A trust model-based Bayesian decision theory in large scale Internet of Things

In addressing the growing problem of security of Internet of Things, we present, from a statistical decision point of view, a naval approach for trust-based access control using Bayesian decision theory. We build a trust model, TrustBayes which represents a trust level for identity management in IoT. TrustBayes model is be applied to address access control on uncertainty environment where identities are not known in advance. The model consists of EX (Experience), KN (Knowledge) and RC (Recommendation) values which is be obtained in measurement while a IoT device requests to access a resource. A decision will be taken based model parameters and be computed using Bayesian decision rules. To evaluate our a trust model, we do a statistical analysis and simulate it using OMNeT++ to investigate battery usage. The simulation result shows that the Bayesian decision theory approach for trust based access control guarantees scalability and it is energy efficient as increasing number of devices and not affecting the functioning and performance.