## 7 Result and Discussion
### RQ2: What Are the Existing ML-Based Approaches to Detect Attacks in RPL-Based 6 LoWPAN?
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Ref/Year** & **A1** & **A2** & **A3** & **A4** & **A5** & **A6** & **A7** & **Advantages and Limitations** & & & & & \\ \hline \multirow{9}{*}{[84], (2018)} & DT. Pearson & ML-based & 17 & H& H& H& H, (Cong & Precision) & & & & & \\  & Configuration & \multirow{2}{*}{[84], (2018)} & \multirow{2}{*}{[83]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} & \multirow{2}{*}{[8]} \\  & Histogram & & ANN & & & & & & & & & & & \\ \cline{5-12}  & & & & & & & & & & & & & & \\ \cline{5-12}  & & & & & & & & & & & & & \\ \cline{5-12}  & & & & & & & & & & & & & \\ \hline \multirow{9}{*}{[84], (2017)} & \multirow{9}{*}{[85]} & \multirow{9}{*}{[86]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} \\  & & & & & & & & & & & & & & \\ \cline{5-12}  & & & & & & & & & & & & & & \\ \cline{5-12}  & & & & & & & & & & & & & \\ \cline{5-12}  & & & & & & & & & & & & & \\ \hline \multirow{9}{*}{[84], (2021)} & \multirow{9}{*}{[85]} & \multirow{9}{*}{[86]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} & \multirow{9}{*}{[8]} \\  & & & & & & & & & & & & & & \\ \cline{5-12}  & & & & & & & & & & & & & \\ \cline{5-12}  & & & & & & & & & & & & & \\ \cline{5-12}  & & & & & & & & & & & & & \\ \hline \multirow{9}{*}{[84], (2022)} & \multirow{9}{*}{[85]} & \multirow{9}{*}{[86]} & \multirow{9}{*}{[8]}  
In [66], the authors presented an ML approach for detecting attacks in RPL-based IoTs. The proposed approach is designed to detect a combination of various RPL attacks, such as Rank and VN attack, Rank and Sybil attack, Rank and BH attack, and Decreased Path Metric attack. The authors evaluated the vulnerability of the most popular RPL's Objective functions (MRHOF and OF0) against invoked attacks in their work. In addition, the authors performed data preprocessing, feature selection and reduction, sampling technique, and normalization, which are essential to getting the best attack detection result. Moreover, the authors initialized their proposed dataset based on IoTs features and network metrics to test the performance of the proposed approach. The generated dataset comprises 24 features collected based on network and power metrics. Furthermore, the authors used several classification algorithms, such as NB, SVMs, MLP, RF, and ZeroR classifiers. Regarding the simulation scenarios, the authors ran seven experiments. Some experiments were performed using a complete set of features, while the rest used an optimal set of features. As for the results, the SMOTE-MLP and Subsample-NB obtained the best results compared to other classifiers in the first experiment. Moreover, from the second to the fifth experiment, the SMOTE-MLP reported superior results compared to the others. Additionally, in the sixth experiment, the ensemble model (Voting-MLP and RF) acquired the top results in RSME, Mean Absolute Percentage Error (MAPE), ROC average, and correctly classified instances compared to the other models. Moreover, the seventh and eighth experiments, the MLP network and MLP network metrics produced the best results when using the OF0 type. Finally, the overall performance of the ML algorithms shows that the voting (MLP and RF) classifiers gained excellent outcomes compared to other ML algorithms.