Static Analysis of Android Malware Detection using Deep Learning

被引：0

作者：

Sandeep, H. R. ^{[1
]}

机构：

[1] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Comp Sci & Engn, Bengaluru, India

来源：

PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICCS) | 2019年

关键词：

Android Malware Classification; Malware; Machine learning; Security; Android; Permissions; APK (application package); Deep learning; Data Mining; Data Extraction; Preprocessing; Vector Representation; Behavioral Analysis; Keras; Deep Learning Dense Model; Random Forest Classifier; Virus Share;

D O I：

10.1109/iccs45141.2019.9065765

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Android Malware is very common these days as applications are not created by trusted sources. People enter their personal data, save cards and much more, thinking these apps are going to keep them fit or help remind them to do certain essential works which we tend to forget in this busy routine of life. In such cases, detecting the malware before even installing an application would be of great help to us. It could possibly even stop a few crimes. In this paper, we propose to use the fully connected deep learning model for detection of Android malware. Key features of the proposed work include detection of Android malware even before installation, the name of the Android malware, version packages with proven extremely high accuracy of about 94.65%. This model also learns all features from all combinations of features. It includes extensive research and testing to achieve very high accuracy.

引用

页码：841 / 845

页数：5

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