Paper Info
Title
Towards a Generic Trust Management Framework Using a Machine-Learning-Based Trust Model
Abstract
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 features 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).
Year
DOI
Venue
2015
10.1109/Trustcom-BigDataSe-ISPA.2015.528
TrustCom/BigDataSE/ISPA
Field
DocType
Citations 
Architecture,World Wide Web,Collaboration,Computer security,Computer science,Support vector machine,Computer communication networks,Artificial intelligence,Computational trust,Machine learning
Conference
1
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
Jorge López1166.11
Stephane Maag222927.21