Semantic Scholar Open Access 2022 13 sitasi

BBVD: A BERT-based Method for Vulnerability Detection

Weichang. Huang Shu-Tyng Lin Chen Li

Lihat Sumber DOI

Abstrak

—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 nat- ural 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.

Penulis (3)

Weichang. Huang

Shu-Tyng Lin

Chen Li

Format Sitasi

APA MLA BibTeX

Huang, W., Lin, S., Li, C. (2022). BBVD: A BERT-based Method for Vulnerability Detection. https://doi.org/10.14569/ijacsa.2022.01312103

Akses Cepat

Lihat di Sumber doi.org/10.14569/ijacsa.2022.01312103

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 13×
Sumber Database: Semantic Scholar
DOI: 10.14569/ijacsa.2022.01312103
Akses: Open Access ✓