Miettinen, Markus ; Heuser, Stephan ; Kronz, Wiebke ; Sadeghi, Ahmad-Reza ; Asokan, N. (2014):
ConXsense – Context Profiling and Classification for Context-Aware Access Control (Best Paper Award).
In: Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security (ASIACCS 2014),
Kyoto, Japan, DOI: 10.1145/2590296.2590337,
[Conference or Workshop Item]
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.
Item Type: | Conference or Workshop Item |
---|---|
Erschienen: | 2014 |
Creators: | Miettinen, Markus ; Heuser, Stephan ; Kronz, Wiebke ; Sadeghi, Ahmad-Reza ; Asokan, N. |
Title: | ConXsense – Context Profiling and Classification for Context-Aware Access Control (Best Paper Award) |
Language: | German |
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. |
Book Title: | Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security (ASIACCS 2014) |
Uncontrolled Keywords: | ICRI-SC;Mobile security; Context sensing; Privacy policies; Context-awareness |
Divisions: | 20 Department of Computer Science 20 Department of Computer Science > System Security Lab Profile Areas Profile Areas > Cybersecurity (CYSEC) LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt |
Event Location: | Kyoto, Japan |
Date Deposited: | 04 Aug 2016 10:13 |
DOI: | 10.1145/2590296.2590337 |
Identification Number: | TUD-CS-2014-0030 |
PPN: | |
Corresponding Links: | |
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