As a new form of malicious software phishing websites appear frequently in recent years which cause great harm to online financial services and data security. In this paper we design and implement an intelligent model for detecting phishing websites. In this model we extract 10 different types of features such as title keyword and link text information to represent the website. Heterogeneous classifiers are then built based on these different features. We propose a principled ensemble classification algorithm to combine the predicted results from different phishing detection classifiers. Hierarchical clustering technique has been employed for automatic phishing categorization. Case studies on large and real daily phishing websites collected from Kingsoft Internet Security Lab demonstrate that our proposed model outperforms other commonly used anti-phishing methods and tools in phishing website detection.
Random forest-SVM-naïve bayes