A Semantic-Aware Approach to Phishing URL Detection

Received: 7 July 2025 | Revised: 23 August 2025 | Accepted: 2 September 2025 | Online: 8 December 2025

Corresponding author: Hoang Thanh Nguyen

Abstract

With the rapid growth of online transactions, phishing attacks have become increasingly unpredictable, particularly those involving malicious Uniform Resource Locators (URLs). Currently, few studies have effectively combined manual and semantic features within a unified framework to both fully utilize the structural information and capture the deep context dependence of the character string. To address this gap, this study proposes a deep attention-based approach for phishing URL detection. Our approach first relies on the importance of manual features and the Bidirectional Encoder Representations from Transformers (BERT) model to extract semantic feature vectors. Subsequently, a hybrid deep learning architecture comprising Bidirectional Long Short-Term Memory (BiLSTM) layers, an attention mechanism, and fully connected dense networks is employed to classify URLs as either phishing or legitimate. Experimental results demonstrate that our proposed model achieves a high classification accuracy of 96.77%, while ablation analysis highlights the individual contributions of key components, including BERT embeddings, attention mechanism, LSTM layers, and feature types, to overall model performance. Finally, by training on a Kaggle benchmark dataset and testing on real-world phishing samples, the study confirms the model's strong generalization capability in detecting emerging phishing threats.