期刊文献+

基于混合特征的Android恶意软件静态检测 被引量:6

Android Malware Static Detection Based on Hybrid Features
下载PDF
导出
摘要 当前智能手机市场中,Android占有很大的市场份额,又因其他的开源,基于Android系统的智能手机很容易成为攻击者的首选目标。随着对Android恶意软件的快速增长,Android手机用户迫切需要保护自己手机安全的解决方案。为此,对多款Android恶意软件进行静态分析,得出Android恶意软件中存在危险API列表、危险系统调用列表和权限列表,并将这些列表合并,组成Android应用的混合特征集。应用混合特征集,结合主成分分析(PCA)和支持向量机(SVM),建立Android恶意软件的静态检测模型。利用此模型实现仿真实验,实验结果表明,该方法能够快速检测Android应用中恶意软件,且不用运行软件,检测准确率较高。 Android occupies a large share in the current smart phone market,and due to its open source, smart phones based on Android are very easy to become the first targets of attacks.With the rapid growth of Android mobile malware,Android owners urgently need security solutions to protect their mobile phones.In this paper,static analysis is performed on many types of Android malware, and a conclusion is got that there are dangerous API list,dangerous system call list and permission list in Android malware.These lists are combined into a hybrid feature set which is then used in combination with principal component analysis (PCA) and support vector machine (SVM) to establish an Android malware static testing model.The simulation experiments realized through this model show that the method can rapidly detect malicious software and it' s not necessary to run software,the detection accuracy is also higher.
出处 《无线电通信技术》 2014年第6期64-68,共5页 Radio Communications Technology
基金 浙江省移动网络应用技术联合重点实验室(2010E10005) 浙江省新一代移动互联网用户端软件科技创新团队(2010R50009) 基于TD-LTE的无线宽带政务示范网的评估测试与优化研究2011C11042 新一代移动互联网移动采编平台研究(2012R10009-20)
关键词 混合特征 主成分分析法 支持向量机 ANDROID应用 恶意检测 hybrid feature principal component analysis support vector machine Android applications malware detection
  • 相关文献

参考文献10

  • 1趋势科技发布《2013信息安全关键十大预测报告》[J].计算机安全,2013(2):75-76. 被引量:1
  • 2李钊,李建军,李智生,杨亚威.基于生物视觉特征的SVM目标分类算法[J].无线电工程,2012,42(10):58-60. 被引量:2
  • 3BURGUERA I,ZURUTUZA U,NADJM-TEHRANI S.Crowdroid:Behavior-based Malware Detection System for Android[C] ∥Proc.First ACM Workshop on Security and Privacy in Smartphones and Mobile devices(SPSM’11),New York,NY,USA,2011:15-26. 被引量:1
  • 4GIBLER C,CRUSSELL J,ERICKSON J,et al.Android Leaks:Automatically Detecting Potential Privacy Leaks in Android Applications on a Large Scale[C] ∥Proc.Fifth Int.Conf.Trust and Trustworthy Computing(TRUST2012),Vienna,Austria,2012:291-307. 被引量:1
  • 5MANN C,STAROSTIN A.A Framework for Static Detection of Privacy Leaks in Android Applications[C] ∥Proc.27th Annual ACM Symp.Applied Computing(SAC’12),Trento,Italy,2012:1457-1462. 被引量:1
  • 6SHABTAI A,FLEDEL Y,ELOVICI Y.Automated Static Code Analysis for Classifying Android Applications Using Machine Learning[C] ∥in Proceedings of the 2010 International Conf.on Computational Intelligence and Security,2010:329-333. 被引量:1
  • 7房鑫鑫..Android恶意软件实现及检测研究[D].南京邮电大学,2013:
  • 8DINI G,MARTINELLI F,SARACINO A,et al.A Multilevel Anomaly Detector for Android Malware[J].In:Kotenko,I.,Skormin,V.(eds.)MMM-ACNS 2012.LNCS,2012(7531):240-253. 被引量:1
  • 9WANG T Y,WU C H,HSIEH C C.A Virus Prevention Model Based on Static Analysis and Data Mining Methods[C] ∥Proc.IEEE Eighth Int.Conf.Computer and Information Technology Workshops,Sydney,2008:288-293. 被引量:1
  • 10SAHS J,KHAN L.A Machine Learning Approach to Android Malware Detection[C] ∥European Intelligence and Security Informatics Conf.,Odense,Denmark,2012:141-147. 被引量:1

二级参考文献6

  • 1谢昭,高隽.图像理解理论与方法[M].北京:科学出版社,2009. 被引量:1
  • 2PREEYAKORN T, SUTHEP M. A Modified Generalized Hough Transform for Image Search [ J ]. IEICE Transac- tions on Information and Systems, 2007, 90 ( 1 ): 165 - 172. 被引量:1
  • 3VIOLA P, JONES M J. Rapid Object Detection Using a Boosted Cascade of Simple Features [ C ] // California: IEEE Conference Compute Vision and Pattern Recongni- tion, 2001:100 - 110. 被引量:1
  • 4LOWED G. Distinctive Image Features from Scale-invari- ant Keypoints [ J ]. International Journal of Computer Vi- sion, 2004, 60(2): 91 -100. 被引量:1
  • 5RIESENHUBER M, POGGIO T. Hierarchical Models of Object Recognition in Cortex[J]. Nature America, 1999, 2(11): 1 019-1 025. 被引量:1
  • 6SERRE T, WOLF L, POGGIO T, Object Recognition with Features Inspired by Visual Cortex[ C ]// California: IEEE Conference Compute Vision and Pattern Recongnition, 2005 : 120 - 130. 被引量:1

共引文献1

同被引文献52

  • 1戚湧,胡俊,於东军.基于自组织映射与概率神经网络的增量式学习算法[J].南京理工大学学报,2013,37(1):1-6. 被引量:7
  • 2Anastasia S,Dennis G.Review of the mobile malware detection approaches[C]//Proceedings of the 23rd International Conference on Parallel,Distributed and Network-Based Processing.Washington,USA:IEEE Computer Society,2015:600-603. 被引量:1
  • 3Islam R,Tian R,Batten L M,et al.Review:classification of malware based on integrated static and dynamic features[J].Journal of Network and Computer Applications,2013,36(2):646-656. 被引量:1
  • 4Mas’Ud M Z,Sahib S,Abdollah M F,et al.Analysis of features selection and machine learning classifier in Android malware detection[C]//Proceedings of IEEE International Conference on Information Science and Applications.Washington,USA:IEEE Computer Society,2014:1-5. 被引量:1
  • 5Zhou Yajin,Wang Zhi,Zhou Wu,et al.Hey,you,get off of my market:detecting malicious Apps in official and alternative Android markets[C]//Proceedings of the 19th Annual Network & Distributed System Security Symposium.Washington,USA:Internet Society,2012:123-129. 被引量:1
  • 6Zhang Yuan,Yang Min,Yang Zhemin,et al.Permission use analysis for vetting undesirable behaviors in Android Apps[J].IEEE Transactions on Information Forensics and Security,2014,9(11):1828-1842. 被引量:1
  • 7Pandita R,Xiao X,Yang W,et al.WHYPER:towards automating risk assessment of mobile applications[C]//Proceedings of the 22nd USENIX Security Symposium.Berkeley,USA:USENIX,2013:89-97. 被引量:1
  • 8Salehi Z,Ghiasi M,Sami A.A miner for malware detection based on API function calls and their arguments[C]//Proceedings of the 16th CSI International Symposium on Artificial Intelligence and Signal Processing.Washington,USA:IEEE Computer Society,2012:563-568. 被引量:1
  • 9Yerima S Y,Sezer S,Muttik I.High accuracy Android malware detection using ensemble learning[J].IET Information Security,2015,9(6):313-320. 被引量:1
  • 10乜聚虎,周学海,余艳玮,吴志忠.Android安全加固技术[J].计算机系统应用,2011,20(10):74-77. 被引量:20

引证文献6

二级引证文献27

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部