national security vulnerability database classification based on an lda topic model

ISCAS OpenIR

	national security vulnerability database classification based on an lda topic model
	Liao Xiaofeng; Wang Yongji; Fan Xiubin; Wu Jingzheng
	2012
Source	Qinghua Daxue Xuebao/Journal of Tsinghua University
ISSN	1000-0054
Volume	52 Issue:10 Pages:1351-1355
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.
Indexed Type	EI ; CNKI
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。
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)
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)
Language	中文
Content Type	期刊论文
URI	http://ir.iscas.ac.cn/handle/311060/15141
Collection	中国科学院软件研究所
Recommended Citation GB/T 7714	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,2012,52(10):1351-1355.
APA	Liao Xiaofeng,Wang Yongji,Fan Xiubin,&Wu Jingzheng.(2012).national security vulnerability database classification based on an lda topic model.Qinghua Daxue Xuebao/Journal of Tsinghua University,52(10),1351-1355.
MLA	Liao Xiaofeng,et al."national security vulnerability database classification based on an lda topic model".Qinghua Daxue Xuebao/Journal of Tsinghua University 52.10(2012):1351-1355.

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