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Title:
national security vulnerability database classification based on an lda topic model
Author: Liao Xiaofeng ; Wang Yongji ; Fan Xiubin ; Wu Jingzheng
Keyword: 漏洞分类 ; 隐含Dirichlet分布(LDA) ; 支持向量机(SVM) ; 中国国家信息安全漏洞库(CNNVD)
Source: Qinghua Daxue Xuebao/Journal of Tsinghua University
Issued Date: 2012
Volume: 52, Issue:10, Pages:1351-1355
Indexed Type: EI ; CNKI
Department: (1) Information Engineering School Nanchang University Nanchang 330029 China; (2) National Fundamental Software Engineering Center Institute of Software The Chinese Academy of Sciences Beijing 100190 China
Sponsorship: 国家重点科技专题“核高基”资助项目(2010ZX01036-001-002)
Abstract: 采用隐含Dirichlet分布主题模型(latent Dirichletallocation,LDA)和支持向量机(support vector machine,SVM)相结合的方法,在主题向量空间构建一个自动漏洞分类器。以中国国家信息安全漏洞库(CNNVD)中漏洞记录为实验数据。实验表明:基于主题向量构建的分类器的分类准确度比直接使用词汇向量构建的分类器有8%的提高。
English Abstract: The current vulnerabilities in China are analyzed using a dataset from the China National Vulnerability Database of Information Security (CNNVD), with a combined latent Dirichlet allocation (LDA) topic model and a support vector machine (SVM) to construct a classifier in the topic vector space. Tests show that the classifier based on topic vectors has about 8% better classification performance than that based on text vectors.
Language: 中文
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/15141
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Liao Xiaofeng,Wang Yongji,Fan Xiubin,et al. national security vulnerability database classification based on an lda topic model[J]. Qinghua Daxue Xuebao/Journal of Tsinghua University,2012-01-01,52(10):1351-1355.
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