Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm-based feature selection and weighting

In recent years, the web phishing attack has become one of the most serious web security problems, in which the phishers can steal significant financial information about the internet users to carry out financial thefts. Several blacklist-based conventional phishing website detection methods are used to predict the phishing websites. However, numerous phishing websites are not predicted precisely by these blacklist-based conventional methods since many new phishing websites are constantly developed and launched on the Web over time. In this study, hybrid intelligent phishing website prediction using deep neural networks (DNNs) with evolutionary algorithm-based feature selection and weighting methods are suggested to enhance the phishing website prediction. In the proposed hybrid intelligent phishing website prediction approaches, the most influential features and the optimal weights of website features are heuristically identified with the genetic algorithm (GA) to help in increasing the accuracy of phishing website prediction. Accordingly, the website features selected and weighted by the GA are utilised to train DNNs to accurately predict the phishing websites. The experimental results demonstrated that the proposed hybrid intelligent phishing website prediction approaches achieved significantly higher classification accuracy, sensitivity, specificity, and geometric mean in phishing website prediction compared to those proposed in other studies.