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Subject: Computer Science (provided by Thomson Reuters)
Title:
基于LDA主题模型的安全漏洞分类
Alternative Title: national security vulnerability database classification based on an lda topic model
Author: 廖晓锋 ; 王永吉 ; 范修斌 ; 吴敬征
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: 清华大学学报(自然科学版)
Issued Date: 2012
Volume: 52, Issue:10, Pages:1351-1355
Indexed Type: CNKI ; CSCD
Department: 南昌大学信息工程学院;中国科学院软件研究所基础软件国家工程研究中心;
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
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Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/15338
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
廖晓锋,王永吉,范修斌,等. 基于LDA主题模型的安全漏洞分类[J]. 清华大学学报(自然科学版),2012-01-01,52(10):1351-1355.
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