Research on dynamic trust-level evaluation mechanism based on machine learning

For the purpose of developing a usable trust relationship between the resource consumers (users) and the resource providers (hosts) in an open computing environment, a dynamic trust evaluation framework based on machine learning is proposed. In this framework, the trust-level of users changes dynamically according to their behaviors and some other common inspect parameters. And the trust-level of resources changes dynamically with the users' assessments and the service quality that resources provide. In this paper, a fuzzy trust-level evaluating algorithm (FTEA) based on fuzzy logic is put forward to generate the evaluation rules and integrated overall trust-level. The FTEA utilizes the rule-based machine learning method and has the advantage of self-learning to get reasoning rules from large amount of input data. The experiment shows that only 1000 groups of data are adequate to generate the reasoning rules, and the execution time of FTEA increases exponentially with the expansion of determination factors, so, an open computing system only need to select 5 to 6 factors or 1000 sample data for implementation.