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

Miettinen, Markus and Heuser, Stephan and Kronz, Wiebke and Sadeghi, Ahmad-Reza and 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 and Heuser, Stephan and Kronz, Wiebke and Sadeghi, Ahmad-Reza and 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.

Title of Book: 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
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