## 3 Research Methodology
### Dataset Description and Pre-Processing
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metric** & **Length** & **Info** & \begin{tabular}{c} **Trans** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Rcv** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **TR/RR** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{c} **Sources** \\ **Count** \\ \end{tabular} &
\begin{tabular}{c} **Dest** \\ **Count** \\ \end{tabular} \\ \hline Length & 83.58 & 15.5 & 64.0 & 64.0 & 87.0 & 99.0 & 102.0 \\ Info & 3.14 & 1.65 & 1.0 & 2.0 & 3.0 & 5.0 & 7.0 \\ Trans Rate (per 1000 ms) & 0.15 & 0.07 & 0.0 & 0.1 & 0.16 & 0.2 & 0.29 \\ Rev Rate (per 1000 ms) & 0.36 & 0.21 & 0.0 & 0.21 & 0.34 & 0.49 & 0.9 \\ TR/RR & & 0.62 & 1.08 & 0.0 & 0.3 & 0.46 & 0.71 & 225.0 \\ Sources Count/s & 147.97 & 69.71 & 1.0 & 95.0 & 156.0 & 201.0 & 286.0 \\ Destinations Count/s & 361.08 & 208.91 & 1.0 & 209.0 & 345.0 & 493.0 & 901.0 \\ Trans Total Duration/s & 0.37 & 0.45 & 0.0 & 0.18 & 0.29 & 0.42 & 9.99 \\ Rcv Total Duration/s & 0.84 & 0.65 & 0.0 & 0.46 & 0.82 & 1.0 & 9.93 \\ Trans Average/s & 0.0 & 0.01 & 0.0 & 0.0 & 0.0 & 0.0 & 1.47 \\ Rcv Average/s & 0.0 & 0.01 & 0.0 & 0.0 & 0.0 & 0.0 & 1.47 \\ DAO & 28.91 & 66.58 & 0.0 & 0.0 & 0.0 & 0.0 & 286.0 \\ DIS & 51.08 & 78.78 & 0.0 & 0.0 & 0.0 & 145.0 & 260.0 \\ DIO & 10.15 & 29.74 & 0.0 & 0.0 & 0.0 & 0.0 & 227.0 \\ Label & 0.45 & 0.5 & 0.0 & 0.0 & 0.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation data. adjustment of DAO, DIO, and 6 LoWPAN characteristics highlight their crucial functions in the Routing Protocol for RPL. The combination of harmless and destructive datasets maintained consistency in structural characteristics, and using Scikit-learn, properties showing strong correlations to the output variable were specifically separated. This comprehensive approach emphasizes the thorough identification and selection of characteristics to achieve research goals within the specific field. Three attacks were evaluated, focusing on the reception elements and transmission of 6 LoWPAN protocol packets and DIO control messages. The selection of relevant attributes was carried out using deep neural networks, Pearson correlation coefficients, and histograms. The DNN technique was used to evaluate the value of the recovered characteristics and identify the most effective number of neurons. The information on the features is provided in Table 6.  
\[S=\frac{p(\Sigma ab)-(\Sigma a)(\Sigma b)}{\sqrt{[p\Sigma c^{2}-(\Sigma c)^{2} ][p\Sigma d^{2}-(\Sigma d)^{2}]}} \tag{1}\]
* Feature Normalization: The feature normalization procedure is used to standardize datasets and populate them into an even range. The min-max scaling procedures were the most direct, uncomplicated, and adaptable method for normalizing the values of the selected attributes. The newly introduced feature, designated as Y*, is adjusted to span a range of values between 0 and 1. RPL stands for the values that have been normalized. The variable \"RPL_FACTOR\" denotes the starting value of the feature, whereas \"RPL_FACTORmin\" and \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Features** & **Attack Detected Min–Max (X)** \\ \hline
1 & Reception rate HF, VN & 6.6879305 \(\times\) 10\({}^{-3}\) \\
2 & Transmission rate HF, DR, and VN & 7.8469016 \(\times\) 10\({}^{5}\) \\
3 & Rcv average per Second HF, DR, and VN & 6.7000000 \(\times\) 10\({}^{0}\) \\
4 & Rcv total duration Per Second HF, DR, and VN & 1.0000000 \(\times\) 10\({}^{3}\) \\
5 & DAO DR & 9.9997274 \(\times\) 10\({}^{0}\) \\
6 & DIS DR and VN & 1.0000000 \(\times\) 10\({}^{0}\) \\
7 & Trans total duration per Second HF, VN & 3.0000000 \(\times\) 10\({}^{0}\) \\
8 & DIO HF, DR, and VN & 1.0000000 \(\times\) 10\({}^{0}\) \\
9 & TR/RR HF, DR, and VN & 4.5000000 \(\times\) 10\({}^{0}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Features and attack detection. Figure 3: Feature importance from Linear Discriminant Analysis (LDA).