ConXsense – Context Profiling and Classification for Context-Aware Access Control (Best Paper Award)

AuthorMiettinen, Markus; Heuser, Stephan; Kronz, Wiebke; Sadeghi, Ahmad-Reza; Asokan, N.
TypeConference Proceedings
AbstractWe present ConXsense, the first framework for context-aware access control on mobile devices based on context classification. Previous context-aware access control systems often require users to laboriously specify detailed policies or they rely on pre-defined policies not adequately reflecting the true preferences of users. We present the design and implementation of a context-aware framework that uses a probabilistic approach to overcome these deficiencies. The framework utilizes context sensing and machine learning to automatically classify contexts according to their security and privacy-related properties. We apply the framework to two important smartphone-related use cases: protection against device misuse using a dynamic device lock and protection against sensory malware. We ground our analysis on a sociological survey examining the perceptions and concerns of users related to contextual smartphone security and analyze the effectiveness of our approach with real-world context data. We also demonstrate the integration of our framework with the FlaskDroid architecture for fine-grained access control enforcement on the Android platform.
InProceedings of the 9th ACM Symposium on Information, Computer and Communications Security (ASIACCS 2014)