Abstract
Phishing has been a widespread issue for many years, claiming countless victims, some of which have not even realized that they fell prey. The sole purpose of phishing is to obtain sensitive information from its victims. There have yet to be a consensus on the best way to detect phishing. In this paper, we analyze web-based phishing detection by using Random Forest. Some important URL features are identified and our study shows that the detection performance with feature selection is improved.
S. Hutchinson and Z. Zhang—equal contribution.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Hutchinson, S., Zhang, Z., Liu, Q. (2018). Detecting Phishing Websites with Random Forest. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_46
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DOI: https://doi.org/10.1007/978-3-030-00557-3_46
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