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EASEAndroid: Automatic Policy Analysis and Refinement for Security Enhanced Android via Large-Scale Semi-Supervised Learning

Ruowen Wang, William Enck, Douglas S. Reeves, Xinwen Zhang, Peng Ning, Dingbang Xu, Wu Zhou, Ahmed M. Azab
2015 USENIX Security Symposium  
In this paper, we propose EASE-Android, the first SEAndroid analytic platform for automatic policy analysis and refinement.  ...  Our key insight is that the policy refinement process can be modeled and automated using semi-supervised learning.  ...  Acknowledgement We would like to thank Michael Grace, Kunal Patel and Xiaoyong Zhou from Samsung Research America for their valuable input for this paper.  ... 
dblp:conf/uss/WangERZNXZA15 fatcat:ut3c4bn6nzgwdpgowdwntaj6tu

Establishing mandatory access control on Android OS [article]

Sven Bugiel, Universität Des Saarlandes, Universität Des Saarlandes
2016
A very recent work, EASEAndroid [194] , tackles the problem of (semi-)automatically analyzing the audit logs of SELinux access control enforcement on real devices at large-scale and thus helping security  ...  To this end, the authors devised a tool based on a semi-supervised classifier and policy refiner that mimics the methodology of human security analysts.  ...  be noted, that these measurements are not directly comparable, because all security models have originally been implemented for a different Android OS version and been tested on a different hardware platform  ... 
doi:10.22028/d291-26642 fatcat:yeqularlsjbzvntfnxri4hm7q4