Detection of Phishing Websites from URLs Using Hybrid Ensemble-Based Machine Learning Technique
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66965-1_2
Reference11 articles.
1. Ramana, A.V., Rao, K.L., Rao, R.S.: Stop-Phish: an intelligent phishing detection method using feature selection ensemble. Soc. Netw. Anal. Min. 11(1), 110 (2021)
2. Lecture Notes in Computer Science;S Kumar,2018
3. Patil, V., Thakkar, P., Shah, C., Bhat, T., Godse, S.P.: Detection and prevention of phishing websites using a machine learning approach. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–5. IEEE (2018)
4. Sindhu, S., Patil, S.P., Sreevalsan, A., Rahman, F., An, M.S.: 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 (2020)
5. Aljammal, A.H., Taamneh, S., Qawasmeh, A., Salameh, H.B.: Machine learning based phishing attacks detection using multiple datasets. Int. J. Interact. Mob. Technol. 17(05), 71–83 (2023). https://doi.org/10.3991/ijim.v17i05.37575
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