## Appendix A Experiments Results  
The following tables, from Table A1 to Table A11, present detailed performance metrics for each model on each dataset. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training Time (S)** \\ \hline Naive Bayes & 97.85\% & 97.86\% & 97.58\% & 97.72\% & 0.4045 S \\ \hline Logistic Regression & 98.29\% & 97.11\% & 99.32\% & 98.21\% & 6.0422 S \\ \hline SGD Classifier & 98.35\% & 97.11\% & 99.11\% & 98.27\% & 4.7780 S \\ \hline XGBoost & 97.23\% & 95.46\% & 98.83\% & 97.12\% & 32.8451 S \\ \hline Decision Tree & 95.38\% & 94.93\% & 95.30\% & 95.12\% & 186.7102 S \\ \hline Random Forest & 98.09\% & 97.37\% & 98.61\% & 97.99\% & 92.4306 S \\ \hline Extra Tree & 98.37\% & 97.98\% & 98.58\% & 98.28\% & 251.8828 S \\ \hline CNN & 98.34\% & 98.21\% & 98.25\% & 98.23\% & 1394.9935 S \\ \hline RNN & 96.56\% & 98.11\% & 94.49\% & 96.26\% & 1198.6864 S \\ \hline LSTM & 98.14\% & 98.27\% & 97.75\% & 98.01\% & 1616.3018 S \\ \hline BERT & 99.31\% & 99.21\% & 99.31\% & 99.26\% & 705.5716 S \\ \hline DistilBERT & 99.16\% & 99.03\% & 99.17\% & 99.10\% & 420.9068 S \\ \hline RoBERTa & 99.19\% & 99.24\% & 99.02\% & 99.13\% & 713.6597 S \\ \hline GCN & 72.49\% & 72.45\% & 72.49\% & 72.43\% & 1456.7544 S \\ \hline \hline \end{tabular}
\end{table} 
Table A1: Performance Metrics for Phishing Detection Models On Enron Dataset (Training Time in Seconds denoted as **S**). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training Time (S)** \\ \hline Naive Bayes & 98.43\% & 100.00\% & 90.22\% & 94.86\% & 0.4096 S \\ \hline Logistic Regression & 97.20\% & 100.00\% & 82.61\% & 90.48\% & 0.4096 S \\ \hline SGD Classifier & 99.48\% & 100.00\% & 97.83\% & 98.36\% & 0.4595 S \\ \hline XGBost & 98.43\% & 98.82\% & 91.30\% & 94.92\% & 24.9872 S \\ \hline Decision Tree & 94.76\% & 81.00\% & 88.04\% & 84.38\% & 4.4804 S \\ \hline Random Forest & 98.60\% & 98.84\% & 92.39\% & 95.51\% & 4.0028 S \\ \hline Extra Tree & 97.73\% & 100.00\% & 85.87\% & 92.40\% & 11.6939 S \\ \hline CNN & 98.25\% & 92.71\% & 96.74\% & 94.68\% & 53.4167 S \\ \hline RNN & 95.45\% & 83.00\% & 90.22\% & 86.46\% & 97.6607 S \\ \hline LSTM & 98.43\% & 93.68\% & 96.74\% & 95.19\% & 109.0630 S \\ \hline BERT & 100.00\% & 100.00\% & 100.00\% & 100.00\% & 156.4819 S \\ \hline DistilBERT & 99.83\% & 100.00\% & 98.68\% & 99.34\% & 85.1270 S \\ \hline RoBERTa & 99.83\% & 98.70\% & 100.00\% & 99.35\% & 164.4693 S \\ \hline GCN & 86.71\% & 75.19\% & 86.71\% & 80.54\% & 101.6573 S \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance Metrics for Phishing Detection Models On SpamAssassin Dataset (Training Time in Seconds denoted as **S**). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training Time (S)** \\ \hline Naive Bayes & 93.16\% & 96.27\% & 72.57\% & 82.76\% & 0.2044 S \\ \hline Logistic Regression & 97.30\% & 96.58\% & 91.28\% & 93.85\% & 5.8902 S \\ \hline SGD Classifier & 97.74\% & 96.58\% & 95.22\% & 95.02\% & 3.0694 S \\ \hline XGBost & 96.92\% & 93.00\% & 93.39\% & 93.19\% & 19.8026 S \\ \hline Decision Tree & 94.82\% & 87.74\% & 89.59\% & 88.66\% & 30.6576 S \\ \hline Random Forest & 96.63\% & 96.04\% & 88.75\% & 92.25\% & 33.9499 S \\ \hline Extra Tree & 96.95\% & 97.09\% & 89.17\% & 92.96\% & 108.0892 S \\ \hline CNN & 97.80\% & 96.83\% & 95.80\% & 97.81\% & 332.5567 S \\ \hline RNN & 95.67\% & 91.43\% & 90.51\% & 90.97\% & 412.7603 S \\ \hline LSTM & 96.77\% & 96.80\% & 96.77\% & 96.78\% & 1802.2137 S \\ \hline BERT & 98.54\% & 96.82\% & 96.82\% & 96.82\% & 388.8494 S \\ \hline DistilBERT & 98.57\% & 96.57\% & 97.21\% & 96.89\% & 218.7989 S \\ \hline RoBERTa & 98.63\% & 96.95\% & 97.08\% & 97.02\% & 396.7849 S \\ \hline GCN & 76.17\% & 78.18\% & 76.17\% & 66.25\% & 566.2911 S \\ \hline \hline \end{tabular}
\end{table}