物联网节点动态行为信任度评估方法仿真

在物联网中节点恶意行为会促使节点的信任度大幅降低,如何确保高信任度并加强网络安全性已经成为首要问题,提出一种物联网节点动态行为信任度评估方法。首先依据多实体贝叶斯建立信任模型,可以控制恶意节点对物联网的攻击和入侵;采用贡献资源数值权重来抑制自私节点,并对其初始化处理;通过对信任的传递与合成计算出推荐信任值,能够减少运算复杂度;并利用综合信任度调高可信任的阈值,最后根据节点间直接的交互记录,来完成信任度的评估。仿真结果表明,所提方法可以较好地识别恶意节点和抑制恶意推荐行为,能够保证信任评估的有效性、可靠性以及安全性。

Short-term Traffic Prediction Simulation of Multi-scale Network Based on Discrete Variables

The malicious behavior of nodes in the Internet of Things will greatly reduce trust level.This article presented a method to evaluate the trust level of dynamic behavior of Internet of Things(IOT)nodes.Firstly,a trust model based on multi-entity Bayesian network was built to control the attacks and intrusions of malicious nodes on the Internet of Things.Then,the weight of contribution resource was used to restrain selfish nodes and initialize these nodes.Moreover,the recommended trust value was calculated by transferring and synthesizing the trust,so that the computing complexity was reduced.The comprehensive trust degree was used to raise the credible threshold.Finally,the evaluation of trust level was completed according to the direct interaction record between nodes.Simulation results show that the proposed algorithm can identify malicious nodes and suppress malicious recommendation behavior.Meanwhile,this method can guarantee the effectiveness,reliability and security of trust evaluation.