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Communication Dans Un Congrès Année : 2015

Towards a generic trust management gramework using a machine-learning-based trust model

Résumé

Nowadays, the ever-growing capabilities in computer communication networks have entitled and encouraged developers and researchers to build collaborative applications, systems, and devices. On the one hand with increased collaboration several advantages have been obtained, but, on the other hand, issues may arise due to untrustworthy interactions. To address these issues, many researchers have studied trust as a computer science concept. Nevertheless, one of the greatest challenges in the trust domain is the lack of a generic trust management framework that will ease and encourage existing collaborative systems to adopt such concepts. In this paper, we propose a generic trust management framework which is capable of processing different trust parameters as required. We propose a RESTful message exchanging architecture, and a trust model based on the solution of a multi-class classification problem using machine learning techniques, namely Support Vector Machines (SVM). Further, based on our previous works, we briefly discuss potential trust properties that can be employed by our proposed framework.
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Dates et versions

hal-01263244 , version 1 (27-01-2016)

Identifiants

Citer

Jorge López, Stephane Maag. Towards a generic trust management gramework using a machine-learning-based trust model. TRUSTCOM 2015 : 14th International Conference on Trust, Security and Privacy in Computing and Communications, Aug 2015, Helsinki, Finland. pp.1343 - 1348, ⟨10.1109/Trustcom.2015.528⟩. ⟨hal-01263244⟩
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