Malicious URL Detection via Pretrained Language Model Guided Multi-Level Feature Attention Network

• URL: http://arxiv.org/abs/2311.12372v1
• Date: Tue, 21 Nov 2023 06:23:08 GMT
• Title: Malicious URL Detection via Pretrained Language Model Guided Multi-Level Feature Attention Network
• Authors: Ruitong Liu, Yanbin Wang, Haitao Xu, Zhan Qin, Yiwei Liu, Zheng Cao,
• Abstract summary: We present an efficient pre-training model-based framework for malicious URL detection. We develop three key modules: hierarchical feature extraction, layer-aware attention, and spatial pyramid pooling. The proposed method has been extensively validated on multiple public datasets.
• Score: 15.888763097896339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread use of the Internet has revolutionized information retrieval methods. However, this transformation has also given rise to a significant cybersecurity challenge: the rapid proliferation of malicious URLs, which serve as entry points for a wide range of cyber threats. In this study, we present an efficient pre-training model-based framework for malicious URL detection. Leveraging the subword and character-aware pre-trained model, CharBERT, as our foundation, we further develop three key modules: hierarchical feature extraction, layer-aware attention, and spatial pyramid pooling. The hierarchical feature extraction module follows the pyramid feature learning principle, extracting multi-level URL embeddings from the different Transformer layers of CharBERT. Subsequently, the layer-aware attention module autonomously learns connections among features at various hierarchical levels and allocates varying weight coefficients to each level of features. Finally, the spatial pyramid pooling module performs multiscale downsampling on the weighted multi-level feature pyramid, achieving the capture of local features as well as the aggregation of global features. The proposed method has been extensively validated on multiple public datasets, demonstrating a significant improvement over prior works, with the maximum accuracy gap reaching 8.43% compared to the previous state-of-the-art method. Additionally, we have assessed the model's generalization and robustness in scenarios such as cross-dataset evaluation and adversarial attacks. Finally, we conducted real-world case studies on the active phishing URLs.

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