## 4 Results and Discussion  
The experimental validation of the ODAE-WPDC model is tested using a dataset from the Kaggle repository [27]. The dataset holds 4898 samples under a legitimate class and 6157 samples under a phishing class as depicted in Table 1. The results are examined in terms of distinct measures, such as accuracy, precision, recall, F-score, and Jaccard index. For effective detection performance, the values of these measures should be high. Figure 3 illustrates the confusion matrices produced by the ODAE-WPDC model under distinct folds. On fold-1, the ODAE-WPDC model recognizes 4816 samples under the legitimate class and 6066 samples under the phishing class. On fold-3, the ODAE-WPDC approach recognizes 4839 samples under the legitimate class and 6128 samples under the phishing class. Additionally, on fold-4, the ODAE-WPDC system recognizes 4833 samples under the legitimate class and 6131 samples under the phishing class. At last, on fold-5, the ODAE-WPDC methodology recognizes 4846 samples under the legitimate class and 6129 samples under the phishing class. \begin{table}
\begin{tabular}{l c} \hline \hline
**Class Name** & **No. of URLs** \\ \hline Legitimate & 4898 \\ Phishing & 6157 \\ Total Number of URL’s & 11,055 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset details. Table 2 and Figure 4 illustrate a brief classification result of the ODAE-WPDC approach under varying folds. The experimental outcomes indicate that the ODAE-WPDC model  
Figure 3: Confusion matrices of ODAE-WPDC approach: (**a**) Fold-1, (**b**) Fold-2, (**c**) Fold-3, (**d**) Fold-4, and (**e**) Fold-5. has resulted in maximum performance under all folds. For sample, with fold-1, the ODAE-WPDC model offers an average \(accu_{y}\) of 98.44%, \(prec_{n}\) of 98.41%, \(reca_{l}\) of 98.42%, \(F_{score}\) of 98.41%, and \(J_{Index}\) of 96.88%. Simultaneously, with fold-2, the ODAE-WPDC approach has an accessible average \(accu_{y}\) of 99%, \(prec_{n}\) of 99.01%, \(reca_{l}\) of 98.97%, \(F_{score}\) of 98.99%, and \(J_{index}\) of 98%. Concurrently, with fold-3, the ODAE-WPDC method has an obtainable average \(accu_{y}\) of 99.20%, \(prec_{n}\) of 99.23%, \(reca_{l}\) of 99.16%, \(F_{score}\) of 99.19%, and \(J_{index}\) of 98.40%. Along with that, with fold-4, the ODAE-WPDC system presents an average \(accu_{y}\) of 99.18%, \(prec_{n}\) of 99.21%, \(reca_{l}\) of 99.13%, \(F_{score}\) of 99.17%, and \(J_{index}\) of 98.34%. At last, with fold-5, the ODAE-WPDC approach has an obtainable average \(accu_{y}\) of 99.28%, \(prec_{n}\) of 99.29%, \(reca_{l}\) of 99.24%, \(F_{score}\) of 99.27%, and \(J_{index}\) of 98.54%. Figure 5 provides an average \(accu_{y}\) inspection of the ODAE-WPDC methodology under distinct folds. The figure implies that the ODAE-WPDC model has gained effectual outcomes under every fold. For instance, with fold-1, the ODAE-WPDC model has obtained an average \(accu_{y}\) of 98.44%. Additionally, with fold-2, the ODAE-WPDC approach has reached an average \(accu_{y}\) of 99%. With fold-3, the ODAE-WPDC system has attained an average \(accu_{y}\) of 99.20%. In addition, with fold-4, the ODAE-WPDC approach has obtained an average \(accu_{y}\) of 99.18%. At last, with fold-5, the ODAE-WPDC methodology has gained an average \(accu_{y}\) of 99.28%.