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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

BBVD: A BERT-based Method for Vulnerability Detection

Author 1: Weichang Huang Author 2: Shuyuan Lin Author 3: Chen Li
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 13, No. 12 · Published 2022 · Cited by 16

DOI: https://doi.org/10.14569/IJACSA.2022.01312103

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Abstract

Software vulnerability detection is one of the key tasks in the field of software security. Detecting vulnerability in the source code in advance can effectively prevent malicious attacks. Traditional vulnerability detection methods are often ineffective and inefficient when dealing with large amounts of source code. In this paper, we present the BBVD approach, which treats high-level programming languages as another natural language and uses BERT-based models in the natural language processing domain to automate vulnerability detection. Our experimental results on both SARD and Big-Vul datasets demonstrate the good performance of the proposed BBVD in detecting software vulnerability.

Keywords

Vulnerability detection BERT software security

How to Cite this Article

Huang, W., Lin, S., & Li, C. (2022). BBVD: A BERT-based Method for Vulnerability Detection. International Journal of Advanced Computer Science and Applications, 13(12). https://doi.org/10.14569/IJACSA.2022.01312103

Huang, Weichang, et al.. "BBVD: A BERT-based Method for Vulnerability Detection." International Journal of Advanced Computer Science and Applications, vol. 13, no. 12, 2022, https://doi.org/10.14569/IJACSA.2022.01312103.

@article{Huang2022,
  title     = {BBVD: A BERT-based Method for Vulnerability Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Weichang Huang and Shuyuan Lin and Chen Li},
  doi       = {10.14569/IJACSA.2022.01312103},
  url       = {https://doi.org/10.14569/IJACSA.2022.01312103}
}

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