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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

BBVD: A BERT-based Method for Vulnerability Detection

Author 1: Weichang Huang
Author 2: Shuyuan Lin
Author 3: Chen Li

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.01312103

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 12, 2022.

  • Abstract and Keywords
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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

Weichang Huang, Shuyuan Lin and Chen Li, “BBVD: A BERT-based Method for Vulnerability Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.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},
doi = {10.14569/IJACSA.2022.01312103},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01312103},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Weichang Huang and Shuyuan Lin and Chen Li}
}


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