HEMD: a highly efficient random forest-based malware detection framework for Android | |
Zhu, HJ (Zhu, Hui-Juan)[ 1,2,3 ]; Jiang, TH (Jiang, Tong-Hai)[ 1,3 ]![]() ![]() ![]() | |
刊名 | NEURAL COMPUTING & APPLICATIONS
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2018 | |
卷号 | 30期号:11页码:3353-3361 |
关键词 | Random forest Malware detection Android Support vector machine Requested permissions |
ISSN号 | 0941-0643 |
DOI | 10.1007/s00521-017-2914-y |
英文摘要 | Mobile phones are rapidly becoming the most widespread and popular form of communication; thus, they are also the most important attack target of malware. The amount of malware in mobile phones is increasing exponentially and poses a serious security threat. Google's Android is the most popular smart phone platforms in the world and the mechanisms of permission declaration access control cannot identify the malware. In this paper, we proposed an ensemble machine learning system for the detection of malware on Android devices. More specifically, four groups of features including permissions, monitoring system events, sensitive API and permission rate are extracted to characterize each Android application (app). Then an ensemble random forest classifier is learned to detect whether an app is potentially malicious or not. The performance of our proposed method is evaluated on the actual data set using tenfold cross-validation. The experimental results demonstrate that the proposed method can achieve a highly accuracy of 89.91%. For further assessing the performance of our method, we compared it with the state-of-the-art support vector machine classifier. Comparison results demonstrate that the proposed method is extremely promising and could provide a cost-effective alternative for Android malware detection. |
WOS记录号 | WOS:000451178200007 |
内容类型 | 期刊论文 |
源URL | [http://ir.xjipc.cas.cn/handle/365002/5782] ![]() |
专题 | 新疆理化技术研究所_多语种信息技术研究室 |
通讯作者 | You, ZH (You, Zhu-Hong)[ 1,3 ] |
作者单位 | 1.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Xinjiang Lab Minor Speech & Language Informat Pro, Urumqi 830011, Peoples R China |
推荐引用方式 GB/T 7714 | Zhu, HJ ,Jiang, TH ,Ma, B ,et al. HEMD: a highly efficient random forest-based malware detection framework for Android[J]. NEURAL COMPUTING & APPLICATIONS,2018,30(11):3353-3361. |
APA | Zhu, HJ ,Jiang, TH ,Ma, B ,You, ZH ,Shi, WL ,&Cheng, L .(2018).HEMD: a highly efficient random forest-based malware detection framework for Android.NEURAL COMPUTING & APPLICATIONS,30(11),3353-3361. |
MLA | Zhu, HJ ,et al."HEMD: a highly efficient random forest-based malware detection framework for Android".NEURAL COMPUTING & APPLICATIONS 30.11(2018):3353-3361. |
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