TU Darmstadt / ULB / TUbiblio

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

Miettinen, Markus ; Heuser, Stephan ; Kronz, Wiebke ; Sadeghi, Ahmad-Reza ; Asokan, N. :
ConXsense – Context Profiling and Classification for Context-Aware Access Control (Best Paper Award).
Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security (ASIACCS 2014)
[Konferenz- oder Workshop-Beitrag], (2014)

Kurzbeschreibung (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.

Typ des Eintrags: Konferenz- oder Workshop-Beitrag (Keine Angabe)
Erschienen: 2014
Autor(en): Miettinen, Markus ; Heuser, Stephan ; Kronz, Wiebke ; Sadeghi, Ahmad-Reza ; Asokan, N.
Titel: ConXsense – Context Profiling and Classification for Context-Aware Access Control (Best Paper Award)
Sprache: Deutsch
Kurzbeschreibung (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.

Buchtitel: Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security (ASIACCS 2014)
Freie Schlagworte: ICRI-SC;Mobile security; Context sensing; Privacy policies; Context-awareness
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Systemsicherheit
Profilbereiche
Profilbereiche > Cybersicherheit (CYSEC)
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt
Veranstaltungsort: Kyoto, Japan
Hinterlegungsdatum: 04 Aug 2016 10:13
ID-Nummer: TUD-CS-2014-0030
Verwandte URLs:
Export:

Optionen (nur für Redakteure)

Eintrag anzeigen Eintrag anzeigen