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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2311.12372 |
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| _version_ | 1866910887245250560 |
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| author | Liu, Ruitong Wang, Yanbin Xu, Haitao Qin, Zhan Zhang, Fan Liu, Yiwei Cao, Zheng |
| author_facet | Liu, Ruitong Wang, Yanbin Xu, Haitao Qin, Zhan Zhang, Fan Liu, Yiwei Cao, Zheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_12372 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PMANet: Malicious URL detection via post-trained language model guided multi-level feature attention network Liu, Ruitong Wang, Yanbin Xu, Haitao Qin, Zhan Zhang, Fan Liu, Yiwei Cao, Zheng Cryptography and Security 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. |
| title | PMANet: Malicious URL detection via post-trained language model guided multi-level feature attention network |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2311.12372 |