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Performance Evaluation of Classification Techniques for Phishing Attack Detection

Phishing attack has been a concerning threat for security experts over the years. The rapid increase and advancement of phishing methods produce a vast challenge in the field of web security. Although several research works has already done and various security mechanisms has been implemented in this field but still people are becoming victim of this attack. Therefore, still there is a need of some productive techniques which can prevent phishing attacks. Broadly Phishing attack exists into two forms first is in the form of phishing emails and secondly in the form of phishing websites. This paper evaluates and studies various classification algorithm performances for the detection of phishing websites. Experimental work is carried out using the data set of phishing websites from UCI Machine Learning Repository. Performance comparison among different classification algorithms is done by using Weka 3.8. tool over the dataset.