References
[1]. Anti-Phishing Working Group. (2016). Phishing activity trends report: 1st Quarter. Retrieved from https://docs. apwg.org/reports/apwg_trends_report_q1_2016.pdf
[2]. Bahnsen, A. C., Bohorquez, E. C., Villegas, S., Vargas, J., & González, F. A. (2017, April). Classifying phishing URLs using recurrent neural networks. In 2017, APWG symposium on electronic crime research (eCrime), (pp. 1-8). IEEE. https://doi.org/10.1109/ECRIME.2017.7945048
[3]. Dhamija, R., Tygar, J. D., & Hearst, M. (2006, April). Why phishing works. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, (pp. 581-590). https://doi.org/10.1145/1124772.1124861
[4]. Dietterich, T. G. (2002, August). Machine learning for sequential data: A review. In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), (pp. 15-30). Heidelberg, Berlin: Springer. https://doi. org/10.1007/3-540-70659-3_2
[5]. Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? The Journal of Machine Learning Research, 15(1), 3133-3181.
[6]. Halgaš, L., Agrafiotis, I., & Nurse, J. R. (2019, August). Catching the Phish: Detecting phishing attacks using recurrent neural networks (RNNs). In International Workshop on Information Security Applications, (pp. 219-233). Springer, Cham.
[7]. Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.
[8]. Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2009, June). Beyond blacklists: Learning to detect malicious web th sites from suspicious URLs. In Proceedings of the 15 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp. 1245-1254). https://doi. org/10.1145/1557019.1557153
[9]. Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2011). Learning to detect malicious urls. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 1-24. https://doi.org/10.1145/1961189.1961202
[10]. Marchal, S., Saari, K., Singh, N., & Asokan, N. (2016, June). Know your phish: Novel techniques for detecting th phishing sites and their targets. In 2016, IEEE 36 International Conference on Distributed Computing Systems (ICDCS), (pp. 323-333). IEEE. https://doi.org/10.1 109/ICDCS.2016.10
[11]. Roopak, S., & Thomas, T. (2014, August). A novel phishing page detection mechanism using html source code comparison and cosine similarity. In 2014, Fourth International Conference on Advances in Computing and Communications, (pp. 167-170). IEEE. https://doi.org/10.1 109/ICACC.2014.47
[12]. Thakur, T., & Verma, R. (2014, December). Catching classical and hijack-based phishing attacks. In International Conference on Information Systems Security, (pp. 318-337). Cham: Springer. https://doi.org/10.1007/ 978-3-319-13841-1_18
[13]. Vargas, J., Bahnsen, A. C., Villegas, S., & Ingevaldson, D. (2016, June). Knowing your enemies: Leveraging data analysis to expose phishing patterns against a major US financial institution. In 2016, APWG Symposium on Electronic Crime Research (eCrime), (pp. 1-10). IEEE. https://doi.org/10.1109/ECRIME.2016.7487942
[14]. Verma, R., & Dyer, K. (2015, March). On the character of phishing URLs: Accurate and robust statistical learning classifiers. In Proceedings of the 5th ACM Conference on Data and Application Security and Privacy, (pp. 111-122). https://doi.org/10.1145/2699026.2699115