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Vulnerability Detection With Graph Attention Network And Metric Learning.pdf (779.76 kB)
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Vulnerability Detection With Graph Attention Network And Metric Learning

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posted on 18.05.2022, 22:18 authored by Chunyong ZhangChunyong Zhang, Bin Liu, Qi Fan, Yang Xin, Hongliang Zhu
Static code vulnerability detection is a critical topic in software security. Existing software analysis methods have a high rate of false positives and false negatives. Researchers are interested in employing deep learning to discover vulnerabilities automatically, thanks to the recent success of deep learning algorithms in other application domains.This paper aims at the problem of insufficient and effective extraction of syntax and semantics, the issue of data imbalance, and the problem of overlapping feature distributions between vulnerable and non-vulnerable samples. We illustrate how to create models in a more principled way. We build GSM, a systematic vulnerability detection model based on Graph Attention Network, Sampling methods, and Metric Learning, one phase for one problem solution. When compared to the state-of-the-art approaches, our method achieves 11.5%, 12.3%, 12.57%, and 7.90% improvement in Precision, Recall, F1-Score, and AUC, respectively. Finally, based on the methods proposed in each stage of this paper, we put forward directions and suggestions for more efficient vulnerability detection tasks in the following research.

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Email Address of Submitting Author

chertish_xxd@bupt.edu.cn

ORCID of Submitting Author

0000-0002-7372-1760

Submitting Author's Institution

the School of Cyberspace Security, Beijing University of Posts and Telecommunications

Submitting Author's Country

China

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