# XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection
## 2 Materials and Methods
### Classifiers
#### 2.4.2 eXtreme Gradient Boosting (XGBoost)
Note that ROCs for RF-RF, RF-RFECV, RF-RFE, RF-SelectKBest, and RF-WFS are overlapped. ## 4 Conclusions

The future IoT will have a profound effect on our economic and social life. Therefore, it is urgent to keep them secure. Intrusion detection systems are highly effective in identifying possible security risks and breaches. The proposed XGB-RF based hybrid machine learning scheme successfully detects different types of intrusions. Five different schemes were investigated in this proposed work. Among these, it has been shown that the accuracy, sensitivity, Kappa index of XGB-RF is almost 10% higher than other schemes. The effectiveness was tested to N-BaIoT dataset with more than 99% accuracy, which is also higher than different state-of-the-art machine learning schemes. Since the security and confidentiality of IoT devices are crucial for their success, this proposed approach can contribute a lot to enhancing the security aspects of IoT systems. However, due to the continual growth of new types of attacks, 383,379 identifying unknown attacks has proven to be a difficult task. Currently, our proposed approach takes 0.0010063 s for detecting a single attack. The near-future work could be reducing the detection time while maintaining higher accuracy so that it can be implemented in a busy IoT system. In addition, we will analyze the performance of machine learning classifiers to detect unknown attacks in IoT environments. Conceptualization, M.A.A. ; methodology, J.A.F., S.T.M., J.S.T., A.S., M.M.A. In addition, M.A.A. ; software, K.M.M. In addition, M.A.A. ; validation, M.A.A. In addition, S.S.M. ; formal analysis, K.M.M. In addition, M.A.A. ; investigation, M.A.A. In addition, S.S.M. ; resources, S.T.M. In addition, J.S.T. ; data curation, S.T.M., J.S.T. In addition, K.M.M. ; writing--original draft preparation, J.A.F., S.T.M., J.S.T., A.S. In addition, M.A.A. ; writing--review and editing, M.A.A. In addition, S.S.M. ; visualization, M.A.A. In addition, S.S.M. ; supervision, M.A.A. ; project administration, M.A.A. ; funding acquisition, S.S.M. All authors have read and agreed to the published version of the manuscript. The funding information is addressed at the Acknowledgment section. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. **Acknowledgments:** SSM acknowledges M1420-09-5369-FSE-000002--Post-Doctoral Fellowship, co-financed by the Madeira 14-20 Pro-gram--European Social Fund. **Conflicts of Interest:** The authors declare that they have no conflict of interest. ## Abbreviations

The following abbreviations are used in this manuscript: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Studies** & **Dataset** & **Classifiers** & **Accuracy** \\ \hline [PERSON] et al. [38] & CICIDS2017 & RF & 99.67\% \\ [PERSON] et al. [39] & KDDCup99 & SVM-DT-NB & 99.62\% \\ [PERSON] et al. [21] & N-BaIoT & NB-J48-ANN & 99.10\% \\ [PERSON] et al. [26] & CICIDS2017 & DMLP & 91\% \\ [PERSON] et al. [35] & N-BaIoT & CART & 99\% \\ [PERSON] et al. [40] & N-BaIoT & RNN & 89.75\% \\ [PERSON] et al. [41] & N-BaIoT & Collective Deep Learning & 99.84\% \\ [PERSON] et al. [36] & N-BaIoT & LGBA-NN & 90\% \\
**Proposed Method** & **N-BaIoT** & **XGB-RF** & **99.94\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance Comparison with Other Studies. ## References

* [1] [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An integrated approach for a sustainable supplier selection based on Industry 4.0 concept. _Environ. Sci. Pollut. Res._**2021**, 1-19. [CrossRef] [PubMed]
* [2] [PERSON] The internet of things: Limitless opportunities for business and society. _J. Strateg. Innov. Sustain. Vol_**2017**, 12, 11-29. * [3] Symantec Internet Security Threat Report. 2019.