.png){kind=icon}
返回
Static vulnerability detection based on class separation☆
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jss.2023.111832.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Software vulnerability detection is a key step to prevent the system from being attacked. However, tens of thousands of codes have brought great challenges to engineers, so we urgently need an automatic and intelligent vulnerability detection method. The existing vulnerability detection model based on deep learning has the problem that it is difficult to separate the features of vulnerable and neutral code. Based on the code data drive, this paper proposes a static vulnerability detection method SDV(Statically Detecting Vulnerability) for C\C++ programs. SDV is a function-level vulnerability code detection method. This paper uses a code property graph to represent the code and decouples the feature extractor and the classifier. In the graph feature extraction stage, we use Jump Graph Attention Network layers and convolutional pooling layers. Their combination can not only prevent the over-smoothing problem but also separate the sample classes deeply. Finally, on the chrdeb dataset, SDV outperforms state-of-the-art function-level vulnerability detection methods by 52.3%, 15.9%, and 39.6% in Precision, Recall, and F1-Score, respectively. On the real project sard, the number of vulnerabilities detected by SDV is 10.7 times more than Reveal. (c) 2023 Elsevier Inc. All rights reserved.
Keyword:
Vulnerability detection
Code property graph
Jump structure
Graph attention network
Class separation
期刊
IF:
4.1
论文数:
5.5K
被引数:
8.4K
机构
引用论文
Long noncoding RNA hypoxia-inducible factor 1 alpha-antisense RNA 1 promotes tumor necrosis factor-α-induced apoptosis through caspase 3 in Kupffer cells.png)
Medicine
OAAI
MedicineIF0
OAAI分享收藏分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Cobalt and Copper Composite Oxides as Efficient Catalysts for Preferential Oxidation of CO in H2-Rich Stream钴和铜复合氧化物作为H2-Rich流中CO优先氧化的有效催化剂
PREAI
分享收藏
查看更多内容