基于隐性标识符的零权限Android智能终端识别
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摘要
针对现有Android智能终端识别直接利用IMEI,Android_ID等显性标识符存在依赖敏感权限和易失效的问题,提出了一种基于隐性标识符组合的零权限设备识别方法.通过调用系统API并执行Linux Shell命令,从物理层、应用层以及用户层3个层次上获取设备型号、屏幕信息、内核编译信息、User Agent、系统语言、字体大小、字体列表、用户安装程序列表等8个隐性标识符,并将其组合形成设备指纹.然后,提出了指纹精确匹配算法和变化指纹关联匹配算法,用于仅1个隐性标识符发生变化且变化前后相似程度大于0.85时的设备关联识别.通过采集真实用户数据进行实验验证,结果表明所提算法的设备识别准确率达到94.52%.
In the current technologies of Android device identification,explicit identifiers are usually adopted,such as IMEI( international mobile equipment identity) and Android_ID,which depend on sensitive permissions and are liable to fail. To solve these problems,a zero permission device identification method based on the implicit identifier group is proposed. From the physic layer,the application layer and the user layer,eight implicit identifiers including device type,screen information,kernel compile information,user agent,system language,font size,font list and user installed package list are obtained by calling system API( application programming interface) and executing Linux Shell commands. These eight implicit identifiers are combined to form the device fingerprint.Then,the corresponding fingerprint exact matching algorithm and the changing fingerprint associated matching algorithm are proposed,which can be applied to device related identification when only one implicit identifier changes and the similarity ratio is above 0. 85 before and after the change. Finally,the experimental verification is carried out based on the real user data,and the results showthat the device identification accuracy rate of the proposed algorithm can reach 94. 52%.
In the current technologies of Android device identification,explicit identifiers are usually adopted,such as IMEI( international mobile equipment identity) and Android_ID,which depend on sensitive permissions and are liable to fail. To solve these problems,a zero permission device identification method based on the implicit identifier group is proposed. From the physic layer,the application layer and the user layer,eight implicit identifiers including device type,screen information,kernel compile information,user agent,system language,font size,font list and user installed package list are obtained by calling system API( application programming interface) and executing Linux Shell commands. These eight implicit identifiers are combined to form the device fingerprint.Then,the corresponding fingerprint exact matching algorithm and the changing fingerprint associated matching algorithm are proposed,which can be applied to device related identification when only one implicit identifier changes and the similarity ratio is above 0. 85 before and after the change. Finally,the experimental verification is carried out based on the real user data,and the results showthat the device identification accuracy rate of the proposed algorithm can reach 94. 52%.
引文
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[2]Han S,Jung J,Wetherall D.An empirical study of third-party tracking by mobile applications in the w ild[R].San Jose,CA,USA:NSDI,2012.
[3]Google Inc.Identifying app installations[EB/OL].(2011-03-01)[2015-01-31].http://android-developers.blogspot.jp/2011/03/identifying-app-installations.html.
[4]Grace M C,Zhou W,Jiang X,et al.Unsafe exposure analysis of mobile in-app advertisements[C]//Proceedings of the Fifth ACM Conference on Security and Privacy in Wireless and Mobile Networks.Tucson,Arizona,USA,2012:101-112.
[5]Kohno T,Broido A,Claffy K C.Remote physical device fingerprinting[J].IEEE Transactions on Dependable and Secure Computing,2005,2(2):93-108.
[6]Dey S,Roy N,Xu W,et al.Accel Print:imperfections of accelerometers make smartphones trackable[C]//Proceedings of the Network and Distributed System Security Symposium.San Diego,CA,USA,2014:1-16.
[7]Zhou Z,Diao W,Liu X,et al.Acoustic fingerprinting revisited:generate stable device ID stealthily w ith inaudible sound[C]//Proceedings of the 2014 ACM SIGSAC Conference on Computer&Communications Security.New York,USA,2014:429-440.
[8]Das A,Borisov N,Caesar M.Do you hear what I hear?Fingerprinting smart devices through embedded acoustic components[C]//Proceedings of the 2014ACM SIG SAC Conference on Computer&Communications Security.New York,USA,2014:441-452.
[9]Franklin J,Mc Coy D,Tabriz P,et al.Passive data link layer 802.11 w ireless device driver fingerprinting[C]//Proceedings of the 15th Conference on USENIX Security Symposium.Vancouver,Canada,2006:167-178.
[10]Erman J,Arlitt M,Mahanti A.Traffic classification using clustering algorithms[C]//Proceedings of the2006 SIG COMM Workshop on Mining Network Data.Pisa,Italy,2006:281-286.
[11]Stber T,Frank M,Schmitt J,et al.Who do you sync you are smartphone fingerprinting via application behaviour[C]//Proceedings of the Sixth ACM Conference on Security and Privacy in Wireless and Mobile Networks.Budapest,Hungary,2013:7-12.
[12]Pang J,Greenstein B,Gummadi R,et al.802.11 user fingerprinting[C]//Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking.M ontreal,Canada,2007:99-110.
[13]Acar G,Juarez M,Nikiforakis N,et al.FPDetective:dusting the w eb for fingerprinters[C]//Proceedings of the 2013 ACM SIG SAC Conference on Computer&Communications Security.Berlin,Germany,2013:1129-1140.
[14]Eckersley P.How unique is your web browser?[C]//Proceedings of the 10th International Conference on Privacy Enhancing Technologies.Berlin,Germany,2010:1-18.