## 5 Results
### Comparison of Our Proposal and Other Methods  
For each ML classifier, we compared the performance of our proposal with other feature selection methods, i.e., RFLV, RFE, SFS, and SFMFS, respectively, which are detailed in Section 5.1. We also checked the performance of the models without any feature selection method, which means that we used all features in training. The results, which are described in Figure 6, prove that our method provides better scores than all others. Due to the fact that all feature selection methods reduced the features, they increased the efficiency in terms of training time and total inference time. For instance, the total inference time with 73 features, that is, all features, is 1.0066 s, and our feature selection method selected 44 features and reduced the feature extraction time to 0.6380 s while increasing performance metrics such as the recall score. These scores are obtained in the Google Colab environment, which has a Tesla T4 GPU-NVIDIA-SMI. This decrease in cost, 36.62%, is critical because it will affect the total prediction time of the model.  
Additionally, we compared the best features obtained with each method and our method to understand the relationships between them. The first observation is that 10 of the 73 features are selected by all. Second, our method selected 12 features that were not selected by others while having the best phishing detection scores. The number of features selected by only one method is 1, 1, 2, and 0 for RFLV, RFE, SFS, and SFMFS, respectively. This shows that our method finds 12 different features that could not be discovered by other methods and increases the classification success.  
Finally, as illustrated in Figure 8, there is a notable convergence among five different feature selection algorithms, as evidenced by the selection of 10 common features. These features include criteria such as \"Is domain name IP address?\", \"Number of hyphens in domain name\", and \"Number of digits in the host name field\". Such a convergence is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** \\ \hline Random Forest & **92.93 \(\pm\) 0.71** & **93.45 \(\pm\) 0.69** & **89.05 \(\pm\) 1.22** \\ XGBost & 91.53 \(\pm\) 0.36 & 91.96 \(\pm\) 0.58 & 87.24 \(\pm\) 0.34 \\ Log. Reg. & 81.29 \(\pm\) 0.72 & 78.17 \(\pm\) 0.62 & 76.95 \(\pm\) 0.53 \\ KNN & 89.77 \(\pm\) 0.60 & 90.01 \(\pm\) 0.39 & 84.65 \(\pm\) 0.47 \\ Naive Bayes & 85.40 \(\pm\) 0.48 & 84.12 \(\pm\) 0.49 & 80.90 \(\pm\) 1.06 \\ Decision Tree & 88.83 \(\pm\) 0.59 & 86.31 \(\pm\) 0.52 & 86.64 \(\pm\) 0.37 \\ ANN & 90.07 \(\pm\) 0.36 & 89.07 \(\pm\) 0.61 & 86.56 \(\pm\) 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance scores of different models with selected features.  
expected as algorithms typically exhibit some degree of similarity in feature identification. However, our GAFS approach diverges from this pattern, identifying 12 additional unique features not highlighted by the other algorithms. These include features like \"Is 'username' used in the URL?\", \"Is there a 'query' field in the URL?\", and \"Is a well-known brand name used in the URL?\". This distinction underscores the ability of feature selection algorithms to identify the unique characteristics of individual features. Although some features, like \"Is a well-known brand name used in the URL?\", cannot be used in real time due to processing costs, the proposed models demonstrate that the GA can select different features compared to other feature selection models. Furthermore, the results suggest that the proposed GAFS approach we used has the ability to discern the collective distinctiveness of these features as a group, rather than just individually. This indicates that the GA approach is adept at capturing a broader and more nuanced spectrum of features, which may be critical in applications where understanding the interplay and collective significance of various features is crucial.