Automatically Combining Static Malware Detection Techniques

被引：0

作者：

De Lille, David ^{[1
]}

Coppens, Bart ^{[1
]}

Raman, Daan ^{[2
]}

De Sutter, Bjorn ^{[1
]}

机构：

[1] Univ Ghent, Comp Syst Lab, B-9000 Ghent, Belgium

[2] NVISO CVBA, Brussels, Belgium

来源：

2015 10TH INTERNATIONAL CONFERENCE ON MALICIOUS AND UNWANTED SOFTWARE (MALWARE) | 2015年

关键词：

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Malware detection techniques come in many different flavors, and cover different effectiveness and efficiency trade-offs. This paper evaluates a number of machine learning techniques to combine multiple static Android malware detection techniques using automatically constructed decision trees. We identify the best methods to construct the trees. We demonstrate that those trees classify sample apps better and faster than individual techniques alone.

引用

页码：48 / 55

页数：8

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