Consensus and majority vote feature selection methods and a detection technique for web phishing
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s12652-020-02054-3.pdf
Reference33 articles.
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3. Basnet RB, Sung AH, Liu Q (2012) Feature selection for improved phishing detection. In: International Conference on Industrial. Springer, Engineering and Other Applications of Applied Intelligent Systems, pp 252–261
4. Chiew KL, Tan CL, Wong K, Yong KS, Tiong WK (2019) A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Inf Sci 484:153–166
5. Feng F, Zhou Q, Shen Z et al (2018) The application of a novel neural network in the detection of phishing websites. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-018-0786-3
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