A Trustworthiness Sequence Prediction Scheme Based on Neural Networks and Mathematical Calculations

Trust management of network nodes can protect the security of IoT, e.g., detection of false messages and malicious nodes, optimizing IoT services, etc. Trust evaluation and trust prediction are the core contents of trust management, which are used to evaluate the trustworthiness of nodes. However, the lack of trust-related data will lead to the failure of the activation of trust evaluation mechanisms. The update of trust needs to be solved by trust prediction schemes. This article designs a trust prediction scheme, utilizing neural networks and mathematical calculations to realize the classification and prediction of trust iterations. First of all, this article gives the detailed architecture of the designed scheme, including the neural network model and data preprocessing. Then analyze the theoretical calculations of related variables in trust iterations, e.g., mean value, variance, etc. Finally, utilizing simulation experiments to verify the performance of the designed trust prediction scheme, including conserved quantity in trust iterations, data set preprocessing, testing of the scheme, malicious attack resistance analysis, etc. The experiments prove that the scheme can resist related malicious attacks, e.g., data tampering attacks, etc. Moreover, the residual error of trust prediction is not more than 0.0075, which is better than the existing trust prediction schemes.