Multi-scale semantic deep fusion models for phishing website detection

In view of semantic counterfeiting characteristics of phishing websites and their multi-scale composition, this paper fully considers the semantic information of different scales, and proposes three semantic-based phishing detection models at different depths using various deep learning methods. The proposed three models are Multi-scale Data-layer Fusion (MDF) model, Multi-scale Feature-layer Fusion (MFF) model and Multi-scale In-depth Fusion(MIF) model. Experimental results on a constructed complex dataset show that the three models all have good recognition capabilities and the MIF model achieves the best performance on a complex dataset, with an F1-Measure of 0.9830, AUC value of 0.9993 and a false positive rate of 0.0047. Then with further comparison with both visual and text methods and an active discovery experiment lasting for 6 months with 3016 phishing websites detected in the real network environment, it is found that the proposed model is both competitive and practical for real detection scenarios.