Android Malware Detection Based on Convolutional Neural Networks

被引：4

作者：

Wang, Zhiqiang ^{[1
,2
,3
]}

Li, Gefei ^{[1
]}

Chi, Yaping ^{[1
]}

Zhang, Jianyi ^{[1
]}

Yang, Tao ^{[3
]}

Liu, Qixu ^{[4
]}

机构：

[1] Beijing Elect Sci & Technol Inst, Beijing, Peoples R China

[2] Minist Publ Secur, State Informat Ctr, Beijing, Peoples R China

[3] Minist Publ Secur, Key Lab Informat Network Secur, Beijing, Peoples R China

[4] Chinese Acad Sci, Inst Informat Engn, Key Lab Network Assessment Technol, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE2019) | 2019年

关键词：

Deep learning; Malware detection; Android Static Analysis;

D O I：

10.1145/3331453.3361306

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Due to the open source and fragmentation of the Android system, its security is increasingly challenged. Currently, Android malware detection has certain deficiencies in large-scale and automation detection. In this paper, we proposed an Android malware detection framework based on Convolutional Neural Network (CNN). We used static analysis tools and python scripts to automatically extract 1003 static features, and transformed the features of each sample into a two-dimensional matrix as input to the CNN model. We selected 5000 malicious samples and 5000 benign samples for verification. The experimental results show that the detection accuracy of CNN reaches 99.68%, which is much higher than other algorithms.

引用

页数：6

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