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arXiv · 1308.2903

ConXsense - Automated Context Classification for Context-Aware Access Control

Markus Miettinen·Stephan Heuser·Wiebke Kronz·Ahmad-Reza Sadeghi·N. Asokan
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Abstract

We 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.

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Markus Miettinen, Stephan Heuser, Wiebke Kronz, Ahmad-Reza Sadeghi, N. Asokan. 2014-06-05. ConXsense - Automated Context Classification for Context-Aware Access Control. https://doi.org/10.1145/2590296.2590337

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