Automated Vulnerability Detection Using Deep Learning Technique
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915467006836736 |
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| author | Yang, Guan-Yan Ko, Yi-Heng Wang, Farn Yeh, Kuo-Hui Chang, Haw-Shiang Chen, Hsueh-Yi |
| author_facet | Yang, Guan-Yan Ko, Yi-Heng Wang, Farn Yeh, Kuo-Hui Chang, Haw-Shiang Chen, Hsueh-Yi |
| contents | Our work explores the utilization of deep learning, specifically leveraging the CodeBERT model, to enhance code security testing for Python applications by detecting SQL injection vulnerabilities. Unlike traditional security testing methods that may be slow and error-prone, our approach transforms source code into vector representations and trains a Long Short-Term Memory (LSTM) model to identify vulnerable patterns. When compared with existing static application security testing (SAST) tools, our model displays superior performance, achieving higher precision, recall, and F1-score. The study demonstrates that deep learning techniques, particularly with CodeBERT's advanced contextual understanding, can significantly improve vulnerability detection, presenting a scalable methodology applicable to various programming languages and vulnerability types. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21968 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Vulnerability Detection Using Deep Learning Technique Yang, Guan-Yan Ko, Yi-Heng Wang, Farn Yeh, Kuo-Hui Chang, Haw-Shiang Chen, Hsueh-Yi Cryptography and Security Artificial Intelligence Software Engineering D.2.4; D.2.5 Our work explores the utilization of deep learning, specifically leveraging the CodeBERT model, to enhance code security testing for Python applications by detecting SQL injection vulnerabilities. Unlike traditional security testing methods that may be slow and error-prone, our approach transforms source code into vector representations and trains a Long Short-Term Memory (LSTM) model to identify vulnerable patterns. When compared with existing static application security testing (SAST) tools, our model displays superior performance, achieving higher precision, recall, and F1-score. The study demonstrates that deep learning techniques, particularly with CodeBERT's advanced contextual understanding, can significantly improve vulnerability detection, presenting a scalable methodology applicable to various programming languages and vulnerability types. |
| title | Automated Vulnerability Detection Using Deep Learning Technique |
| topic | Cryptography and Security Artificial Intelligence Software Engineering D.2.4; D.2.5 |
| url | https://arxiv.org/abs/2410.21968 |