# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 5 Conclusions and Future Works  
In conclusion, many different DL algorithms exist, which numerous researchers in previous studies have implemented to detect phishing websites. However, choosing the right approach best suited for a specific application or dataset is a challenging task. To solve this problem, an empirical study was conducted in this paper, based on some of the most frequently-used DL techniques, such as DNN, CNN, LSTM, and GRU. Different neural network architectures were tested for each of these DL algorithms to find the optimal set of parameter settings that produce the highest performance accuracy. The empirical experiments were performed on the UCI dataset, consisting of 11055 phishing and benign URLs with 30 website features. Various performance metrics were measured to evaluate the effectiveness and the feasibility of the DL-based phishing detection model. The results obtained from the experiments indicated that among the four DL techniques, there was no single algorithm that produced the best measures in all performance metrics. Researchers and developers need to select the best suited to their particular applications or according to specific requirements. They can also combine different DL algorithms in a hybrid or ensemble model to join their advantages and cure their disadvantages. As part of our future work, we plan to experiment with other DL algorithms that are relatively new and have not been fully explored in the phishing detection domain, such as Autoencoder (AE), Generative Adversarial Network (GAN), or Deep Reinforcement Learning (DRL). In addition to homogeneous EDL models, we will also implement heterogeneous EDL architectures by integrating multiple DL algorithms of different genres. Plus, we plan to use a more significant and unbalanced dataset in the experiment set up to reflect real-life scenarios, as we live in a big data era and phishing is an imbalanced classification problem, where the number of phishing URLs is much smaller than legitimate URLs. Conceptualization, N.Q.D. and A.S.; methodology, N.Q.D. and A.S.; software, N.Q.D. and A.S.; validation, N.Q.D. and A.S.; formal analysis, N.Q.D. and A.S.; investigation, N.Q.D. and A.S.; resources, N.Q.D. and A.S.; data curation, N.Q.D. and A.S.; writing--original draft preparation, N.Q.D. ; writing--review and editing, N.Q.D., A.S., O.K., T.Y. and H.F.; visualization, N.Q.D. ; supervision, A.S.; project administration, A.S.; funding acquisition, A.S. and O.K. All authors have read and agreed to the published version of the manuscript. This work was supported / funded by the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1). The authors sincerely thank Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876, for the completion of the research. Faculty of Informatics and Management, University of Hradec Kralove, SPEV project Grant Number: 2102/2021. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.kaggle.com/isatish/phishing-dataset-uci-ml-csv](https://www.kaggle.com/isatish/phishing-dataset-uci-ml-csv)]([https://www.kaggle.com/isatish/phishing-dataset-uci-ml-csv](https://www.kaggle.com/isatish/phishing-dataset-uci-ml-csv)) (accessed on 5 May 2021). **Acknowledgments:** We are grateful for the support of [PERSON] and [PERSON] in consultations regarding application aspects from Hradec Kralove University, Czech Republic. **Conflicts of Interest:** The authors declare no conflict of interest. **Appendix A**