| CVE漏洞分类框架下的SVM学习模型构建 |
| |
| 引用本文： | 彭华,莫礼平,唐赞玉. CVE漏洞分类框架下的SVM学习模型构建[J]. 吉首大学学报(自然科学版), 2013, 34(4): 62-66. DOI: 10.3969/j.issn.1007-2985.2013.03.014 |
| |
| 作者姓名： | 彭华 莫礼平 唐赞玉 |
| |
| 作者单位： | （吉首大学信息科学与工程学院，湖南 吉首 416000） |
| |
| 基金项目： | 湖南省科技厅科技计划资助项目，湖南省教育厅一般科学研究资助项目 |
| |
| 摘 要： | 在CVE漏洞分类框架中，构建了基于支持向量机的学习模型，实现了根据不同的分类特征对CVE进行分类.
|
| 关 键 词： | 支持向量机（SVM） 公共漏洞和暴露（CVE） 分类特征 分类准确性 |
Construction of a SVM Learning Model in the Categorization Framework for CVE |
| |
| PENG Hua , MO Li-ping , TANG Zan-yu. Construction of a SVM Learning Model in the Categorization Framework for CVE[J]. Journal of Jishou University(Natural Science Edition), 2013, 34(4): 62-66. DOI: 10.3969/j.issn.1007-2985.2013.03.014 |
| |
| Authors: | PENG Hua MO Li-ping TANG Zan-yu |
| |
| Affiliation: | (College of Information Science and Engineering,Jishou University,Jishou,416000,Hunan China) |
| |
| Abstract: | In the categorization framework for CVE,this paper designs and constructs a learning model based on SVM,so that it can categorize the CVE according to the different taxonomic features.In the process of constructing a learning model based on SVM,first of all,the training data is generated according to the different taxonomic features in the several vulnerability databases,then a data fusion and cleansing process are designed to eliminate the inconsistencies of data,and finally the n-fold cross-validation method is used to evaluate the effect of the model.The learning model has been verified to have better performance of CVE classification. |
| |
| Keywords: | support vector machine (SVM) common vulnerabilities and exposures (CVE) taxonomic feature classification accuracy |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《吉首大学学报(自然科学版)》浏览原始摘要信息 |
|
点击此处可从《吉首大学学报(自然科学版)》下载全文 |
