ConXsense - Automated Context Classification for Context-Aware Access Control
This addresses security and privacy concerns for mobile device users by automating context classification, though it is incremental as it builds on existing context-aware and access control methods.
The paper tackles the problem of laborious policy specification in context-aware access control by introducing ConXsense, a framework that uses machine learning to automatically classify contexts for security, achieving integration with FlaskDroid on Android and demonstrating effectiveness with real-world data.
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.