Neural network monitoring model for Internet of Things edge node security

Abstract

The invention relates to the technical field of Internet of Things, in particular to a neural network monitoring model for Internet of Things edge node security. The neural network monitoring model comprises a hybrid memory type neural network model. The neural network monitoring model is characterized in that the hybrid memory type neural network model comprises a sparse automatic encoder, a deepbelief network module, a classifier and a memory module. The model comprises the following steps: S1, the input information of the sparse automatic encoder is x, output is hW, b (x), and the objective function expression of the sparse automatic encoder is shown in the specification, wherein W and b represent a parameter and a bias term respectively, and J (W, b) represents an objective function of a conventional sparse automatic encoder. The problem that the classification judgment capability of the Internet of Things fog end gateway for the data flow security condition is insufficient when facing large-flow node data can be effectively solved, the dimensionality reduction capability can greatly reduce the dimensionality and complexity of data processing, the memory capability can effectively memorize the type and the condition of node security, and finally the accuracy and the high efficiency of real-time security monitoring are obviously improved.

Description

technical field [0001] The invention relates to the technical field of the Internet of Things, in particular to a neural network monitoring model for edge node security of the Internet of Things. Background technique [0002] The Internet of Things (IOT) has unleashed one of the biggest technological waves in recent decades. An estimated 50 billion devices will be interconnected by 2020, forming a network that potentially covers everything around us. The Internet of Things will affect billions of people across industrial, commercial, medical, automotive and other applications. Given its wide-ranging impact on individuals, institutions, and systems, security has risen to become the most critical component of any IoT system, and it is widely recognized that any responsible commercial IoT enterprise must truly grasp security. When assessing IoT network vulnerabilities, developers look to the most fundamental element—the edge node. As "things" in the Internet of Things, a lar...

AI Technical Summary

Problems solved by technology

Deep learning technology can learn and extract effective feature information from larger data sets. It has been proven that it can effectively monitor and analyze network data and security conditions. It is aimed at information security monitoring of Internet of Things (IoT) edge gateway nodes (fog terminals) , the current methods based on traditional machine learning have obvious deficiencies in indicators such as detection rate and accuracy rate

In addition, the existing information security monitoring technology is mainly aimed at the Internet and cannot meet the complex data security requirements of the distributed Internet of Things. Therefore, a neural network monitoring model for edge node security of the Internet of Things is needed to improve the above problems

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