Multiple Classifier Systems for More Accurate Java']JavaScript Malware Detection

被引：0

作者：

Yi, Zibo ^{[1
]}

Ma, Jun ^{[1
]}

Luo, Lei ^{[1
]}

Yu, Jie ^{[1
]}

Wu, Qingbo ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Comp, Changsha, Hunan, Peoples R China

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON PROMOTION OF INFORMATION TECHNOLOGY (ICPIT 2016) | 2016年 / 66卷

关键词：

machine learning; !text type='Java']Java[!/text]Script malware detection; multiple classifier system;

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：

The researches of JavaScript malware detection focus on machine learning techniques in recent years. These works extract features from JavaScript's abstract syntax tree for the training of classifiers and achieve satisfactory detection results. However, in the training set there exist some scripts that are not so representative and may cause occasional incorrect classification. We propose multiple classifier system (MCS) to reduce this kind of misclassification. As shown in the experiments, the accuracy increases because of the MCS while training time is slightly greater than the original classifier.

引用

页码：139 / 143

页数：5

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