## 5 Conclusions  
Phishing attacks are a significant threat to the security of organizations and individuals, and machine learning can be a powerful tool for detecting them. Classifiers and features are essential components of this process. By selecting the most appropriate classifier and evaluating the relevance of different features, it is possible to improve the performance of phishing detection.  
In this work, we comprehensively evaluated different classifiers as well as features to find out their contributions to the performance of phishing detection in terms of several metrics such as accuracy, recall, precision, F1 score, and detection time. We also introduced a new and publicly available phishing dataset for the research community. According to our evaluations, the URL + HTTP feature set paired with the DT classifier appeared as the top performer, achieving the highest F1 score (0.99), accuracy (0.99), and precision (0.99). On the contrary, the NB classifier with several feature sets exhibited the lowest F1 score (0.53), precision (0.68), and accuracy (0.67). Additionally, the recall performances varied across the classifiers, with SVM consistently providing the highest recall (0.99) most of the time. The analysis of classification time revealed that the DT classifier yielded not only the best F1 score but also one of the shortest classification times, while NB was the fastest with the URL feature set (0.01 s), and MLP was the slowest with the HTML + HTTP feature set (1.00 s). Further testing on other benchmark datasets reaffirmed the robust performances of DT and SVM, positioning them as reliable choices for phishing detection tasks.  
With the increasing prevalence of phishing attacks, it is crucial that organizations invest in the development of robust and effective phishing detection systems. Based on our findings, we can conclude that organizations can improve their ability and speed to detect and respond to phishing attacks and better protect themselves and their customers from this threat by utilizing machine learning with appropriate classifiers and feature sets.  
In future work, other possible features and classifiers can be analyzed. Also, a cost analysis can be performed by considering the effects of the collection speed of the features.  
Conceptualization, E.S.G.; methodology, S.K. and E.S.G.; software, S.K.; validation, S.K. and E.S.G.; data curation, S.K.; writing--original draft preparation, S.K. and E.S.G.; writing--review and editing, E.S.G.; supervision, E.S.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Study** & **Dataset Size** &
\begin{tabular}{l} **Classification** \\ **Method** \\ \end{tabular} & **Accuracy** \\ \hline Proposed work & 500 legitimate, 500 phishing & DT & 0.99 \\ [17] & 100 legitimate, 100 phishing & TF-IDF & 0.95 \\ [24] & 600 legitimate, 800 phishing & Neural network & 0.92 \\ [25] & 24086 legitimate, 16797 phishing & Rule-based & 0.99 \\ [26] & 6950 legitimate, 860 phishing & PILFER & 0.99 \\ [29] & 5000 legitimate, 5000 phishing & Random forest & 0.94 \\ [32] & 995 legitimate, 667 phishing & Multi-layer filters & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the experimental results with earlier works.  
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/sibelkapan/phishing_dataset/](https://github.com/sibelkapan/phishing_dataset/)]([https://github.com/sibelkapan/phishing_dataset/](https://github.com/sibelkapan/phishing_dataset/)).