ConXsense - Automated Context Classification for Context-Aware Access Control
Markus Miettinen, Stephan Heuser, Wiebke Kronz, Ahmad-Reza, Sadeghi, N. Asokan

TL;DR
ConXsense is a novel framework that uses machine learning and context sensing to automatically classify smartphone contexts for improved, user-preferred access control and security, addressing limitations of prior static or user-defined policies.
Contribution
It introduces a probabilistic, context-aware access control framework that automatically classifies security-related contexts on mobile devices, integrating with Android for real-time enforcement.
Findings
Effective context classification using real-world data
Improved security against device misuse and sensory malware
User perceptions align with framework's security assessments
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
