Machine Learning to Identify Android Malware

被引：0
|
作者：
Tam, Geran [1 ]
Hunter, Aaron [1 ]
机构：
[1] BC Inst Technol, Burnaby, BC, Canada
来源：
2018 9TH IEEE ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON) | 2018年
关键词：
Security; malware; machine learning;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper investigates the use of machine learning based classification for the detection of Android malware. A dataset of benign Android applications and malware was formed, utilizing only apps that have appeared in recent years. Three machine learning classifiers were then applied to the joint data set, and the results were reviewed for accuracy. The purpose of this study is to evaluate the utility of these classifiers on new Android malware that may have implemented evasion techniques that make them more difficult to detect.
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页数：4
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