Phishing Detection Model Integrating URL Characters and HTML Word Semantic Deep Features

Deep learning methods, known for their powerful feature learning and classification capabilities, are widely used in phishing detection. To improve accuracy, this study proposes DPMLF (Deep Learning Phishing Detection Model with Multi-Level Features), which integrates URL character-level and HTML word-level semantic features. DPMLF utilizes character embeddings and parallel convolutional kernels to precisely extract local URL features. For HTML text, it employs word-level embeddings and stacked convolutional layers with dense connections to capture both local and long-range text information. The fully connected layer then fuses these features into a multi-level, fine-grained feature vector for classification. The results of experiments conducted on two public datasets with different scales show that DPMLF is accurate in phishing attack detection.