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基于贝叶斯网络的Android恶意行为检测方法 被引量:7

Way of Android malicious behavior detection based on Bayesian networks
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摘要 Android操作系统是市场占有率最高的移动操作系统,基于Android平台的恶意软件也呈现爆发式的增长,而目前仍然没有有效的手段进行Android恶意行为的检测,通过分析Android恶意行为的特点,采用基于贝叶斯网络的机器学习算法进行Android恶意行为的检测,通过静态分析的方法进行Android文件静态特征的提取,将Android恶意应用的静态分析与贝叶斯网络相结合,最后通过使用提出的方法构建贝叶斯网络模型,通过实验验证了提出的Android恶意行为检测模型的有效性。 Android is the most popular operating system by far, which has the highest market share. Malicious software based on Android platform also presents explosive growth, but currently there are no effective means, which can detect the Android malicious behavior. In this paper, through analyzing the characteristics of the Android malicious behavior, it uses the machine learning algorithm based on Bayesian networks to detect the Android malicious behavior. Beyond that,this paper extracts the static characteristics of the Android file based on the static analysis method, which has realized the combination of static analysis and the Bayesian network. In the end, through the experiment, it verifies the effectiveness of the Android malicious behavior detection model.
作者 张国印 曲家兴 李晓光 ZHANG Guoyin;QU Jiaxing;LI Xiaoguang(College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;HLJ Province National Defense Science and Technology Institute, Harbin 150001, China)
出处 《计算机工程与应用》 CSCD 北大核心 2016年第17期16-23,191,共9页 Computer Engineering and Applications
基金 黑龙江省国防科学技术研究院项目(No.20150309) 黑龙江省自然科学基金面上项目(No.F201406) 黑龙江省青年科学基金(No.QC2014C067) 黑龙江博士后科研启动基金(No.LBH-Q14056)
关键词 ANDROID 机器学习 特征选择 贝叶斯网络 Android machine learning feature selection Bayesian network
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参考文献12

  • 1李玉宏,张祎.Android 软件静态分析系统的设计与实现[D].北京:北京邮电大学,2012:4-6.
  • 2张冬梅,董胜亚.基于Android 平台的软件异常行为检测技术研究[D].北京:北京邮电大学,2013:4-5.
  • 3Shabtai A,Fledel Y,Elovici Y.Securing Android-poweredmobile devices using SELinux[J].IEEE Security & Privacy,2010,8(3):36-44.
  • 4Yerima S Y,Sezer S,McWilliams G.A new Android malware detection approach using Bayesian classification[C].AINA,2013:121-128.
  • 5Wu D J,Mao C H,Wei T E,et al.Droidmat:Androidmalware detection through manifest and API calls tracing[C].2012 Seventh Asia Joint Conference on InformationSecurity(Asia JCIS),2012:62-69.
  • 6Enck W,Octeau D,McDaniel P,et al.A study of Androidapplication security[C].USENIX Security Symposium,2011:5-30.
  • 7Arp D,Spreitzenbarth M,Hübner M,et al.DREBIN:effectiveand explainable detection of Android malware inyour pocket[C].NDSS,2014:1-2.
  • 8Deshotels L,Notani V,Lakhotia A.Droidlegacy:automatedfamilial classification of Android malware[C].Proceedingsof ACM SIGPLAN on Program Protection andReverse Engineering Workshop,2014.
  • 9迟庆云,刘梦琳,姜振凤,胡华.特征提取方法对朴素贝叶斯文本分类器的影响分析[J].长江大学学报(自科版)(上旬),2013,10(9):91-93. 被引量:3
  • 10王平.基于加权贝叶斯的垃圾邮件过滤算法的改进[EB/OL].[2015-06-09].http://www.docin.com/p-605955810.html.

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