Associative Classification Techniques for Predicting e-Banking Phishing Website
Classification Data Mining (DM) Techniques can be a very useful tool in detecting and identifying e-banking phishing websites. In this paper, the authors present a novel approach to overcome the difficulty and complexity in detecting and predicting e-banking phishing website. They proposed an intelligent resilient and effective model that is based on using association and classification Data Mining algorithms. These algorithms were used to characterize and identify all the factors and rules in order to classify the phishing website and the relationship that correlate them with each other. They implemented six different classification algorithm and techniques to extract the phishing training data sets criteria to classify their legitimacy.