%A Hu Zhongyi,Wang Chaoqun,Wu Jiang %T Identifying Phishing Websites with Multiple Online Data Sources %0 Journal Article %D 2017 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2017.06.05 %P 47-55 %V 1 %N 6 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4396.shtml} %8 2017-06-25 %X

[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.