Fast Detection Model of Untrusted Nodes in Fog Computing Based on CGAN

Abstract

Aiming at the problem that the existing network nodes can’t detect the untrusted nodes quickly. In this paper, a condition generated fast untrusted node detection model (FUNM) for enemy network (CGAN) is proposed, which improves the detection efficiency greatly with high accuracy. Different from the traditional generative adversary network (GAN), this model limits the degree of freedom of convergence of generator and discriminator by adding constraints, so as to speed up the convergence and detect the untrusted nodes accurately and quickly. The experimental results show that the CGAN based on fast detection model of untrusted nodes has obvious advantages in terms of accuracy, false alarm rate and real rate, which provides great help for the security of edge networks.