## 3 Proposed Methodology
### Feature Selection Using Hybrid Genetic Fire Hawker Optimizer (FHO)
Here, \(SP_{c}\) is represented as the safe area below the \(c\)-th fire hawk territory. Moreover, the prey might be transferred to a group of other fire hawks. Additionally, there is a probability that the prey will come near the fire hawks that become captured by nearby flames. Equation (10) demonstrates the position update development technique as follows:  
\[PR_{w}^{new}=PR_{w}+(r_{5}\times FH_{Alter}-r_{6}\times SP),\left\{ \begin{array} []{c}c=1,2,3,\ldots,n.\\ w=1,2,3,\ldots,r.\end{array} \right. \tag{10}\]Here, outside of the \(c\)-th fire hawk's area, \(SP\) represents the safe place. The mathematical representation for \(SP_{c}\) and \(SP\) is represented in Equation (11):  
\[SP_{c}=\frac{\sum_{w=1}^{r}PR_{w}}{a},\left\{ \begin{array}{l}w=1,2,3,\ldots,a.\\ c=1,2,3,\ldots,c.\end{array} \right. \tag{11}\]  
\[SP=\frac{\sum_{l=1}^{m}PR_{l}}{n},l=1,2,3,\ldots,n \tag{12}\]  
Within the search space, \(PR_{l}\) indicates the \(l\)-th prey. Algorithm 1 explains the FHO algorithm's pseudo-code. ```
Procedure Fire Hawk Optimizer Establish the initial locations for a potential solution \((Y)\)  Calculate values of fitness for initial potential solutions  Generate central fire as the global best \((GB)\) solution while Iteration < Higher number of iterations  Create the number of fire hawks for generating a random integer  number \((n)\)  Create preys \((PR)\) and fire hawks \((FH)\) in search space  Evaluate the overall distance between prey and fire hawks  Determine fire hawk location by integrating prey for\(c=1:n\)  Determine the fire hawks' new location by utilizing Equation (8) for\(w=1:r\)  Estimate the safe location lower \(c^{th}\) fire hawk area with Equation (11)  Evaluate the prey's location with Equation (9)  Using Equation (12), calculate the \(c^{th}\) fire hawk region outside the safe location  Equation (10) can be utilized to evaluate the location of prey end  Determine the potential fitness values of recently generated fire  hawks and prey  By handling central fire, generate the global best \((GB)\) solution end while  Return \(GB\) endprocedure
```  
**Algorithm 1**Pseudo-code for FHO algorithm. A population-based optimization algorithm, the genetic algorithm (GA), enhances the solution by computing multiple populations at once [26]. The GA provides high parameters for model optimization, and parallelism might be established as continuous or discrete. The GA was employed to determine the optimal hyperparameters for CNN model training. Each hyperparameter was signified by a chromosome present in a GA-based HPO. Every chromosome has many genes encoded in a binary format. Techniques like crossover, selection, and mutation were transferred in this chromosome to estimate the best parameters. A higher chance of selecting and transferring chromosomes to the next generation has been connected with the higher function of fitness values. In the subsequent generation, such chromosomes generate more individuals that combine enhanced features from both parents. By interchanging the percentage of genes from different chromosomes, crossover was developed. A mutation is another process that creates new chromosomes and involves randomly changing one or more chromosomal genes. The next generation will have a more extensive variety of processes of features and a reduced probability of lacking important ones due to crossover and mutation methods. Algorithm 2 illustrates the pseudo-code for GA-based HPO. Classification for Intrusion Detection Using Optimized Residual Dense-Assisted Multi-Attention Transformer  
Classification is the process of organizing data into homogenous groups or classes based on some shared features obtained in the feature. This study employed an optimized residual dense-assisted multi-attention transformer (Op-ReDMAT) model to classify the features. The proposed transformer encoder layer was utilized to predict the frequency distributions of different cyber-attacks based on each attack's mel-frequency cepstral coefficients (MFCC) structure.