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. •Semantic information at different scales is mined and fused at different depths.•Three semantic-based deep phishing detection models are proposed.•Various comparative experiments are carried out and prove the effectiveness.•A phishing discovery experiment in reality detected 3016 phishing websites.