[1] Hannousse, A., & Yahiouche, S. (2021). Towards benchmark datasets for machine learning based website phishing detection: An experimental study. Engineering Applications of Artificial Intelligence, 104, 104347.
[2] Anti-Phishing Working Group. (2006). Phishing Activity Trends Report-May, 2006. http://www. anti-phishing. org/reports/apwg_report_May2006. pdf.
[4] Safi, A., & Singh, S. (2023). A Systematic Literature Review on Phishing Website Detection Techniques. Journal of King Saud University-Computer and Information Sciences.
[5] Yang, L., Zhang, J., Wang, X., Li, Z., Li, Z., & He, Y. (2021). An improved ELM-based and data preprocessing integrated approach for phishing detection considering comprehensive features. Expert Systems with Applications, 165, 113863.
[6] Jain, A. K., & Gupta, B. B. (2018). PHISH-SAFE: URL features-based phishing detection system using machine learning. In Cyber Security: Proceedings of CSI 2015 (pp. 467-474). Springer Singapore.
[7] Jain, A. K., & Gupta, B. B. (2018). Two-level authentication approach to protect from phishing attacks in real time. Journal of Ambient Intelligence and Humanized Computing, 9, 1783-1796.
[8] Sindhu, S., Patil, S. P., Sreevalsan, A., Rahman, F., & AN, M. S. (2020, October). Phishing detection using random forest, SVM and neural network with backpropagation. In 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) (pp. 391-394). IEEE.
[9] Zhu, E., Ju, Y., Chen, Z., Liu, F., Fang, X. (2020). DTOF-ANN: an artificial neural network phishing detection model based on decision tree and optimal features. Appl. Soft Comput. J. 95,. https://doi.org/10.1016/j.asoc.2020.106505 106505.
[10] Alkawaz, M. H., Steven, S. J., Hajamydeen, A. I., & Ramli, R. (2021, April). A comprehensive survey on identification and analysis of phishing website based on machine learning methods. In 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 82-87). IEEE.
[11] Basit, A., Zafar, M., Javed, A. R., & Jalil, Z. (2020, November). A novel ensemble machine learning method to detect phishing attack. In 2020 IEEE 23rd International Multitopic Conference (INMIC) (pp. 1-5). IEEE.