# Boosted Reptile Search Algorithm for Engineering and Optimization Problems
## 4 Experimental Results and Discussion
### Series of Analysis 2: RSRFT for Security in the IoT
#### 4.2.3 Results and Discussion  
The performance of the enhanced RSA based on TMO and RFO as the feature selection technique to improve the detection of IDSs in the IoT environment is discussed in this section. Table 8 shows the average results of the developed RSRFT to improve the detection of IDSs in the IoT environment using the training and testing sets. From these results, it can be noticed that RSRFT had a high ability to improve the detection of the IDSs in terms of accuracy. In addition, it can be observed that the behavior of RFO alone was the worst one among the tested algorithms using the training and testing sets. According to the results of the precision, recall, and F1-measure, it can be noticed that the developed algorithm was the first-ranked algorithm. This indicated that the ability of the developed RSRFT was better than exploring and exploiting the search space.  
Figures 8 and 9 show the average accuracy, precision, F1-measure, and recall using the training and testing sets, respectively. From these figures, we can see that the average of each measure (i.e., accuracy, precision, F1-measure, and recall) of RSRFT was better than that of the other methods using the training and testing sets. This was followed by the RSA, which provided better results than the other methods.  
Moreover, we used the Friedman test as a non-parametric test to assess if there was a significant difference between the developed RSRFT and the others. Table 9 shows the mean rank obtained using each approach over the training and testing sets and among the performance measures. From these results, it can be noticed that the developed RSRFT had the highest mean rank, and the difference between it and the others was significant since the \(p\)-value was less than 0.05, except for the precision value using the training set.  
From the previous results of the two experimental series, it can be observed that the developed RSRFT had high efficiency to determine the solution of different engineering problems, whereas we noticed the high ability of RSRFT to enhance the process of improving the detection of IDSs in the IoT environment by reducing the number of selected features. This resulted from boosting the operators of the RSA, RFO, and TMO to improve the rate of convergence towards the optimal solution. However, there are some limitations  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Target Class**} & \multicolumn{2}{c}{**KDDCup-99**} & \multicolumn{2}{c}{**NSL-KDD**} & \multirow{2}{*}{**Target Class**} & \multicolumn{2}{c}{**Bot-IoT**} & \multirow{2}{*}{**Target Class**} & \multirow{2}{*}{**Target Class**} & \multirow{2}{*}{**CICIDS-2017**} \\ \cline{2-2} \cline{5-10}  & **Train** & & & & & & & & **Train** & & **Test** \\ \hline Normal & 97,278 & 60,593 & 67,343 & 9710 & Normal & 370 & 107 & Benign & 727,397 & 163,572 \\ DoS & 391,458 & 229,853 & 45,927 & 7458 & DoS & 1,320,148 & 385,309 & DDoS & 112,901 & 25,388 \\ Probe & 4107 & 4166 & 11,656 & 2422 & DDoS & 1,541,315 & 330,112 & FTP-Patorator & 6997 & 1574 \\  & & & & & & & & SSH-Patorator & 5201 & 1169 \\ R2L & 1126 & 16,189 & 995 & 2887 & Reconnaissance & 72,919 & 18,163 & PortScan & 140,043 & 31,492 \\  & & & & & & & & Brute Force & 1329 & 299 \\ U2R & 52 & 228 & 52 & 67 & Theft & 65 & 14 & SQL Injection & 19 & 4 \\  & & & & & & & & XSS & 575 & 129 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Description of the datasets.