# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 4 Experiments and Results
### Comparison of Our Approach's Results with Existing Works  
This section provides a detailed analysis of how our results compare to those of prominent earlier research. It is crucial to acknowledge that comparing our technique directly with existing works is difficult because each study uses different evaluation approaches, leading to a diverse range of methods. These variations include different sample sets, class ratios, and metrics. Tables 6 and 7 present the results of existing works, showcasing their performance in terms of common evaluation metrics such as accuracy and F1-score. This table also provides information about the methods used and the insights gained from these studies. In the assessment of the efficacy of our two proposed techniques for detecting phishing emails, it is apparent that both the weighted ensemble and soft voting methodologies outperform individual classifiers and demonstrate competitive performance. By utilizing a dataset consisting of 15,000 non-spam and 3000 spam emails, our thorough analysis highlights the strengths and weaknesses of different classifiers. The individual classifiers, namely BDT, NN, LR, SVM, and BPM, exhibit diverse performance in terms of precision, recall, and F1 score. BDT stands out, with an impressive F1-score of 0.905, showcasing a resilient balance between precision and recall. While NN performs well, with an F1-score of 0.880, BDT surpasses it, indicating its superiority within this context. LR, SVM, and BPM display moderate-to-high performance in various aspects, highlighting the trade-offs between precision and recall. The ensemble methods, particularly weighted ensemble, and soft voting, demonstrate noteworthy F1-scores of 0.90 and 0.8512, respectively. The weighted ensemble approach demonstrates a notable balance between precision and recall, as well as efficient training times, indicating computing efficiency. However, soft voting achieves a balanced trade-off between precision and recall, making it appropriate for situations when either parameter is more important. Although ensemble approaches have somewhat lower precision than individual classifiers, they efficiently maintain an overall balance. Upon comparing our results with the findings reported in Tables 6 and 7, our methods demonstrate a competitive level of performance. The BDT classifier is identified as the highest-performing individual classifier, while the ensemble approaches (Method 1 and Method 2) demonstrate effectiveness in attaining a balance between precision and recall. Recognizing the variation in evaluation methods used in previous studies is essential, taking into account elements such as the sets of samples, ratios of different classes, and metrics used. Our two proposed ensemble approaches have shown encouraging results in identifying phishing emails, indicating their usefulness in achieving a balance between precision and recall. The thorough comparison with previous studies further confirms the competitive performance of our methods, highlighting the significance of taking specific criteria into account when choosing the most appropriate classifier or ensemble method for detecting phishing. The ROC curves provide further insights into the sensitivity and specificity demonstrated by each technique, offering a comprehensive perspective on their strengths and limitations in detecting phishing emails.