首页|基于图注意力网络的开源软件安全漏洞检测方法

基于图注意力网络的开源软件安全漏洞检测方法

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随着开源软件的普及,软件安全问题日益凸显,传统漏洞检测方法已无法应对如今的复杂病毒漏洞,其检测结果往往存在着一定的局限性,因此提出基于图注意力网络的开源软件安全漏洞检测方法.先构建图注意力网络模型,以源代码检测为切入点,进行图注意漏洞的识别检测.为验证文章所提方法中图注意力网络模型的性能,将文章所提模型与Drebin算法进行比较,结果表明图注意力网络模型能够有效检测出开源软件中安全漏洞的数量,其最大误差值为4个,相较于Drebin算法的安全漏洞检测方法具有更高的准确性和效率.
Security Vulnerability Detection Method of Open-source Software Based on Graph Attention Network
With the popularity of open source software,software security problems are becoming increasingly prominent.The traditional vulnerability detection method has been unable to cope with today's complex virus vulnerabilities,and its detection results often have certain limitations.Therefore,the open source software security vulnerability detection method based on graph attention network is proposed.Firstly,the graph attention network model is constructed,and the source code detection is used as the entry point.Then,the vulnerability feature extraction of the open source software is used to realize the vulnerability identification and detection.In order to verify the performance of the graph attention network model in the proposed method,the model in this paper is compared with the Drebin algorithm.The results show that the graph attention network model can effectively detect the number of vulnerabilities in the open source software,and its maximum error value is 4,which has higher accuracy and efficiency compared with the security vulnerability detection method of Drebin algorithm.

networksecurity vulnerabilityopen source softwaregraph attention

陈静

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兰州博文科技学院电信工程学院,甘肃兰州 730101

网络 安全漏洞 开源软件 图注意力

2024

信息与电脑
北京电子控股有限责任公司

信息与电脑

影响因子:1.143
ISSN:1003-9767
年,卷(期):2024.36(1)
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