## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** & **Jaccard Index** \\ \hline \multicolumn{6}{c}{**Fold 1**} \\ \hline legitimate & 98.44 & 98.15 & 98.33 & 98.24 & 96.53 \\ phishing & 98.44 & 98.67 & 98.52 & 98.59 & 97.23 \\ \hline
**Average** & **98.44** & **98.41** & **98.42** & **98.41** & **96.88** \\ \hline \multicolumn{6}{c}{**Fold 2**} \\ \hline legitimate & 99.00 & 99.06 & 98.69 & 98.88 & 97.78 \\ phishing & 99.00 & 98.96 & 99.25 & 99.11 & 98.23 \\ \hline
**Average** & **99.00** & **99.01** & **98.97** & **98.99** & **98.00** \\ \hline \multicolumn{6}{c}{**Fold 3**} \\ \hline legitimate & 99.20 & 99.40 & 98.80 & 99.10 & 98.21 \\ phishing & 99.20 & 99.05 & 99.53 & 99.29 & 98.58 \\ \hline
**Average** & **99.20** & **99.23** & **99.16** & **99.19** & **98.40** \\ \hline \multicolumn{6}{c}{**Fold 4**} \\ \hline legitimate & 99.18 & 99.46 & 98.67 & 99.07 & 98.15 \\ phishing & 99.18 & 98.95 & 99.58 & 99.26 & 98.54 \\ \hline
**Average** & **99.18** & **99.21** & **99.13** & **99.17** & **98.34** \\ \hline \multicolumn{6}{c}{**Fold 5**} \\ \hline legitimate & 99.28 & 99.43 & 98.94 & 99.18 & 98.38 \\ phishing & 99.28 & 99.16 & 99.55 & 99.35 & 98.71 \\ \hline
**Average** & **99.28** & **99.29** & **99.24** & **99.27** & **98.54** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of ODAE-WPDC approach with various measures and folds. The training accuracy (TA) and validation accuracy (VA) attained by the ODAE-WPDC system on test dataset is demonstrated in Figure 6. The experimental outcomes imply that the ODAE-WPDC algorithm has gained maximal values of TA and VA. Specifically, the VA seems to be higher than TA. Figure 4: Average analysis of ODAE-WPDC approach with various measures and folds. Figure 5: Average accuracy analysis of ODAE-WPDC approach with distinct folds. The training loss (TL) and validation loss (VL) achieved by the ODAE-WPDC approach on test dataset are established in Figure 7. The experimental outcomes infer that the ODAE-WPDC system has accomplished the least values of TL and VL. Specifically, the VL seems to be lower than TL. Figure 6: TA and VA analysis of ODAE-WPDC approach. Figure 7: TL and VL analysis of ODAE-WPDC approach. Finally, a detailed comparative study of the algorithm with other algorithms on WS phishing detection is given in Table 3[28; 29]. The experimental findings state that the ODAE-WPDC methodology has gained maximal performance over the other models. Figure 8 illustrates a comparative \(prec_{n}\) and \(reca_{l}\) inspection of the ODAE-WPDC system with recent models. The figure implies that the ODAE-WPDC approach has resulted in enhanced performance in terms of \(prec_{n}\) and \(reca_{l}\). With regard to \(prec_{n}\), the ODAE-WPDC system has obtained improved \(prec_{n}\) of 99.29%, whereas the DL-SGD, DL-RMSProp, DL-Adam, SI-BBA, PDGAN, and NIOSELM models have gained \(prec_{n}\) of 94.97%, 93.77%, 94.87%, 94.59%, 94.96%, and 94.65%, respectively. In addition, in terms of \(reca_{l}\), the ODAE-WPDC model has obtained higher \(reca_{l}\) of 99.24% whereas the DL-SGD, DL-RMSProp, DL-Adam, SI-BBA, PDGAN, and NIOSELM methods have achieved \(reca_{l}\) of 95.17%, 95.34%, 95.93%, 94.84%, 94.02%, and 94.66%, correspondingly. Figure 8: \(Prec_{n}\) and \(reca_{l}\) analysis of ODAE-WPDC approach with existing methodologies. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline ODAE-WPDC & 99.28 & 99.29 & 99.24 & 99.27 \\ DL-SGD & 94.64 & 94.97 & 95.17 & 94.50 \\ DL-RMSProp & 92.84 & 93.77 & 95.34 & 95.52 \\ DL-Adam & 94.69 & 94.87 & 95.93 & 95.27 \\ SI-BBA & 94.93 & 94.59 & 94.84 & 94.78 \\ PDGAN & 94.12 & 94.96 & 94.02 & 92.21 \\ NIOSELM & 93.40 & 94.65 & 94.66 & 90.86 \\ MLP-SL & 87.80 & 88.75 & 87.41 & 74.75 \\ SVM-SL & 83.37 & 87.22 & 88.54 & 75.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of ODAE-WPDC approach with recent algorithms.