Malware Detection on Android Smartphones using API Class and Machine Learning

被引：0
|
作者：
Westyarian [1 ]
Rosmansyah, Yusep [1 ]
Dabarsyah, Budiman [1 ]
机构：
[1] Inst Teknol Bandung, Sch Elect Engn & Informat, Dept Elect Engn, Jl Ganeca 10, Bandung 40132, Indonesia
来源：
5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATICS 2015 | 2015年
关键词：
Android; Malware detection; APIs class; Machine Learning;
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：
This paper proposes a (new) method to detect malware in Android smartphones using API (application programming interface) classes. We use machine learning to classify whether an application is benign or malware. Furthermore, we compare classification precision rate from machine learning. This research uses 51 APIs package classes from 16 APIs classes and employs cross validation and percentage split test to classify benign and malware using Random Forest, J48, and Support Vector Machine algorithms. We use 412 total application samples (205 benign, 207 malware). We obtain that the classification precision average is 91.9%.
引用
收藏
页码：294 / 297
页数：4
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