## 4 Experiments
### Evaluation on Unbalanced Dataset  
The quantity of phishing webpages on the internet is typically much lower compared to that of legitimate webpages. Consequently, it is paramount to employ an imbalanced dataset for model evaluation. We curated an imbalanced dataset consisting of 3637 verified phishing webpages and 40,066 benign webpages. The benign webpages were sampled from the top 10 million websites, providing a larger sampling space than any previous experimental setup to ensure the diversity of benign samples. Therefore, our experimental setup presents two key challenges compared to prior research: (1) We deliberately introduced a significantly large class imbalance, where the model tends to focus more on the plentiful benign webpage samples, potentially resulting in a diminished capability to accurately predict positive samples. (2) Due to the diversity and comprehensiveness of our benign samples, the test set contains negative sample patterns and features that are unseen or infrequently encountered in the training set. This poses a risk of the model misclassifying samples that should actually belong to the negative class as positive, leading to an increase in false positives. Table 2 presents the results of these models using different training data sizes (70%, 30%, and 10% of the total dataset). When utilizing 70% of the data for training, our method achieved an accuracy of 0.9853, outperforming all the other methods, including URLNet, DURLD, DTD, FS-NB, TCURL, and PhishBERT, which achieved accuracies ranging from 0.9679 to 0.9810. Additionally, our method demonstrated a high true-positive rate (TPR) of 0.9839, indicating its ability to effectively identify phishing webpages. Moreover, our method exhibited a lower false-positive rate (FPR) of 0.0146, suggesting its capacity to minimize the misclassification of benign webpages as phishing. The F1-score, a comprehensive measure of precision and recall, was significantly higher for our method at 0.9176, while the baseline methods ranged from 0.8244 to 0.8950. When training with only 30% of the data, our method continued to outperform the baselines, achieving an accuracy of 0.9567. Notably, our method exhibited a higher TPR of 0.9395 compared to the other methods, indicating its robustness in detecting phishing  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & & **URLNet** & **DURLD** & **DTD** & **FS-NB** & **TCURL** & **PhishBERT** & **Our Method** \\ \hline \multirow{4}{*}{70\% of Data} & Accuracy & 0.9756 & 0.9752 & 0.9661 & 0.9679 & 0.9737 & 0.9810 & 0.9853 \\ \cline{2-9}  & TPR & 0.9698 & 0.9620 & 0.9558 & 0.9565 & 0.9619 & 0.9730 & 0  
webpages. The FPR for our method was notably low at 0.0417, showcasing its ability to maintain a low misclassification rate of benign webpages. The F1-score for our method was 0.7832, surpassing those of the baseline methods, which ranged from 0.5704 to 0.6623. Even with a limited training set size of 10%, our method demonstrated commendable performance, achieving an accuracy of 0.8878. It showcased a relatively high TPR of 0.8708, implying its capability to identify a considerable proportion of phishing webpages. Furthermore, our method achieved a low FPR of 0.1107, indicating its ability to effectively distinguish benign webpages. The F1-score for our method was 0.5637, surpassing those of the baseline methods, which ranged from 0.3145 to 0.4634. Overall, our method consistently outperformed the baseline methods across varying training set sizes. It showcased robustness in identifying phishing webpages, maintaining a low misclassification rate of benign webpages and achieving higher F1-scores. These results highlight the effectiveness and superiority of our method in tackling the challenges posed by imbalanced datasets and varying training set sizes. Furthermore, we plotted the ROC curves and calculated the corresponding AUC values for several models under this experimental setup.