A Variational Generative Network Based Network Threat Situation Assessment

Abstract

In recent years, with the problem of network security is getting worse, the network threat situation assessment becomes an important approach to solve these problems. Aiming at the traditional methods based on data category tag that has high modeling cost, low efficiency, and a long period in the network threat situation assessment, this paper proposes a Variational-Generative (V-G) network assessment method. Firstly, we design the V-G network which is composed of VAE’s encoder and GAN’s discriminator and obtain the reconstruction error of each layer network by training the network collection layer of the V-G network with normal network traffic. Then, conduct the reconstruction error learning by the 3-layer variational autoencoder of the output layer and calculate the abnormal threshold of the training. Moreover, carry out the group threat testing with the test dataset contains abnormal network traffic and calculate the threat probability of each test group. Finally, obtain the Threat Situation Value (TSV) according to the threat probability and the threat impact. The simulation results show that compared with the other methods, this proposed method can evaluate the overall situation of network security threat more intuitively and has a stronger characterization ability for network threats.