Feature Selection Approach for Phishing Detection

This study offers a novel approach to the feature selection. We propose a method to reduce the number of features while maintaining high detection accuracy by combining two popular feature importance methods and the GRiS mean method. The proposed model also improves the training time, test time, and usage of memory. Among the Ill features of the latest published phishing website detection dataset, the efficient features were selected with this approach. Using the Random Forest algorithm and the proposed feature selection approach with only nineteen features, the accuracy of phishing detection can reach up to 97.12%.