## 4 Results and Discussion
The test results are listed comparatively in Table 3 to provide a comparative analysis of those classifiers applied to different datasets, shedding light on the strengths and weaknesses of the classifiers with different sets of features and instances. The performance metrics for comparison were chosen as accuracy, precision, recall and F1 score. As shown in the \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Classifier** & **Feature Set** & **\# Features** & **Time** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{SVM} & URL & 18 & 0.03 & 0.91 & 0.91 & 0.91 & 0.91 \\  & URL + HTML & 23 & 0.03 & 0.94 & 0.93 & 0.95 & 0.94 \\  & URL + HTTP & 20 & 0.03 & 0.97 & 0.96 & **0.99** & 0.97 \\  & HTML & 5 & 0.03 & 0.85 & 0.81 & 0.91 & 0.86 \\  & HTML + HTTP & 7 & 0.03 & 0.92 & 0.88 & 0.97 & 0.93 \\  & HTTP & 2 & 0.02 & 0.93 & 0.88 & **0.99** & 0.93 \\  & URL + HTML + HTTP & 25 & 0.05 & 0.98 & 0.97 & **0.99** & 0.98 \\ \hline \multirow{8}{*}{SGD} & URL & 18 & 0.03 & 0.84 & 0.86 & 0.81 & 0.84 \\  & URL + HTML & 23 & 0.03 & 0.92 & 0.91 & 0.93 & 0.92 \\  & URL + HTTP & 20 & 0.02 & 0.97 & 0.97 & 0.97 & 0.97 \\  & HTML & 5 & 0.02 & 0.84 & 0.84 & 0.85 & 0.84 \\  & HTML + HTTP & 7 & 0.03 & 0.91 & 0.88 & 0.95 & 0.92 \\  & HTTP & 2 & 0.03 & 0.92 & 0.87 & **0.99** & 0.93 \\  & URL + HTML + HTTP & 25 & 0.03 & 0.98 & 0.98 & 0.97 & 0.98 \\ \hline \multirow{8}{*}{NB} & URL & 18 & **0.01** & 0.67 & 0.93 & 0.37 & 0.53 \\  & URL + HTML & 23 & 0.03 & 0.68 & 0.95 & 0.37 & 0.54 \\  & URL + HTTP & 20 & 0.02 & 0.67 & 0.93 & 0.37 & 0.53 \\  & HTML & 5 & 0.02 & 0.75 & 0.68 & 0.94 & 0.79 \\  & HTML + HTTP & 7 & 0.02 & 0.81 & 0.75 & 0.95 & 0.84 \\  & HTTP & 2 & 0.03 & 0.92 & 0.87 & **0.99** & 0.93 \\  & URL + HTML + HTTP & 25 & 0.03 & 0.68 & 0.95 & 0.38 & 0.54 \\ \hline \multirow{8}{*}{MLP} & URL & 18 & 0.61 & 0.95 & 0.92 & 0.97 & 0.95 \\  & URL + HTML & 23 & 0.08 & 0.93 & 0.90 & 0.96 & 0.93 \\  & URL + HTTP & 20 & 0.12 & 0.98 & 0.97 & **0.99** & 0.98 \\  & HTML & 5 & 0.17 & 0.84 & 0.83 & 0.87 & 0.85 \\  & HTML + HTTP & 7 & 1.00 & 0.91 & 0.88 & 0.95 & 0.92 \\  & HTTP & 2 & 0.08 & 0.92 & 0.87 & **0.99** & 0.93 \\  & URL + HTML + HTTP & 25 & 0.06 & 0.96 & 0.97 & 0.95 & 0.96 \\ \hline \multirow{8}{*}{k-NN} & URL & 18 & 0.05 & 0.93 & 0.89 & 0.98 & 0.93 \\  & URL + HTML & 23 & 0.05 & 0.92 & 0.91 & 0.93 & 0.92 \\  & URL + HTTP & 20 & 0.05 & 0.97 & 0.94 & **0.99** & 0.97 \\  & HTML & 5 & 0.05 & 0.85 & 0.85 & 0.85 & 0.85 \\  & HTML + HTTP & 7 & 0.05 & 0.93 & 0.91 & 0.96 & 0.94 \\  & HTTP & 2 & 0.05 & 0.92 & 0.87 & 0.98 & 0.92 \\  & URL + HTML + HTTP & 25 & 0.05 & 0.97 & 0.95 & 0.98 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection statistics for each combination of the feature sets and classifiers. table, the number of features of the datasets was variable, ranging from 25 to 48 features, indicating different levels of complexity in the feature space. Across all classifiers, the performance metrics varied based on the dataset and the number of features considered. However, SVM and DT consistently demonstrated robust performances across different datasets and feature dimensions, suggesting their suitability for phishing detection tasks. The experimental results obtained in our work were also compared to earlier works in the literature. The comparison is summarized in Table 4, where the best classification models and accuracies are listed. As shown in the table, our work stands out among the cited studies in phishing attack detection, showcasing a significant dataset size of 1000 instances and utilizing various classifiers, resulting in an impressive accuracy of  
Figure 1: The classification times vs. the highest F1 scores achieved using each classifier and the corresponding feature subsets.