A dynamic Bayesian-based comprehensive trust evaluation model for dispersed computing environment

Dispersed computing is a new resource-centric computing paradigm. Due to its high degree of openness and decentralization, it is vulnerable to attacks, and security issues have become an important challenge hindering its development. The trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing networks. In this paper, a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing environment. Specifically, in the calculation of direct trust, a logarithmic decay function and a sliding window are introduced to improve the timeliness. In the calculation of indirect trust, a random screening method based on sine function is designed, which excludes malicious nodes providing false reports and multiple malicious nodes colluding attacks. Finally, the comprehensive trust value is dynamically updated based on historical interactions, current interactions and momentary changes. Simulation experiments are introduced to verify the performance of the model. Compared with existing model, the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes, the interaction success rate, and the computational cost.