Automatic software vulnerability assessment by extracting vulnerability elements

被引:7
作者
Sun, Xiaobing [1 ,2 ]
Ye, Zhenlei [1 ]
Bo, Lili [1 ,2 ,3 ]
Wu, Xiaoxue [1 ,2 ]
Wei, Ying [1 ]
Zhang, Tao [4 ]
Li, Bin [1 ,2 ]
机构
[1] Yangzhou Univ, Sch Informat Engn, Yangzhou, Peoples R China
[2] Yangzhou Univ, Jiangsu Prov Engn Res Ctr Knowledge Management & I, Yangzhou, Peoples R China
[3] Nanjing Univ Aeronaut & Astronaut, Key Lab Safety Crit Software, Minist Ind & Informat Technol, Nanjing, Peoples R China
[4] Macau Univ Sci & Technol MUST, Sch Comp Sci & Engn, Macau, Peoples R China
基金
中国国家自然科学基金;
关键词
Vulnerability assessment; Deep learning; Multi-class classification; Mining software repositories; CLASSIFICATION;
D O I
10.1016/j.jss.2023.111790
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Software vulnerabilities take threats to software security. When faced with multiple software vulnerabilities, the most urgent ones need to be fixed first. Therefore, it is critical to assess the severity of vulnerabilities in advance. However, increasing number of vulnerability descriptions do not use templates, which reduces the performance of the existing software vulnerability assessment approaches. In this paper, we propose an automated vulnerability assessment approach that using vulnerability elements for predicting the severity of six vulnerability metrics (i.e., Access Vector, Access Complexity, Authentication, Confidentiality Impact, Integrity Impact and Availability Impact). First, we use BERT-MRC to extract vulnerability elements from vulnerability descriptions. Second, we assess six metrics using vulnerability elements instead of full descriptions. We conducted experiments on our manually labeled dataset. The experimental results show that our approach has an improvement of 12.03%, 14.37%, and 38.65% on Accuracy over three baselines.& COPY; 2023 Elsevier Inc. All rights reserved.
引用
收藏
页数:13
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