Network security threat assessment method based on unsupervised generation reasoning

YANG Hongyu; WANG Fengyan; L?Weili

doi:10.16511/j.cnki.qhdxxb.2020.22.005

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Journal of Tsinghua University(Science and Technology) ›› 2020, Vol. 60 ›› Issue (6) : 474-484. DOI: 10.16511/j.cnki.qhdxxb.2020.22.005

SPECIAL SECTION: TRUSTED COMPUTING AND INFORMATION SECURITY

Network security threat assessment method based on unsupervised generation reasoning

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Abstract

Supervised network data modeling based on data category tags is computationally expensive, inefficient and requires long time for network threat assessments. This paper presents a network security threat assessment method based on unsupervised generation reasoning. A variant auto encoder - generative adversarial network (VAE-GAN) model is designed with training data set containing only normal network traffic input to the network collection layer of the VAE-GAN while monitoring the reconstruction error of each layer network output and a 3-layer variant auto encoder of the output layer is used to train the reconstruction error with a test data set used for group threat testing while monitoring the threat occurrence probability for each group of tests. Finally, the severities of the network security threats are determined based on the threat occurrence probability with a threat situation impact factor used to calculate the threat level to quantify the network security threat. Simulations show that this method more intuitively evaluates the overall network security threat than back propagation (BP) and radical basis function (RBF) methods and more effectively characterizes the network threat.

Key words

unsupervised generation reasoning / variant auto encoder-generative adversarial network (VAE-GAN) / threat probability / threat situation assessment

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YANG Hongyu, WANG Fengyan, L�Weili. Network security threat assessment method based on unsupervised generation reasoning[J]. Journal of Tsinghua University(Science and Technology). 2020, 60(6): 474-484 https://doi.org/10.16511/j.cnki.qhdxxb.2020.22.005

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