[Objective] This study aims to identify phishing websites more effectively with the help of online evaluation data and URL abnormal features. [Methods] First, we used eight machine learning techniques to compare the performance of various online evaluation data and URL abnormal features in identifying phishing websites. Then, we proposed a new method to improve the accuracy of the identification procedures. [Results] We found that the evaluation data had better performance than abnormal features of URL. Combining the two data sets could improve the identification performance. [Limitations] We did not consider the difference between the numbers of phishing sites and the good ones. [Conclusions] Online evaluation data and URL abnormal features could help us identify phishing websites effectively, which indicates the direction of future studies.
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