An efficient method for detecting phishing websites using data mining on web pages

Phishing is regarded as a kind of internet attack on the web which aimed to steal the users’ personal information for online stealing. Phishing plays a negative role in reducing the trust among the users in the business network based on the E-commerce framework. therefore, in this research, we tried to detect phishing websites using data mining. The detection of the outstanding features of phishing is regarded as one of the important prerequisites in designing an accurate detection system. Therefore, in order to detect phishing features, a list of 30 features suggested by phishing websites was first prepared. A new idea based on two steps: feature selection and feature extraction, has been proposed. To evaluate the proposed method, the performance of decision tree J48, random forest, naïve Bayes methods were evaluated on the reduced features. The results indicated that accuracy of the model created to determine the phishing websites by using the two-stage feature reduction-based Wrapper and Principal Component Analysis (PCA) algorithm in the random forest method of 96.58%, which is a desirable outcome compared to other methods.