Network security threat assessment method based on unsupervised generation reasoning
YANG Hongyu1, WANG Fengyan1, L�Weili2
1. School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China; 2. Pipeline Changchun Transmission and Oil Company, China National Petroleum Corporation, Changchun 130000, China
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.
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