Abstract
In recent years, the Internet of Things (IoT) has been an inseparable part of our lives. IoT is typically heterogeneous in nature and requires interconnection with different types of devices or "things". Being able to secure such a distributed environment is an onerous task. The heterogeneity of IoT, along with other factors, poses a challenge when it comes to securing communication between these devices. In this paper, we propose a novel IoT trust and reputation model that employs distributed probabilistic neural networks (PNNs) to classify trustworthy nodes from malicious ones. Our model tackles the cold start problem in IoT environments by predicting ratings for newly joined devices based on their characteristics and learns over time. Processing is completely distributed and is handled by the nodes themselves. This guarantees better availability, since there is no single point of failure. Moreover, our model can accommodate the various capabilities and types of IoT devices. Unlike other proposed models in the literature, our model provides different levels of security depending on the sensitivity of the data being transmitted.