An Effective Strategy for Identifying Phishing Websites using Class-Based Approach

This paper presents a novel approach to overcome the difficulty and complexity in detecting and predicting social networking phishing website. We proposed an intelligent resilient and effective model that is based on using A New Class Based Associative Classification Algorithm which is an advanced and efficient approach than all other association and classification Data Mining algorithms. This algorithm is 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. Applying the association rule into classification can improve the accuracy and obtain some valuable rules and information that cannot be captured by other classification approaches. The class label is taken good advantage in the rule mining step so as to cut down the searching space. The proposed algorithm also synchronize the rule generation and classifier building phases, shrinking the rule mining space when building the classifier to help speed up the rule generation.