@INPROCEEDINGS{,
  author = {Leonid Batyuk and Sahin Albayrak},
  title = {Secure context-aware reconfiguration for mobile devices},
isbn = {ISBN 978-3-8396-0159-4}, 
  booktitle = {Inproceedings of the 5th Future Security Research Conference},
  year = {2010},
  abstract = {Over the past ten years, the trend in user applications has definitely
	moved towards autonomous mobile devices, and wireless networks. More
	and more applications rely on various physical and logical sensors.
	The computing environments of today have become highly heterogeneous
	and spontaneous, but the models and methods for software development
	remained mostly the same as for desktop computers and wired networks.
	These models do not allow software to adapt to the current situation
	of
	
	the user.
	
	
	A novel approach to solving this problem is context-awareness, where
	software is enabled with knowledge about its environment and adapts
	itself to the current needs of the user. During the last decade,
	numerous context-awareness frameworks have evolved on both infrastructure-level
	and device-level.
	
	
	Most context-aware systems rely on predefined settings which invoke
	a certain action upon a change in the environment. However, in several
	works an attempt has been made to overcome the fixed model and implement
	a generic approach. The concepts of context proximity and context
	familiarity have been introduced, which, to some extent, enable reasoning
	upon context without having a precise set of rules and predefined
	conditions.
	
	
	These approaches provide more flexibility, but less control due to
	inability of such methods to annotate context information with semantic
	data, enabling full-fledged reasoning. As such, automated semantic
	recognition and annotation of context is a fundamental problem we
	address.
	
	
	Utilizing contextual information for autonomous secure self-configuration
	is an example of a use case where manual rule-base management is
	unfeasible, but also current heuristics fail due to their shortcomings.
	We contribute to existing heuristic approaches with methods of artificial
	intelligence, utilizing techniques known from the field of anomaly
	detection to identify, predict and annotate context information,
	allowing to make more precise and reliable security decisions upon
	sensor data.}
}