Accurate prediction of the trust relationship is the basis for trusted access and secure interaction between Internet of things nodes. To evaluate the degree of trust, a trust metric is assigned to every node depending on its several attributes. Normal nodes in Internet of things tend to suffer collusion attacks from malicious nodes; thus, the accuracy of the trust measurement decreases. To enhance the security of interaction between massive Internet of things nodes, we propose a multidimensional attribute trust model and a dynamic maintenance mechanism of a trusted group. The proposed model provides a reference for the selection and evaluation of node multidimensional attribute factors to adapt to different Internet of things application scenarios. The dispersion of satisfaction records is used to discover abnormal data and weaken its influence on the calculation of the node’s comprehensive trust evaluation. The construction of trusted groups provides an architectural foundation for the application of group signature that maintains low network overhead. The performance of multidimensional attribute trust model and dynamic maintenance mechanism is verified using Netlogo. Simulation results show the efficiency of the proposed model to classify the malicious nodes and honest nodes, as well as to build a trusted group that could ensure honest nodes occupy the major proportion.

信任关系的准确预测是物联网节点之间可信访问和安全交互的基础。为了评估信任度，将信任度量标准根据其几个属性分配给每个节点。物联网中的普通节点易于遭受恶意节点的串通攻击；因此，信任度测量的准确性降低。为了提高大规模物联网节点之间交互的安全性，我们提出了多维属性信任模型和可信组的动态维护机制。该模型为节点多维属性因子的选择和评估提供了参考，以适应不同的物联网应用场景。满意度记录的分散性用于发现异常数据，并减弱其对节点综合信任评估计算的影响。可信组的构建为组签名的应用程序提供了架构基础，从而保持了较低的网络开销。使用Netlogo验证了多维属性信任模型和动态维护机制的性能。仿真结果表明，该模型对恶意节点和诚实节点进行分类，以及建立可确保诚实节点占主要比例的可信组的有效性。使用Netlogo验证了多维属性信任模型和动态维护机制的性能。仿真结果表明，该模型对恶意节点和诚实节点进行分类，以及建立可确保诚实节点占主要比例的可信组的有效性。使用Netlogo验证了多维属性信任模型和动态维护机制的性能。仿真结果表明，该模型对恶意节点和诚实节点进行分类，以及建立可确保诚实节点占主要比例的可信组的有效性。