Automatic Classification for Vulnerability Based on Machine Learning

被引：0
|
作者：
Shuai, Bo [1 ]
Li, Haifeng [1 ]
Li, Mengjun [1 ]
Zhang, Quan [1 ]
Tang, Chaojing [1 ]
机构：
[1] Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China
来源：
2013 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) | 2013年
关键词：
Vulnerability classification; Words location; Vulnerability distribution; LDA model; SVM;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to solve the problems of traditional machine learning methods for automatic classification of vulnerability, this paper presents a novel machine learning method based on LDA model and SVM. Firstly, word location information is introduced into LDA model called WL-LDA (Weighted Location LDA), which could acquire better effect through generating vector space on themes other than on words. Secondly, a multi-class classifier called HT-SVM (Huffman Tree SVM) is constructed, which could make a faster and more stable classification by making good use of the prior knowledge about distribution of the number of vulnerabilities. Experiments show that the method could obtain higher classification accuracy and efficiency.
引用
收藏
页码：312 / 318
页数：7
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