## 3 Proposed Model
### Stage 1: Feature Selection Using GA Algorithm  
The feature selection process utilizes the GA algorithm, which is a well-known evolutionary optimization technique. GA mimics natural selection, where the fittest individuals are selected to pass their genes to the next generation. In the context of feature selection, GA is employed to identify the most relevant subset of features that contribute significantly to the model's performance while minimizing irrelevant or redundant features. This optimization process allows for the reduction of the dataset's dimensionality, improving the model's computational efficiency without sacrificing accuracy. This algorithm is a novel metaheuristic optimization approach modeled on North American bald eagles and their hunting behavior [40; 41; 42]. Bald eagles always select areas for hunting that are prime locations for food in large quantities, a behavior that can be quantitatively represented by Equation (1):  
\[P_{new,j}=P_{best}+\alpha\times r(P_{ave}-P_{i}) \tag{1}\]  
where the parameter \(\alpha\) is used to control positional variations within a range of 1.5, and \(r\) is an arbitrary integer that can take a value of either 0 or 1. Then, the eagle will search in a location close by that has now become available in the search process. In the equation, \(P_{best}\) denotes the search area selected by the eagle according to the optimal location identified during the search. In this phase, the eagle seeks prey in the identified location, moving in spirals to allow the hunt to be completed more rapidly. The optimal swooping location can be mathematically expressed as follows:  
\[ \begin{array}{ll}P_{i,new}&=P_{i}+y(i)\times(P_{i}-P_{i+1})+x(i)\times(P_{i} -P_{mean})\\ x(i)&=\frac{xr(i)}{\max(|x|)},\,y(i)=\frac{yr(i)}{\max(|yr|)}\\ xr(i)&=r(i)\times\sin(\theta(i)),\,yr(i)=r(i)\times\cos(\theta(i))\\ \theta(i)&=a\times\pi\times rand\\ r(i)&=\theta(i)+R\times rand\end{array}  \tag{2}\]  
In the above equation, parameter \(a\) represents the angle between the point of search and the center, with values ranging from 5 to 10. Parameter \(R\) denotes the number of search \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Cited Work** & **Advantages** & **Limitations** \\ \hline [PERSON] and [PERSON] [34] & Accurate malware detection using GRU. & Generalizability to different attack types is unclear. \\ \hline [PERSON] et al. [31] and [PERSON] et al. [33] & Strong detection accuracy for serious attacks. & Computational complexity may hinder real-time performance. \\ \hline [PERSON] et al. [37] & Adaptable to varying network conditions. & Dynamic switching may introduce detection delays. \\ \hline [PERSON] et al. [38] & Effective for real-time detection in power grids. & Limited to power grid environments. \\ \hline [PERSON] et al. [31] & Efficient and reliable threat detection. & RNNs may result in slower processing speeds. \\ \hline [PERSON] et al. [39] and [PERSON] et al. [35] & Balances accuracy and efficiency. & Uncertain performance in highly heterogeneous environments. \\ \hline [PERSON] [36] & Superior detection of large-scale IoT attacks, suitable for resource-constrained devices. & Handling diverse IoT data types may be a challenge. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)cycles, with values ranging from 0.5 to 2 [43; 44]. During the swooping phase, the bald eagle swoops toward the prey and moves into the most appropriate area for the target. \[ \begin{array}{ll}P_{i,new}&=rand\times P_{best}+x(i)\times(P_{i}-C_{1}\times P _{mean})\\ &+y_{1}(i)\times(P_{i}-C_{2}\times P_{best})vec_{1},\alpha\in[1,2]\\ x_{1}(i)&=\frac{x(i)}{\max(|x|)},\;y_{1}(i)=\frac{y(i)}{\max(|y|)}\\ r(i)&=r(i)\times\sinh[(\theta(i))],yr(i)\\ &=r(i)\times\cosh[(\theta(i))]\\ \theta(i)&=a\times\pi\times randver(i)=\theta(i)\end{array}  \tag{3}\]  
In the execution of the GA-FS algorithm, each objective can be combined into the method as one primary formula in such a way that all major significances are recognized [45; 46; 47].