Context Based Trust Formation Using Direct UserExperience in the Internet of Things(IoT) The Internet of Things (IoT) is converting the real world into cyber network. Security becomes a challenging task in IoT. For that reason, trust is used as an essential security measure in IoT devices. Trust Management System (TMS) is designed to mitigate the risks of uncertainty between the communicating parties. Direct interactions with servers based on the user experiences in various contexts can be considered as a solution for trust formation. The aim of this paper is to develop a context based trust management system that uses direct experiences of users while being connected with servers in various contexts. The technique is based on the Naive Bayesian classification method. The context based direct user experiences are filtered, classified and then used as a trust formation procedure to formulate and update trust with the passage of time. The proposed protocol has been evaluated using web services datasets and results show that trust formation considering contextual information provides a promising solution.

A novel reward and penalty trust evaluation model based on confidence interval using Petri Net Abstract   Trust brings a novel means to improve the security of entities. Entities potentially initiate interactions with each other without having prior contacts. These interactions can either be formed directly between two entities or indirect through the recommendation of their acquaintances or third parties. In this paper, we present a novel trust model according to historical interaction between entities so that, the relations between entities are modeled based on four types (i.e. completely successful, completely unsuccessful, relatively successful and relatively unsuccessful) of their prior interactions. We also consider the reward and penalty for encouraging honest behaviors and preventing malicious behaviors, respectively. Unlike other proposed models, instead of taking into account the fixed amount of interactions for the experience level, in this paper, to calculate more accurate we have used the confidence interval to determine the level of experience. Also, to resist selfish and malicious behavior, the recommendation trust value for an entity computed by calculating the similarityweighted recommendations of the entities that have interacted with him according to adjusted cosine similar function. In addition, we have developed the Petri Net model for design, analysis, and performance evaluation of the proposed model. By performing empirical evaluations, we have demonstrated that various scenarios can be better explained by our proposed reward and penalty trust model based on the confidence interval (RTMC) rather than the commonly used classical models. Simulation results and theoretical analysis proved that the RTMC promotes interaction between entities with containment capability in synergies cheating.