A New Dataset and Methodology for Malicious URL Classification
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915087090974720 |
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| author | Schvartzman, Ilan Sarussi, Roei Ashkenazi, Maor kringel, Ido Tocker, Yaniv Shohet, Tal Furman |
| author_facet | Schvartzman, Ilan Sarussi, Roei Ashkenazi, Maor kringel, Ido Tocker, Yaniv Shohet, Tal Furman |
| contents | Malicious URL (Uniform Resource Locator) classification is a pivotal aspect of Cybersecurity, offering defense against web-based threats. Despite deep learning's promise in this area, its advancement is hindered by two main challenges: the scarcity of comprehensive, open-source datasets and the limitations of existing models, which either lack real-time capabilities or exhibit suboptimal performance. In order to address these gaps, we introduce a novel, multi-class dataset for malicious URL classification, distinguishing between benign, phishing and malicious URLs, named DeepURLBench. The data has been rigorously cleansed and structured, providing a superior alternative to existing datasets. Notably, the multi-class approach enhances the performance of deep learning models, as compared to a standard binary classification approach. Additionally, we propose improvements to string-based URL classifiers, applying these enhancements to URLNet. Key among these is the integration of DNS-derived features, which enrich the model's capabilities and lead to notable performance gains while preserving real-time runtime efficiency-achieving an effective balance for cybersecurity applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00356 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A New Dataset and Methodology for Malicious URL Classification Schvartzman, Ilan Sarussi, Roei Ashkenazi, Maor kringel, Ido Tocker, Yaniv Shohet, Tal Furman Machine Learning Cryptography and Security Malicious URL (Uniform Resource Locator) classification is a pivotal aspect of Cybersecurity, offering defense against web-based threats. Despite deep learning's promise in this area, its advancement is hindered by two main challenges: the scarcity of comprehensive, open-source datasets and the limitations of existing models, which either lack real-time capabilities or exhibit suboptimal performance. In order to address these gaps, we introduce a novel, multi-class dataset for malicious URL classification, distinguishing between benign, phishing and malicious URLs, named DeepURLBench. The data has been rigorously cleansed and structured, providing a superior alternative to existing datasets. Notably, the multi-class approach enhances the performance of deep learning models, as compared to a standard binary classification approach. Additionally, we propose improvements to string-based URL classifiers, applying these enhancements to URLNet. Key among these is the integration of DNS-derived features, which enrich the model's capabilities and lead to notable performance gains while preserving real-time runtime efficiency-achieving an effective balance for cybersecurity applications. |
| title | A New Dataset and Methodology for Malicious URL Classification |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2501.00356 |