Permission-Based Malware Detection System for Android Using Machine Learning Techniques

被引：25

作者：

Arslan, Recep Sinan ^{[1
]}

Dogru, Ibrahim Alper ^{[1
]}

Barisci, Necaattin ^{[1
]}

机构：

[1] Gazi Univ, Dept Comp Engn, TR-06560 Ankara, Turkey

来源：

INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING | 2019年 / 29卷 / 01期

关键词：

Android; permission-based; security; risk assessment; malware detection; MOBILE; SECURITY; VERIFICATION; THREATS;

D O I：

10.1142/S0218194019500037

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Mobile applications create their own security and privacy models through permission-based models. Some applications may request extra permissions that they do not need but may use for suspicious activities. The aim of this study is to identify those spare permissions requested and use this information in the security and privacy approach, which uses static and code analysis together and applies them to the existing datasets; then the results are compared and accuracy level is determined. Classification is made with an accuracy rate of 91.95%.

引用

页码：43 / 61

页数：19

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