O.O. and J.B.A.; validation, A.L.I, D.-T.D. C.-C.L. and C.-T.L.; formal analysis, A.L.I.; investigation, J.B.A.; resources, C.-C.L., C.-T.L. and D.-T.D.; data curation, J.B.A., S.O.F., A.L.I. and J.O.O.; writing--original draft preparation, J.B.A.; writing--review and editing, A.L.I., C.-C.L., C.-T.L. and D.-T.D.; visualization, J.B.A., A.L.I, S.O.F. and J.O.O.; supervision, J.B.A.; project administration, J.B.A., S.O.F., A.L.I., C.-C.L., C.-T.L. and D.-T.D.; funding acquisition, J.B.A. and A.L.I. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] is supported in part by the Nigerian Petroleum Technology Development Fund (PTDF) and, in part, by the German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program under grant 57473408.  
Not applicable.  
The data related to the outcome of this study are available upon reasonable request from the first author.  
The authors declare no conflict of interest.