## 4 Results and Discussion
### Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Best Model (s)** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Enron & BERT & 99.31\% & 99.21\% & 99.31\% & 99.26\% \\ \hline SpamAssassin & BERT & 98.88\% & 98.23\% & 97.94\% & 98.09\% \\ \hline Ling & BERT & 100.00\% & 100.00\% & 100.00\% & 100.00\% \\ \hline TREC\_05 & BERT & 98.94\% & 98.73\% & 98.71\% & 98.72\% \\ \hline TREC\_06 & RoBERTa & 98.63\% & 96.95\% & 97.08\% & 97.02\% \\ \hline TREC\_07 & CNN & 99.45\% & 99.24\% & 99.76\% & 99.50\% \\ \hline CEAS\_08 & DistilBERT & 99.68\% & 99.68\% & 99.75\% & 99.71\% \\ \hline Nazario & RoBERTa & 99.51\% & 99.67\% & 99.33\% & 99.50\% \\ \hline Nigerian & BERT & 99.61\% & 99.53\% & 99.69\% & 99.61\% \\ \hline Merged & RoBERTa & 99.03\% & 99.17\% & 98.80\% & 98.98\% \\ \hline Merged (Balanced) & RoBERTa & 99.08\% & 99.20\% & 98.96\% & 99.08\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Best Performing Models on Ten Datasets. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Model** & **Enron** & **SpamAssassin** & **Ling** & **TREC\_05** & **TREC\_06** & **TREC\_07** & **CEAS\_08** & **Nazario\_5** & **Nigerian\_5** & **Merged** & **Merged** (Balanced)** \\ \hline Naive Bayes & 97.85\% & 96.70\% & 98.43\% & 92.27\% & 93.16\% & 96.61\% & 97.47\% & 98.82\% & 99.23\% & 92.93\% & 92.93\% \\ \hline Logistic Regression & 98.28\% & 96.25\% & 97.20\% & 97.46\% & 97.30\% & 99.04\% & 99.04\% & 97.97\% & 98.29\% & 97.19\% & 97.00\% \\ \hline SGD Classifier & 98.38\% & 97.68\% & 99.48\% & 97.00\% & 97.74\% & 99.28\% & 99.10\% & 98.82\% & 99.34\% & 96.66\% & 96.51\% \\ \hline XGBoost & 97.23\% & 96.79\% & 98.43\% & 95.77\% & 96.92\% & 98.80\% & 98.75\% & 97.80\% & 98.68\% & 95.65\% & 95.56\% \\ \hline Decision Tree & 95.38\% & 90.98\% & 94.76\% & 94.22\% & 94.82\% & 97.48\% & 98.23\% & 94.42\% & 95.06\% & 94.17\% & 94.13\% \\ \hline Random Forest & 98.09\% & 96.88\% & 98.60\% & 96.99\% & 96.63\% & 98.96\% & 99.15\% & 97.63\% & 99.34\% & 97.20\% & 97.23\% \\ \hline Extra Tree & 98.37\% & 97.23\% & 97.73\% & 97.14\% & 96.95\% & 98.93\% & 99.28\% & 97.97\% & 98.79\% & 97.31\% & 97.38\% \\ \hline CNN & 98.34\% & 98.11\% & 98.25\% & 96.57\% & 97.80\% & 99.45\% & 99.38\% & 98.37\% & 99.45\of each model on each dataset. We compared the results with the merged and balanced merged datasets. Detailed results are provided in Table 3. Moreover, we investigated each model's efficiency on all datasets regarding the training time. As discussed above, DL models demonstrated higher accuracy than traditional ML models. However, their computational demands and longer training times present challenges, as shown in Table 5. DL models typically require GPUs for efficient processing, making them less practical for real-time or large-scale applications compared to traditional ML models. To investigate this further, we measured the prediction time of the top-performing DL models (i.e., BERT, RoBERTa, and DistilBERT) on the merged and balanced merged datasets, which encompass all data samples from the 9 datasets. This will help in understanding which top-performing model is the best candidate for real-time phishing detection applications. As shown in Table 6, DistilBERT has the fastest prediction time across the two datasets, which makes it suitable for real-time phishing detection applications. The Ling dataset achieves the highest average accuracy among all datasets but exhibits lower recall values. This indicates that while models perform well in overall predictions on this dataset, they tend to miss a more significant number of phishing emails--a critical shortcoming in security applications. In contrast, the TREC_07 dataset achieves the highest recall, showing that models are most effective at identifying phishing emails in this dataset. Furthermore, the TREC_07 dataset also excels in precision, ensuring that phishing emails are accurately classified with minimal false positives.