Android malware detection method based on deep neural network

Android is increasingly facing the threat of malware attacks.It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine,method for Android malware detection and family classification based on deep neural network...

Full description

Saved in:
Bibliographic Details
Main Authors: Fan CHAO, Zhi YANG, Xuehui DU, Yan SUN
Format: Article
Language:English
Published: POSTS&TELECOM PRESS Co., LTD 2020-10-01
Series:网络与信息安全学报
Subjects:
Online Access:http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020060
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1841529975109320704
author Fan CHAO
Zhi YANG
Xuehui DU
Yan SUN
author_facet Fan CHAO
Zhi YANG
Xuehui DU
Yan SUN
author_sort Fan CHAO
collection DOAJ
description Android is increasingly facing the threat of malware attacks.It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine,method for Android malware detection and family classification based on deep neural network was proposed.Based on the comprehensive extraction of application components,Intent Filter,permissions,and data flow,the method performed an effective feature selection to reduce dimensions,and conducted a large-sample detection and multi-class classification for malware based on deep neural network.The experimental results show that the method can conduct an effective detection and classification.The accuracy of binary classification between benign and malicious Apps is 97.73%,and the accuracy of family multi-class classification can reach 93.54%,which is higher than other machine learning algorithms.
format Article
id doaj-art-7576abc5627740b39bab4ffa966107e0
institution Kabale University
issn 2096-109X
language English
publishDate 2020-10-01
publisher POSTS&TELECOM PRESS Co., LTD
record_format Article
series 网络与信息安全学报
spelling doaj-art-7576abc5627740b39bab4ffa966107e02025-01-15T03:14:23ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2020-10-016677959560964Android malware detection method based on deep neural networkFan CHAOZhi YANGXuehui DUYan SUNAndroid is increasingly facing the threat of malware attacks.It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine,method for Android malware detection and family classification based on deep neural network was proposed.Based on the comprehensive extraction of application components,Intent Filter,permissions,and data flow,the method performed an effective feature selection to reduce dimensions,and conducted a large-sample detection and multi-class classification for malware based on deep neural network.The experimental results show that the method can conduct an effective detection and classification.The accuracy of binary classification between benign and malicious Apps is 97.73%,and the accuracy of family multi-class classification can reach 93.54%,which is higher than other machine learning algorithms.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020060Androidmalware detectionstatic analysisfeature selectiondeep neural network
spellingShingle Fan CHAO
Zhi YANG
Xuehui DU
Yan SUN
Android malware detection method based on deep neural network
网络与信息安全学报
Android
malware detection
static analysis
feature selection
deep neural network
title Android malware detection method based on deep neural network
title_full Android malware detection method based on deep neural network
title_fullStr Android malware detection method based on deep neural network
title_full_unstemmed Android malware detection method based on deep neural network
title_short Android malware detection method based on deep neural network
title_sort android malware detection method based on deep neural network
topic Android
malware detection
static analysis
feature selection
deep neural network
url http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020060
work_keys_str_mv AT fanchao androidmalwaredetectionmethodbasedondeepneuralnetwork
AT zhiyang androidmalwaredetectionmethodbasedondeepneuralnetwork
AT xuehuidu androidmalwaredetectionmethodbasedondeepneuralnetwork
AT yansun androidmalwaredetectionmethodbasedondeepneuralnetwork