# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 4 Experiments and Results
### Discussion  
This evaluation of text-based features yields valuable insights regarding the performance of different classifiers in the detection of phishing emails. Each classifier displays distinct strengths and weaknesses, underscoring the significance of selecting an appropriate algorithm based on specific requirements. BDT achieves an impressive F1-score of 0.905, thereby showcasing a robust ability to balance precision and recall. BDT stands out among the individual classifiers, as it exhibits the highest F1 score, accuracy, precision, and recall. The interpretability of Decision Tree models may raise concerns; however, in this particular context, the emphasis on informative features contributes to its success. NN performs well, attaining an F1-score of 0.880, thereby demonstrating a good equilibrium between precision and recall. NN exhibits high accuracy and precision, rendering it effective in identifying positive instances. Although NN's performance is commendable, it slightly trails behind BDT, suggesting the latter's superiority in this evaluation.  
LR attains a moderate F1-score of 0.757, denoting harmonious performance in terms of precision and recall. Logistic Regression (LR) provides high levels of accuracy and precision. However, the recall value is considerably lower, suggesting a limitation in capturing all positive cases.  
Figure 9: Phishing detection classifier performance comparison—method 2.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **F1-Score** & **Accuracy** & **Precision** & **Recall** \\ \hline Method 2 (Soft Voting Ensemble) & 0.8512 & 0.9556 & 0.9442 & 0.7824 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Soft voting ensemble classification result.  
Support Vector Machine (SVM) demonstrates improved performance, with an F1-score of 0.844. This indicates a better balance between precision and recall compared to Logistic Regression (LR). Support Vector Machine (SVM) exhibits a comparatively low percentage of false positives, indicating its effectiveness in accurately identifying positive cases.  
BPM achieves an F1-score of 0.870, demonstrating high accuracy and precision, albeit at the cost of memory. BPM efficiently reduces the occurrence of incorrect positive results, demonstrating its proficiency in handling situations when there is an unequal distribution of classes.  
The trade-off associated with recollection is that BPM may fail to identify certain good examples. The weighted ensemble model demonstrates competitive performance, with an F1 score of 0.90. This strategy successfully achieves a desirable balance between precision and recall. Moreover, it possesses a short training duration, making it highly computationally efficient. Although select individual classifiers may have significantly higher precision, the overall balance is maintained.  
The soft voting method achieves an F1-score of 0.8512 while also maintaining a favorable equilibrium between precision and recall. It is suited for situations where increased precision or recall is more important. Soft voting is particularly effective in the field of ensemble learning for detecting phishing.  
It is important to emphasize that the choice of the most suitable classifier or ensemble approach depends on unique requirements. The BDT classifier stands out as the highest-performing individual classifier, while the ensemble techniques (Method 1 and Method 2) demonstrate competitive performance, providing a favorable trade-off between precision and recall. The inclusion of ROC curves offers further validation of this evaluation, highlighting the trade-off between sensitivity and specificity demonstrated by each classifier and ensemble approach. These comprehensive assessments provide significant insights into the benefits and considerations of each technique in recognizing phishing emails.