Social Computational Trust Model (SCTM): A Framework to Facilitate Selection of Partners

Authors
Publication date 2018
Book title Proceedings of INDIS 2018: Innovating the Network for Data-Intensive Science
Book subtitle held in conjunction with SC18: The International Conference for High Performance Computing, Networking, Storage and Analysis, Dallas, Texas, November 11-16, 2018
ISBN
  • 9781728101958
ISBN (electronic)
  • 9781728101941
Event 2018 IEEE/ACM Innovating the Network for Data-Intensive Science
Pages (from-to) 45-54
Publisher Los Alamitos, California: IEEE Computer Society
Organisations
  • Faculty of Law (FdR)
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
  • Faculty of Law (FdR) - Leibniz Center for Law (FdR)
Abstract
Creating a cyber security alliance among network domain owners, as a means to minimize security incidents, has gained the interest of practitioners and academics in the last few years. A cyber security alliance, like any membership organization, requires the creation and maintenance of trust among its members, in this case the network domain owners. To promote the disclosure and sharing of cyber security information among the network domain owners, a trust framework is needed. This paper discusses a social computational trust model (SCTM), that helps alliance members to select the right partner to collaborate with and perform collective tasks, and encourages the sharing of incident data and intelligence. The social computational trust model combines benevolence and competence to estimate the risk of interaction. Benevolence is computed from personal experiences gained through direct interactions and competence is assessed on the base of the received feedback from the other members. An agent based model case study is presented to demonstrate our approach. The practicability of the proposed risk estimation is validated with a detailed experiment.
Document type Conference contribution
Language English
Published at https://doi.org/10.1109/INDIS.2018.00008
Other links https://www.scopus.com/pages/publications/85063317933
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