Reinforcement learning application for dynamic trust modeling in large-scale open distributed systems

In large-scale open distributed environment such as Grid computing, Ubiquitous computing, P2P computing and Ad hoc, etc., network is a dynamic and cooperative system made up of multi-software serving. Under the dynamic and uncertainty environment, traditional approaches to security are often found lacking, so dynamic trust model becomes a new and hot topic of security research for these new distributed applications. Focusing on dynamic, uncertainty and collaborative between entities under large-scale distributed environment, theory of Reinforcement Learning (RL) is applied to the study of dynamic trust model. First, basic formal description is conducted for trust decision, and behavior state-space structure is constructed based on gain function according the interaction time sequence between entities. Then, applying RL algorithm, based on direct trust gain function and feedback trust gain function, overall trust degree fusion computing model is set up. New model makes full use of the advantages of the RL algorithm, brakes away from the incongruence problem of trust-making in the traditional method, in which the weights are set up by subjective manners. Simulation's results show that, compared to the existing trust models, the new model has a better dynamic adaptation capability.