## 5 Results
### Performance Scores of the Proposed Method  
We performed our feature selection method with different ML classifiers and obtained the best set of features for each of them. We compared their recall value with the proposed model in Figure 6, and it is clear that the model has a considerable advantage over them. Furthermore, evolutionary progress of each model is shown in Figure 7, which shows the iteration-based enhancement of the proposed model. The best, average, and worst performance scores, i.e.,the recall score, of each generation is plotted. Since we utilize elitism in the proposed algorithm, best-score trends always indicate improvement. However, based on average scores, it can be seen that the average success of new generations is sometimes lower than that of previous generations, but general improvement continues. This situation is the natural result of the GA, which includes the randomness and the natural selection principle. We used the same GA parameters to setup the heuristic-based feature selection algorithm, e.g., number of generations, population size, crossover type, mutation type, and local optimization ratio. Then, for each best feature list, we obtained their accuracy, precision, and recall scores, which are shown in Table 6. Although the most important measure for the phishing detection success is the recall score of the positive class, we also \begin{table}
\begin{tabular}{l l l l l l} \hline \multicolumn{2}{c}{**Versions**} & \multicolumn{1}{c}{**V1**} & \multicolumn{1}{c}{**V2**} & \multicolumn{1}{c}{**V3**} & \multicolumn{1}{c}{**V4**} \\ \hline \multicolumn{2}{c}{**\# of Features**} & \multicolumn{1}{c}{**25**} & \multicolumn{1}{c}{**35**} & \multicolumn{1}{c}{**45**} & \multicolumn{1}{c}{**55**} \\ \hline Logistic & Recall & 52.29 & 51.2 & 49.95 & _52.41_ \\ Regression & Accuracy & 73.12 & 72.95 & 73.05 & _73.84_ \\ Random & Recall & 79.48 & 79.33 & 81.65 & _87.86_ \\ Forest & Accuracy & 86.97 & 86.39 & 87.85 & _91.76_ \\ XGBoost & Recall & 80.66 & 85.65 & 85.81 & _86.36_ \\  & Accuracy & 87.44 & 90.48 & 90.63 & _90.91_ \\ Decision & Recall & 75.93 & 77.79 & 80.17 & _83.08_ \\ Tree & Accuracy & 84.81 & 85.91 & 86.62 & _87.39_ \\ Naive & Recall & 70.43 & 70.61 & 75.16 & _75.18_ \\ Bayes & Accuracy & 78.24 & 78.26 & 76.63 & _77.09_ \\ KNN & Recall & 78.05 & 79.72 & 79.37 & _83.15_ \\ Accuracy & 83.23 & 83.33 & 84.97 & _88.18_ \\  & Recall & 75.71 & 77.07 & _77.19_ & _84.79_ \\ ANN & Accuracy & 84.16 & 84.98 & 84.62 & _89.39_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Scores of feature selection with Sequential Feature Selection. \begin{table}
\begin{tabular}{l l l l l l} \hline \multicolumn{2}{c}{**Versions**} & \multicolumn{1}{c}{**DT-V1**} & \multicolumn{1}{c}{**LG-V2**} & \multicolumn{1}{c}{**RF-V3**} & \multicolumn{1}{c}{**XGB-V4**} \\ \hline \multicolumn{2}{c}{**\# of Features**} & \multicolumn{1}{c}{**23**} & \multicolumn{1}{c}{**22**} & \multicolumn{1}{c}{**28**} & \multicolumn{1}{c}{**16**} \\ \hline Logistic & Recall & 71.65 & 72.16 & 71.89 & 72.08 \\ Regression & Accuracy & 79.82 & 81.29 & 80.06 & 81.24 \\ Random & Recall & **87.04** & 84.77 & 86.87 & 84.86 \\ Forest & Accuracy & **91.29** & 88.85 & 91.11 & 88.76 \\ XGBoost & Recall & 76.98 & 74.61 & _85.28_ & 82.32 \\  & Accuracy & 85.34 & 83.44 & _90.26_ & 87.73 \\ Decision & Recall & _84.18_ & 82.15 & 83.99 & 82.42 \\ Tree & Accuracy & 86.81 & 86.71 & 86.68 & _86.99_ \\ Naive & Recall & 69.92 & 69.33 & 69.55 & _72.08_ \\ Bayes & Accuracy & 75.9 & 78.3 measured fundamental classification metrics such as accuracy and precision in order to better evaluate the overall success of the model. We observed that while maximizing the recall score, the other metrics were also at a reasonable level. Another important result is that our feature selection method improved the performance scores of each classifier compared to the case where all features were used. Figure 6: Comparison of feature selection methods with ML models. Figure 7: Evolutionary progress of the proposed method with ML models.