PhishFusionNet: A Wide and Deep Phishing Detection with a Hybrid Learning Approach

Phishing is a type of cyber threat that targets organizations and individuals worldwide, causing billions of dollars in losses. So far, most successful anti-phishing methods require experts to extract features from phishing sites and third-party detection systems to detect them. This paper presents a PhishFusionNet model, an effective wide-and-deep learning framework for identifying phishing URLs with a high degree of generalization and accuracy. The proposed model successfully discovers both sequential and global patterns within URLs. This is achieved by integrating character-level embeddings that represent the deep component with handcrafted URL features that capture the wide component. We have tested our proposed model on over six million real-world labelled URLs. The results of the test on such a large-scale dataset with an optimal accuracy of 98.9 % have demonstrated that our model outperforms many other tested approaches. Based on these results, we believe that our proposed model is an effective, reliable, and scalable solution for cybersecurity and real-time phishing-detection applications.