The growing amount of phishing outbreaks is one of the main concern for security scientists nowadays. Signature-based approaches are used in the traditional methods for categorizing phishing websites. Such tools and techniques fail with advanced and complex phishing webpage. Consequently, learning-based algorithms are widely adopted in many industries. Such algorithms can be applied to analyze phishing websites. The detection accuracy can be increased with a large dataset and complex features. This chapter addresses the problem of analyzing such phishing activities on web pages. Explicitly, we propose a machine learning-based framework with various supervised learning algorithms such as Random Forest, Support Vector Machine, and Decision Trees. We perform the hyperparameter optimization of these algorithms using sci-kit-learn machine learning frameworks. In the end, we discussed the applied implementation of classifying the phishing attack on the real-world dataset available on Kaggle.
