## 5 Results and Discussion
### Evaluation and Results  
Table 3 shows the test loss, test AUC, training loss, and training AUC obtained with each DL algorithm and each L. It was observed that GRU and BiGRU always gave better test accuracy than the other algorithms. BiLSTM showed values close to GRU or BiGRU; however, LSTM gave the worst results.  
Although the Area Under the ROC Curve (AUC) is not as appropriate to define the accuracy of an algorithm, it is important to compare the performance of the four DL algorithms. Table 4 shows the AUC calculated with each length L. Our model is of binary type since only two classes are evaluated (if the AUC is close to 0, indicating ham; or if the AUC is close to 1, indicating phishing). It was observed that GRU and BiGRU gave better results than LSTM and BiLSTM. Even at L = 200, GRU scored better. In addition, we also controlled the execution time of each DL algorithm in our model and observed that GRU was the one that was trained the fastest because it was trained in 240 s.  
Table 5 shows the computed micro avg and weighted avg metrics. In the three runs of the experiment with L = 1000, 500, and 200, it was again observed that GRU and BiGRU prevailed over LSTM and BiLSTM. In our particular case with unbalanced binary data, the most-appropriate metric to note is the weighted avg; but even for those, it was observed that GRU was equal to BiGRU. To continue our experiment with F1-Score, we have used L = 200 only because it represents 58.02% of our dataset. We do not use values below 200 words because the metrics decrease considerably.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **L** & **AUC = 0** & **AUC = 1** & **Time** \\  & & **(Ham)** & **(Phishing)** & **(s)** \\ \hline LSTM & 1000 & 0.74 & 0.72 & 1480 \\ BiLSTM & 1000 & 0.94 & 0.94 & 1660 \\ GRU & 1000 & 0.96 & 0.96 & 1320 \\ BiGRU & 1000 & 0.96 & 0.96 & 1860 \\ \hline LSTM & 500 & 0.81 & 0.80 & 700 \\ BiLSTM & 500 & 0.94 & 0.94 & 800 \\ GRU & 500 & 0.96 & 0.96 & 600 \\ BiGRU & 500 & 0.96 & 0.96 & 840 \\ \hline LSTM & 200 & 0.91 & 0.91 & 300 \\ BiLSTM & 200 & 0.94 & 0.94 & 320 \\ GRU & 200 & 0.96 & 0.96 & 240 \\ BiGRU & 200 & 0.95 & 0.95 & 320 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Area Under the ROC Curve (AUC) with L = 1000, 500, and 200.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithm** & **L** & **Test Loss** & **Test AUC** & **Train Loss** & **Train AUC** \\ \hline LSTM & 1000 & 0.27 & 0.92 & 0.27 & 0.93 \\ BiLSTM & 1000 & 0.17 & 0.98 & 0.12 & 0.99 \\ GRU & 1000 & 0.14 & 0.99 & 0.09 & 0.99 \\ BiGRU & 1000 & 0.15 & 0.96 & 0.07 & 1 \\ \hline LSTM & 500 & 0.23 & 0.95 & 0.23 & 0.95 \\ BiLSTM & 500 & 0.17 & 0.98 & 0.12 & 0.99 \\ GRU & 500 & 0.14 & 0.99 & 0.09 & 0.96 \\ BiGRU & 500 & 0.14 & 0.99 & 0.07 & 1 \\ \hline LSTM & 200 & 0.19 & 0.97 & 0.18 & 0.97 \\ BiLSTM & 200 & 0.19 & 0.98 & 0.10 & 0.99 \\ GRU & 200 & 0.16 & 0.98 & 0.09 & 0.99 \\ BiGRU & 200 & 0.17 & 0.98 & 0.07 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test loss, test AUC, training loss, and training AUC with L = 1000, 500, and 200.  
To define which of the DL algorithms worked best with the data embedded with GloVe, we performed the execution of the four algorithms using the K-fold cross-validation technique, where K = 5 and shuffle = true. Hence, the results obtained in each K-fold and the mean accuracy obtained in each algorithm can be seen in Table 6.