Paper Info
Title
An Unsupervised Learning-Based Network Threat Situation Assessment Model For Internet Of Things
Abstract
With the wide application of network technology, the Internet of Things (IoT) systems are facing the increasingly serious situation of network threats; 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 and low efficiency in the network threat situation assessment, this paper proposes a network threat situation assessment model based on unsupervised learning for IoT. Firstly, we combine the encoder of variational autoencoder (VAE) and the discriminator of generative adversarial networks (GAN) to form the V-G network. Then, we obtain the reconstruction error of each layer network by training the network collection layer of the V-G network with normal network traffic. Besides, we conduct the reconstruction error learning by the 3-layer variational autoencoder of the output layer and calculate the abnormal threshold of the training. Moreover, we carry out the group threat testing with the test dataset containing abnormal network traffic and calculate the threat probability of each test group. Finally, we 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.
Year
DOI
Venue
2020
10.1155/2020/6656066
SECURITY AND COMMUNICATION NETWORKS
DocType
Volume
ISSN
Journal
2020
1939-0114
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Hongyu Yang1194.73
Renyun Zeng200.68
Fengyan Wang310.70
Guangquan Xu417133.20
Jiyong Zhang515621.11