Identifying Phishing Websites Based on URL Multi-Granularity Feature Fusion
Hu Zhongyi(),Zhang Shuoguo,Wu Jiang
School of Information Management, Wuhan University, Wuhan 430072, China The Center for Electronic Commerce Research and Development, Wuhan University, Wuhan 430072, China
[Objective] This study proposes a model based on URL multi-granularity feature fusion, aiming to more effectively identify phishing websites. [Methods] First, we retrieved the character-level and word-level features of URLs with one-hot encoding and BERT. Then, we constructed the new identification model by fusing the deep features of both granularities. [Results] The accuracy, recall, F-value, and AUC values of the proposed model reached 0.96, 0.98, 0.97, and 0.97, respectively. It had better performance than the single-granularity feature representation-based models, benchmark classifiers, and other popular models. [Limitations] More research is needed to include webpage contents to the model. [Conclusions] The proposed model can represent URL features more comprehensively, and effectively identify phishing websites.
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