Abstract:Phishing is a kind of online fraud that combines social engineering techniques and sophisticated attack vectors to steal the users' sensitive information to achieve the illegal purpose. In order to detect phishing web pages quickly and efficiently, this paper presents a model for depth detection of phishing web pages based on ensemble learning. The model uses page rendering to deal with common page camouflage, extract several sensitive features including URL and domain features, link and reference information, and contents of text messages; and then constructs and trains several base learning models with ensemble learning method using the features above; finally, generates the final result with base models using classification and integration method. Experiments on PhishTank indicate that the detection model this paper proposed has good accuracy and recall rate.