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.

Information Security Monitoring and Management Method Based on Big Data in the Internet of Things Environment

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A Pythagorean fuzzy Petri net based security assessment model for civil aviation airport security inspection information system

“…In recent years, the security situation assessment models have been effective investigated, [19][20][21][22][23][24][25][26][27] such as models based on mathematical methods, knowledge reasoning methods, and deep learning methods.…”

“…Based on deep learning methods, [21][22][23][24] as the number of deep learning network neurons continued to increase in recent years, it can represent more complex models. And after training with a large amount of data, more accurate results were obtained.…”

“…According to (21), combined with the transition credibility vector U, calculate the Pythagorean fuzzy matrix Z k of transition output which is as follows:…”

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