【目的】充分利用多源網絡評估數據和URL異常特征數據,研究提高釣魚網站識別準確性的可行性方案。【方法】采用8種機器學習技術,對比研究網絡評估數據與傳統的URL異常特征數據在釣魚網站識別中的性能,并融合兩類數據研究進一步提高釣魚網站識別準確性的可行性方案。【結果】在釣魚網站識別中,相比于傳統的URL異常特征,利用網絡評估數據可以取得更好的識別效果。融合兩類數據對于提高識別準確性有一定幫助。【局限】未考慮釣魚網站與正常網站的數量存在嚴重的不均衡問題。【結論】充分利用多源網絡評估數據和URL異常特征數據識別釣魚網站的方法是比較合理和有效的,對后續相關研究具有一定的借鑒意義。

[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.