浏览全部资源
扫码关注微信
1. 东南大学网络空间安全学院,江苏 南京 211189
2. 东南大学江苏省计算机网络技术重点实验室,江苏 南京 211189
3. 网络通信与安全紫金山实验室,江苏 南京 211189
4. 东南大学信息科学与工程学院,江苏 南京 211189
[ "宋宇波(1977− ),男,江苏无锡人,博士,东南大学副教授,主要研究方向为无线网络和移动通信安全、移动终端安全、专有数据安全、区块链安全等" ]
[ "陈琪(1996− ),女,江苏泰州人,东南大学硕士生,主要研究方向为物联网流量识别、Android隐私保护等" ]
[ "宋睿(1994− ),男,江苏宿迁人,东南大学硕士生,主要研究方向为移动终端安全、专有数据安全、区块链安全等" ]
[ "胡爱群(1966− ),男,江苏南通人,博士,东南大学教授,主要研究方向为无线网络安全、物理层安全技术" ]
网络出版日期:2021-06,
纸质出版日期:2021-06-25
移动端阅览
宋宇波, 陈琪, 宋睿, 等. 基于虚拟机字节码注入的Android应用程序隐私保护机制[J]. 通信学报, 2021,42(6):171-181.
Yubo SONG, Qi CHEN, Rui SONG, et al. Android application privacy protection mechanism based on virtual machine bytecode injection[J]. Journal on communications, 2021, 42(6): 171-181.
宋宇波, 陈琪, 宋睿, 等. 基于虚拟机字节码注入的Android应用程序隐私保护机制[J]. 通信学报, 2021,42(6):171-181. DOI: 10.11959/j.issn.1000-436x.2021115.
Yubo SONG, Qi CHEN, Rui SONG, et al. Android application privacy protection mechanism based on virtual machine bytecode injection[J]. Journal on communications, 2021, 42(6): 171-181. DOI: 10.11959/j.issn.1000-436x.2021115.
为了解决Android应用权限机制的滥用,提出了一种基于虚拟机字节码注入技术的 Android 应用程序权限访问控制方法。所提方法能够根据用户的安全需求和使用场景,生成虚拟机字节码形式的安全策略,并将其注入Android应用的涉及危险权限请求和敏感数据访问的代码单元中,从而实现动态应用行为控制。对国内4家主流应用商店爬取的应用程序进行测试,结果表明,所提方法可以对合法App的敏感API调用和危险权限请求进行有效拦截,并根据预定的安全策略实施控制,注入虚拟机字节码后的大部分App运行不受注入代码影响,稳健性得到保证,且具有较好的普适性。
To solve the abuse of the Android application permission mechanism
a method of Android application access control based on virtual machine bytecode injection technology was proposed.The security policy in the form of virtual machine bytecode was generated according to the user’s security requirement and usage scenario
and injected into the coding unit of Android application that involves dangerous permission request and sensitive data access
to realize dynamic application behavior control.Tests on applications crawled from four mainstream domestic App stores show that the method can effectively intercept sensitive API calls and dangerous permission requests of legitimate App programs and implement control according to pre-specified security policies.Also
after injecting virtual machine bytecode
most of the App program operation is not affected by the injected code
and the robustness is guaranteed.The proposed method has a good universality.
TALAL M , ZAIDAN A , ZAIDAN B B , et al . Comprehensive review and analysis of anti-malware Apps for smartphones [J ] . Telecommunication Systems , 2019 , 72 ( 2 ): 285 - 337 .
NAUMAN M , KHAN S , ZHANG X W . APEX:extending Android permission model and enforcement with user-defined runtime constraints [C ] // The 5th ACM Symposium on Information,Computer and Communications Security . New York:ACM Press , 2010 : 328 - 332 .
MAHINDRU A , SANGAL A L . DeepDroid:feature selection approach to detect Android malware using deep learning [C ] // 2019 IEEE 10th International Conference on Software Engineering and Service Science . Piscataway:IEEE Press , 2019 : 16 - 19 .
BLÄSING T , BATYUK L , SCHMIDT A D , et al . An Android application sandbox system for suspicious software detection [C ] // 2010 5th International Conference on Malicious and Unwanted Software . Piscataway:IEEE Press , 2010 : 55 - 62 .
GIBLER C , CRUSSELL J , ERICKSON J , et al . AndroidLeaks:automatically detecting potential privacy leaks in android applications on a large scale [C ] // International Conference on Trust and Trustworthy Computing . Berlin:Springer , 2012 : 291 - 307 .
SCHUTTE J , TITZE D , DE FUENTES J M . AppCaulk:data leak prevention by injecting targeted taint tracking into android Apps [C ] // 2014 IEEE 13th International Conference on Trust,Security and Privacy in Computing and Communications . Piscataway:IEEE Press , 2014 : 370 - 379 .
CHEN K , WANG P , LEE Y , et al . Finding unknown malice in 10 seconds:mass vetting for new threats at the google-play scale [C ] // 24th USENIX Security Symposium . Berkeley:USENIX Association , 2015 : 659 - 674 .
VIDAS T , CHRISTIN N . Evading android runtime analysis via sandbox detection [C ] // The 9th ACM Symposium on Information,Computer and Communications Security . New York:ACM Press , 2014 : 447 - 458 .
FERREIRA J , RESENDE R , MARTINHO S . Beacons and BIM models for indoor guidance and location [J ] . Sensors , 2018 , 18 ( 12 ): 4374 - 4384 .
FARUKI P , BHARMAL A , LAXMI V , et al . Android security:a survey of issues,malware penetration,and defenses [J ] . IEEE Communications Surveys & Tutorials , 2015 , 17 ( 2 ): 998 - 1022 .
FELT A P , CHIN E , HANNA S , et al . Android permissions demystified [C ] // The 18th ACM conference on Computer and Communications Security . New York:ACM Press , 2011 : 627 - 638 .
LIU X L , DU X J , ZHANG X S , et al . Adversarial samples on android malware detection systems for IoT systems [J ] . Sensors , 2019 , 19 ( 4 ): 974 .
KANG H , JANG J W , MOHAISEN A , et al . Detecting and classifying android malware using static analysis along with creator information [J ] . International Journal of Distributed Sensor Networks , 2015 , 11 ( 6 ): 474 - 479 .
PENG H , GATES C , SARMA B , et al . Using probabilistic generative models for ranking risks of Android apps [C ] // The 2012 ACM Conference on Computer and Communications Security . New York:ACM Press , 2012 : 241 - 252 .
CHRISTODORESCU M , JHA S , SESHIA S A , et al . Semantics-aware malware detection [C ] // 2005 IEEE Symposium on Security and Privacy . Piscataway:IEEE Press , 2005 : 32 - 46 .
LEE J , JEONG K , LEE H . Detecting metamorphic malwares using code graphs [C ] // The 2010 ACM Symposium on Applied Computing . New York:ACM Press , 2010 : 1970 - 1977 .
SUAREZ T G , DASH S K , AHMADI M , et al . Droidsieve:Fast and accurate classification of obfuscated android malware [C ] // The Seventh ACM on Conference on Data and Application Security and Privacy . New York:ACM Press , 2017 . 309 - 320 .
DE-MAIORCA D , DE-ARIU D , CORONA I , et al . Stealth attacks:an extended insight into the obfuscation effects on Android malware [J ] . Computers & Security , 2015 , 51 : 16 - 31 .
MING J , XIN Z , LAN P W , et al . Impeding behavior-based malware analysis via replacement attacks to malware specifications [J ] . Journal of Computer Virology and Hacking Techniques , 2017 , 13 ( 3 ): 193 - 207 .
SARACINO A , SGANDURRA D , DINI G , et al . MADAM:effective and efficient behavior-based android malware detection and prevention [J ] . IEEE Transactions on Dependable and Secure Computing , 2018 , 15 ( 1 ): 83 - 97 .
VINOD P , SHOJAFAR M , KUMAR N , et al . Identification of android malware using refined system calls [J ] . Concurrency,Computation Practice and Experience , 2019 , 75 ( 2 ): 1 - 30 .
SHABTAI A , KANONOV U , ELOVICI Y , et al . Andromaly:a behavioral malware detection framework for android devices [J ] . Journal of Intelligent Information Systems , 2012 , 38 ( 1 ): 161 - 190 .
MA W , DUAN P , LIU S , et al . Shadow attacks:automatically evading system-call-behavior based malware detection [J ] . Journal in Computer Virology , 2012 , 8 ( 12 ): 1 - 13 .
LEE S H , KIM S H , KIM S , et al . Appwrapping providing fine-grained security policy enforcement per method unit in android [C ] // 2017 IEEE International Symposium on Software Reliability Engineering Workshops . Piscataway:IEEE Press , 2017 : 36 - 39 .
0
浏览量
441
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构