# Boosted Reptile Search Algorithm for Engineering and Optimization Problems
## 4 Experimental Results and Discussion
### Series of Analysis 2: RSRFT for Security in the IoT  
Within this section, we evaluated the applicability of RSRFT to improve the detection of IDSs in the IoT environment [81]. In general, the problem definition of IDSs in the Internet of Things (IoT) is the need to detect and prevent unauthorized access, data breaches, and malicious activities in IoT devices and networks. With the rapid growth of the IoT, there has been an increase in the number and complexity of connected devices, which has also led to an increase in the number of potential attack vectors and vulnerabilities. As a result, there is a growing need for effective IDSs that can identify and respond to these threats in real-time [82].  
The challenge of developing IDSs for the IoT is the complexity of the networks and the diversity of the devices. The systems need to be designed to detect and respond to a wide range of attack types, including malware, denial-of-service attacks, and insider threats. Furthermore, the systems need to be able to identify anomalous behaviors and patterns that may indicate an attack, while also avoiding false positives [83].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Estimated Values for Parameters**} & \multirow{2}{*}{**Optimal**} \\ \cline{2-2} \cline{4-6}  & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) & **Weight** \\ \hline RSRFT & 69.003908 & 89.003914 & 1 & 789.52330 & 2.965888 & 0.307109 \\ RFO & 72 & 93 & 762 & 2 & 1 & 0.25359 \\ RSA [17] & 70.0347 & 90.0349 & 1.0000 & 801.7285 & 2.9740 & 0.31176 \\ TLBO [76] & 70 & 90 & 1 & 810 & 3 & 0.313656611 \\ NSGA-II [77] & 70 & 90 & 1.5 & 1000 & 3 & 0.470400 \\ WCA [78] & 70 & 90 & 1 & 910 & 3 & 0.313656 \\ MVO [79] & 70 & 90 & 1 & 910 & 3 & 0.313656 \\ MVO [80] & 70 & 90 & 1 & 910 & 3 & 0.313656 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The value of the estimated parameters using RSRFT to solve the multiple disc clutch brake problem.  
To address this problem, we present an alternative technique to detect the intrusions in the IoT. In this experiment, RSRFT was used as a feature selection technique to enhance the process of selecting the important features of the collected data in the IoT environment. This can be achieved through by the Boolean version of RSRFT by converting the real value of each solution into a binary value.  
The proposed RSRFT starts by generating the value of the initial population. This can be defined as  
\[X=LB+rand\times(UB-LB) \tag{32}\]  
where \(UB\) and \(LB\) are the upper and lower boundaries, respectively. In this experiment, the values of \(UB=1\) and \(LB=0\), while \(rand\in[0,1]\). This step is followed by obtaining the binary of \(X_{i}\) using the following formula:  
\[BX_{i}=\left\{\begin{array}{ll}1&if\ X_{i}>0.5\\ 0&otherwise\end{array}\right. \tag{33}\]  
The next process is to assess the quality of the selected features, which correspond to the ones in \(X_{i}\), using the following formula.  
\[Fit_{i}=\eta\times\gamma_{i}+(1-\eta)\times(\frac{|BX_{i}|}{D}) \tag{34}\]  
where \(\gamma_{i}\) denotes the classification error of the KNN classifier, whereas the term \((\frac{|BX_{i}|}{D})\) refers to the ratio of features selected using \(X_{i}\). The value of \(\eta\in[0,1]\) is applied as the weight of the two terms of Equation (34).  
Thereafter, according to the fitness values, we determined the best of them and their corresponding solution \(X_{b}\). Then, we used this solution and the operators of RSRFT that are discussed in Sections 3.2 and 3.3 to update \(X\). The next step after reaching the stop conditions is to compute the efficiency of \(X_{b}\) using the testing set of IoT data.  
In the following sections, we introduce the datasets used in this experiment to assess RSRFT as an FS method. The comparison was conducted with moth-flame optimization (MFO) [60], the bat (BAT) algorithm [84], transient search optimization (TSO) [83], and the RSA. The parameter of each approach was assigned based on the original implementation.