Abstract
Phishing websites utilise a variety of techniques to imitate the URL address and page content of a legitimate website in order to steal users' personal information. In this study, we analyse the structural properties of the phishing website URL, extract 12 different types of data, and train four machine learning algorithms. Then, in order to identify unknown URLs, utilise the method that performed the best as our model. The recommendation of the original regular web page of the phishing web page is implemented after a snapshot of the web page is extracted and compared with the regular web page snapshot.
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