A probabilistic-based approach towards trust evaluation using Poisson Hidden Markov Models and Bonus Malus Systems

In the paper, the uncertainty of trust is transformed into a probability vector denoting the probability distribution over possible trust levels of an entity that is hidden from observation but determined by its expected performance. We propose the use of Poisson Hidden Markov Models (PHMMs) for estimating the trust for entities in wireless environments, in which the Poisson distribution is used to describe the occurrences of behavioral patterns in peer-to-peer interactions. PHMMs allow us to explicitly consider an entity´s unobserved trustworthiness that influences it´s observed behaviors. As well, the hidden Markov process is associated with a Bonus-Malus System that is used to reduce the computational complexity of parameter estimations involved. An application of the model in the scenario of detection of probabilistic packet dropping attack has been investigated. The simulations demonstrate that the approach is capable of accurately estimating the (hidden) trust states probability distribution as well as the expected performance for the entities in the networks through their observed behaviors.