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An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things
Joint Authors
Yang, Hongyu
Zhang, Jiyong
Zeng, Renyun
Wang, Fengyan
Xu, Guangquan
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-28
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Yang, Hongyu& Zeng, Renyun& Wang, Fengyan& Xu, Guangquan& Zhang, Jiyong. 2020. An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1208495
Modern Language Association (MLA)
Yang, Hongyu…[et al.]. An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things. Security and Communication Networks No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1208495
American Medical Association (AMA)
Yang, Hongyu& Zeng, Renyun& Wang, Fengyan& Xu, Guangquan& Zhang, Jiyong. An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1208495
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208495