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DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model
Zhu, HJ (Zhu, Hui-Juan); You, ZH (You, Zhu-Hong); Zhu, ZX (Zhu, Ze-Xuan); Shi, WL (Shi, Wei-Lei); Chen, X (Chen, Xing); Cheng, L (Cheng, Li)
2018
发表期刊NEUROCOMPUTING
ISSN0925-2312
卷号272期号:1页码:638-646
摘要

The Android platform is becoming increasingly popular and various organizations have developed a variety of applications (App) to cater to market trends. Due to the characteristics of the Android platform, such as supporting the unofficial App stores, open source policy and the great tolerance for App verification, it is inevitable that it faces serious problems of malicious software intrusion. In order to protect the users from the serious damages caused by Android malware, we propose a low-cost and high-efficient method to extract permissions, sensitive APIs, monitoring system events and permission-rate as key features, and employ the ensemble Rotation Forest (RF) to construct a model to detect whether an Android App is malicious or not. Specifically, a dataset containing 2,130 samples is used to verify the performance of the proposed method. The experimental results show that the proposed method achieves an high accuracy of 88.26% with 88.40% sensitivity at the precision of 88.16%. To further evaluate the performance of the proposed model, we also compare it with the state-of-the-art Support Vector Machine (SVM) model under the same experimental conditions, and the comparison results demonstrate that the proposed method improves the accuracy by 3.33% compared to SVM. The experimental results show that the proposed model is extremely promising and could provide a cost-effective alternative for Android malware detection.

关键词Rotation Forests Malware Detection Neural Network Mobile Phones
DOI10.1016/j.neucom.2017.07.030
收录类别SCI
WOS记录号WOS:000413821400064
引用统计
被引频次:4[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.xjipc.cas.cn/handle/365002/5066
专题多语种信息技术研究室
通讯作者You, ZH (You, Zhu-Hong)
作者单位1.Yang Zhou Univ, Sch Informat Engn, Yangzhou 225000, Jiangsu, Peoples R China
2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
3.Xinjiang Lab Minor Speech & Language Informat Pro, Urumqi 830011, Peoples R China
4.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
5.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Peoples R China
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GB/T 7714
Zhu, HJ ,You, ZH ,Zhu, ZX ,et al. DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model[J]. NEUROCOMPUTING,2018,272(1):638-646.
APA Zhu, HJ ,You, ZH ,Zhu, ZX ,Shi, WL ,Chen, X ,&Cheng, L .(2018).DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model.NEUROCOMPUTING,272(1),638-646.
MLA Zhu, HJ ,et al."DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model".NEUROCOMPUTING 272.1(2018):638-646.
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