PMANet: Malicious URL detection via post-trained language model guided multi-level feature attention network

• URL: http://arxiv.org/abs/2311.12372v2
• Date: Fri, 21 Mar 2025 12:26:20 GMT
• Title: PMANet: Malicious URL detection via post-trained language model guided multi-level feature attention network
• Authors: Ruitong Liu, Yanbin Wang, Haitao Xu, Zhan Qin, Fan Zhang, Yiwei Liu, Zheng Cao,
• Abstract summary: We propose PMANet, a pre-trained Language Model-Guided multi-level feature attention network.<n>PMANet employs a post-training process with three self-supervised objectives: masked language modeling, noisy language modeling, and domain discrimination.<n> Experiments on diverse scenarios, including small-scale data, class imbalance, and adversarial attacks, demonstrate PMANet's superiority over state-of-the-art models.
• Score: 16.73322002436809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of malicious URLs has made their detection crucial for enhancing network security. While pre-trained language models offer promise, existing methods struggle with domain-specific adaptability, character-level information, and local-global encoding integration. To address these challenges, we propose PMANet, a pre-trained Language Model-Guided multi-level feature attention network. PMANet employs a post-training process with three self-supervised objectives: masked language modeling, noisy language modeling, and domain discrimination, effectively capturing subword and character-level information. It also includes a hierarchical representation module and a dynamic layer-wise attention mechanism for extracting features from low to high levels. Additionally, spatial pyramid pooling integrates local and global features. Experiments on diverse scenarios, including small-scale data, class imbalance, and adversarial attacks, demonstrate PMANet's superiority over state-of-the-art models, achieving a 0.9941 AUC and correctly detecting all 20 malicious URLs in a case study. Code and data are available at https://github.com/Alixyvtte/Malicious-URL-Detection-PMANet.

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