Authors:
Ronaldo Rodrigues Martins
;
Marcos Paulo de Oliveira Camargo
;
William Filisbino Passini
;
Gabriel Nagassaki Campos
and
Frank José Affonso
Affiliation:
Department of Statistics, Applied Mathematics and Computation, São Paulo State University – UNESP, PO Box 178, Rio Claro, São Paulo, 13506-900, Brazil
Keyword(s):
Self-protecting, Mobile Applications, Web Service, Security.
Abstract:
The evolution of software systems in the last 10 years has brought new challenges for the development area, especially for service-oriented Mobile Applications (MobApps). In the mobile computing domain, the integration of MobApps into service-based systems has been a feasible alternative to boost the capacity of processing and storage of such applications. In parallel, this type of application needs monitoring approaches mainly due to the need of dealing with a large number of users, continuous changes in the execution environment, and security threats. Besides that, most MobApps do not present the self-protecting property by default, resulting in a number of adverse situations, such as integrity of execution, reliability, security, and adaptations at runtime. The principal contribution of this paper is an approach based on MAPE-K (Monitor-Analyze-Plan-Execute over Knowledge) loop and machine learning techniques to ensure self-protecting features in MobApps, in particular, those base
d on services. Experimental results showed that this approach can autonomously and dynamically mitigate threats, making these applications more trustworthy and intrusion-safe. Our approach has good potential to contribute to the development of MobApps, going beyond existing approaches.
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