In cyberspace, phishing is one of several cybercrimes that often target internet users all over the world. Phishing performs by trying to trick the victim into accessing a web page which looks original, then instructing them to send important data. For prevention, it is essential to build a phishing detection system (PDS). Recent phishing detection system based on data mining and machine learning techniques. Development of an effective detection system while minimizing false positives and negatives is still a challenge. Instead of using single classification approach it would be better to use ensemble approach. In this work an ensemble approach is utilized to build a phishing website classification system. Bagging also known as Bootstrap Aggregating is a meta algorithm established to enhance the machine learning algorithms performance. To detect phishing website various classification models have been developed and implemented. It is observed that combination of Bagging, AdaBoost and j48 gives best results that is 97.2% accuracy.

Meta-algorithm, classification, web phishing, website, internet, cyber security

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