Phishing Websites Detection Using Data Mining Classification Model

Phishing is a significant security threat to the Internet; it is an electronic online identity theft in which the attackers use spoofing techniques like fake websites that mimic legal websites to trick users into revealing their private information. Many of successful phishing attacks do exist and subsequently a considerable number of anti-phishing methods have been proposed. However, they vary in terms of their accuracy and error rate. This paper proposes an algorithm for phishing websites detection using data mining classification model. It is implemented and experimented using a dataset composed of 20 different webpage features and 1,000 instances. The experimental results showed that the proposed algorithm outperforms the original one in terms of the number of classification rules, accuracy (87%) and less error rate (0.1 %).