A Trustworthiness Evaluation Mechanism Based on Principles–Assumptions Model

With the development of 5G networks, quantum networks, Internet of Things, and smart cities, network information security and trust appear new challenges. False messages, malicious devices, communication vulnerabilities, etc., which threaten network security, the trust status of nodes need to be identified, and trust needs to be measured. Access control, data encryption, identity authentication, electronic signature, etc., are a series of network security technologies, however, they are powerless in the face of the large and complex network information. This article proposes a kind of model based on machine learning, which utilizes the miscellaneous network information of nodes, to be applied to the trustworthiness evaluation, trust management of nodes, and detection of malicious nodes. First, this article introduces the designed principle–assumptions model, and its parameter optimization mechanism, i.e., the gradient descent algorithm. Second, this article carries on the simulation experiment and analysis of the model, including the three trust evaluation mechanisms, the generation of the data set, the training, testing, and evaluation of the model, the resistance analysis of malicious attacks, and the comparison-based performance evaluation. The experimental results show that under malicious network attacks, when the number of malicious nodes is 45%, the F-score of the principle–assumptions model is more than 95%. The ability of the model to detect malicious nodes is excellent and can be applied to the trust evaluation and management of network nodes.