Performance Analysis of Classifying URL Phishing Using Recursive Feature Elimination
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62624-1_4
Reference23 articles.
1. Tang, L., Mahmoud, Q.H.: A Survey of machine learning-based solutions for phishing website detection. Mach. Learn. Knowl. Extr. 3(3), 672–694 (2021). https://doi.org/10.3390/make3030034
2. Khan, S.A., Khan, W., Hussain, A.: Phishing attacks and websites classification using machine learning and multiple datasets (a comparative analysis). In: Huang, D.S., Premaratne, P. (eds.) Intelligent Computing Methodologies: 16th International Conference, ICIC 2020, Bari, Italy, October 2–5, 2020, Proceedings, Part III, pp. 301–313. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60796-8_26
3. Basit, A., Zafar, M., Liu, X., Javed, A.R., Jalil, Z., Kifayat, K.: A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommun. Syst. 76(1), 139–154 (2020). https://doi.org/10.1007/s11235-020-00733-2
4. Yadollahi, M.M., Shoeleh, F., Serkani, E., Madani, A., Gharaee, H.: An adaptive machine learning based approach for phishing detection using hybrid features, In: 2019 5th International Conference on Web Research (ICWR), pp. 281–286. IEEE (2019)
5. Sundararajan, A., Gressel, G., Achuthan, K.: Feature selection for phishing detection with machine learning. Int. J. Eng. Adv. Technol. 8(6s3), 1039–1045 (2019). https://doi.org/10.35940/ijeat.F1331.0986S319
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