Anti Phishing Browser Extension

Abstract: The problem of Web phishing attacks has grown considerably in recent years and phishing is considered as one of the most popular web crimes. In a Web phishing attack, the phisher creates a forged or phishing website to deceive Web users in order to achieve their sensitive financial and personal information. Several tralatitious techniques for detecting phishing website have been suggested to cope with this problem. However, detecting phishing websites is a defiance task, as most of these techniques are not capable of correct decision dynamically as to whether the new website is phishing or legitimate. This paper presents a technique for phishing website detection based on machine learning classifiers with a wrapper features selection method. In this paper, some casual supervised machine learning approaches are applied with effective and significant features selected by the wrapper features selection approach to accurately detect phishing websites. The experimental results confirmed that the performance of the machine learning classifiers had improved by using the wrapper-based features selection. Moreover, the machine learning classifiers with the wrapper-based features selection outruned the machine learning classifiers with other features selection methods. Keywords: Introduction, Proposed System, Techniques for phishing websites detection, Supervised Machine Learning