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COCL: An Intelligent Framework for Enhancing Deep Learning-Based Vulnerability Detection
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DOI：10.1109/TII.2023.3329670.png)
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摘要
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Due to the powerful feature extraction capability of deep learning (DL), many recent studies have used it to conduct source code vulnerability analysis. However, although it has a good performance on artificial datasets, it does not perform satisfactorily on the real-world vulnerabilities with higher complexity. In this article, we introduce contrastive curriculum learning into DL-based vulnerability detection to find a suitable boundary to distinguish vulnerabilities from normal codes. Contrastive learning can be used to reduce the difference between different vulnerabilities while amplifying the difference between vulnerabilities and normal codes. To make the training phase of contrastive learning more intelligent, we apply curriculum learning to mimic the way humans acquire knowledge, which means that the model will learn simple samples first and then increase the difficulty of training samples. Specifically, we implement an intelligent framework (i.e., contrastive curriculum learning (COCL)) that can enhance the detection effect of existing DL-based vulnerability detectors. To verify the capability of COCL, we select four state-of-the-art DL-based vulnerability detectors (i.e., AutoVulTC, VulDeePecker, BenchSG, and Devign) as our base models. The experimental results show that using COCL can bring an improvement of 8.1% to the F1 scores of these models on a real-world vulnerability dataset.
Keyword:
Contrastive learning
curriculum learning (CL)
deep learning (DL)
vulnerability detection
期刊
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9.9
论文数:
8.6K
被引数:
6.0W
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