Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection

The problem of Web phishing attacks has grown considerably in recent years and phishing is considered as one of the most dangerous Web crimes, which may cause tremendous and negative effects on online business. In a Web phishing attack, the phisher creates a forged or phishing website to deceive Web users in order to obtain their sensitive financial and personal information. Several conventional techniques for detecting phishing website have been suggested to cope with this problem. However, detecting phishing websites is a challenging task, as most of these techniques are not able to make an accurate decision dynamically as to whether the new website is phishing or legitimate. This paper presents a methodology for phishing website detection based on machine learning classifiers with a wrapper features selection method. In this paper, some common supervised machine learning techniques are applied with effective and significant features selected using the wrapper features selection approach to accurately detect phishing websites. The experimental results demonstrated that the performance of the machine learning classifiers was improved by using the wrapper-based features selection. Moreover, the machine learning classifiers with the wrapper-based features selection outperformed the machine learning classifiers with other features selection methods.