计算机科学
编码器
源代码
编码(集合论)
脆弱性(计算)
人工智能
深度学习
机器学习
F1得分
程序设计语言
操作系统
计算机安全
集合(抽象数据类型)
作者
Soolin Kim,Jusop Choi,Muhammad Ejaz Ahmed,Surya Nepal,Hyoungshick Kim
标识
DOI:10.1109/issrew55968.2022.00042
摘要
Deep learning technologies recently received much attention to detect vulnerable code patterns accurately. This paper proposes a new deep learning-based vulnerability detection tool dubbed VulDeBERT by fine-tuning a pre-trained language model, Bidirectional Encoder Representations from Transformers (BERT), on the vulnerable code dataset. To support VulDeBERT, we develop a new code analysis tool to extract well-represented abstract code fragments from C and C++ source code. The experimental results show that VulDeBERT outperforms the state-of-the-art tool, VulDeePecker [1] for two security vul- nerability types (CWE-119 and CWE-399). For the CWE-119 dataset, VulDeBERT achieved an Fl score of 94.6 %, which is significantly better than VulDeePecker, achieving an Fl score of 86.6 % in the same settings. Again, for the CWE-399 dataset, VulDeBERT achieved an Fl score of 97.9 %, which is also better than VulDeePecker, achieving an Fl score of 95 % in the same settings.
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