Fine-grained Android Malware Detection based on Deep Learning

被引：0

作者：

Li, Dongfang ^{[1
,3
]}

Wang, Zhaoguo ^{[2
,3
]}

Xue, Yibo ^{[1
,3
]}

机构：

[1] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China

[2] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin, Heilongjiang, Peoples R China

[3] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Tech, Beijing, Peoples R China

来源：

2018 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY (CNS) | 2018年

关键词：

Android Malware Detection; Deep Neural Network; Fine-grained Classification; Smartphones Security;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Android smartphone users have been suffering from the security problems these years. There is a serious threat to the network security and privacy brought by the mobile malware. In this paper, we use the deep-learning-based method to detect Android malware and implement an automatic detection engine to detect the families of malicious applications. The results of the evaluation show that the engine can detect 97% of the malware at 0.1% false positive rate (FPR) when detecting the fine-grained malware families.

引用

页数：2

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