scholarly journals The Key Technology of Computer Network Vulnerability Assessment Based on Neural Network

2020 ◽  
Author(s):  
Shaoqiang Wang
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Bp Neural Network ◽  
Vulnerability Assessment ◽  
Computer Network ◽  
Network Clustering ◽  
Network Vulnerability ◽  
Large Matrix ◽  
Training Samples ◽  
Lm Algorithm

Abstract With the wide application of computer network, network security has attracted more and more attention. The main reason why all kinds of attacks on the network can pose a great threat to the network security is the vulnerability of the computer network system itself. Introducing neural network technology into computer network vulnerability assessment can give full play to the advantages of neural network in network vulnerability assessment. The purpose of this article is by organizing feature map neural network and the combination of multilayer feedforward neural network, the training samples using SOM neural network clustering, the result of clustering are added to the original training samples and set a certain weight, based on the weighted iterative update ceaselessly, in order to improve the convergence speed of BP neural network. On the BP neural network algorithm for LM algorithm was improved, the large matrix inversion in the LM algorithm using the parallel algorithm method is improved for solving system of linear equations, and use of computer network vulnerability assessment as the computer simulation and analysis on the actual example, design a set of computer network vulnerability assessment scheme, finally the vulnerability is lower than 0.75, which is beneficial to research on related theory and application to provide the reference and help.

scholarly journals Key Technology of Computer Network Vulnerability Assessment Based on Neural Network

2020 ◽  
Author(s):  
Shaoqiang Wang
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Bp Neural Network ◽  
Vulnerability Assessment ◽  
Computer Network ◽  
Network Clustering ◽  
Network Vulnerability ◽  
Large Matrix ◽  
Training Samples ◽  
Lm Algorithm

Abstract With the wide application of computer network, network security has attracted more and more attention. The main reason why all kinds of attacks on the network can pose a great threat to the network security is the vulnerability of the computer network system itself. Introducing neural network technology into computer network vulnerability assessment can give full play to the advantages of neural network in network vulnerability assessment. The purpose of this article is by organizing feature map neural network and the combination of multilayer feedforward neural network, the training samples using SOM neural network clustering, the result of clustering are added to the original training samples and set a certain weight, based on the weighted iterative update ceaselessly, in order to improve the convergence speed of BP neural network. On the BP neural network algorithm for LM algorithm was improved, the large matrix inversion in the LM algorithm using the parallel algorithm method is improved for solving system of linear equations, and use of computer network vulnerability assessment as the computer simulation and analysis on the actual example, design a set of computer network vulnerability assessment scheme, finally the vulnerability is lower than 0.75, which is beneficial to research on related theory and application to provide the reference and help.

scholarly journals The key technology of computer network vulnerability assessment based on neural network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shaoqiang Wang
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Bp Neural Network ◽  
Vulnerability Assessment ◽  
Computer Network ◽  
Network Clustering ◽  
Network Vulnerability ◽  
Large Matrix ◽  
Training Samples ◽  
Lm Algorithm

Abstract With the wide application of computer network, network security has attracted more and more attention. The main reason why all kinds of attacks on the network can pose a great threat to the network security is the vulnerability of the computer network system itself. Introducing neural network technology into computer network vulnerability assessment can give full play to the advantages of neural network in network vulnerability assessment. The purpose of this article is by organizing feature map neural network, and the combination of multilayer feedforward neural network, the training samples using SOM neural network clustering, the result of clustering are added to the original training samples and set a certain weight, based on the weighted iterative update ceaselessly, in order to improve the convergence speed of BP neural network. On the BP neural network, algorithm for LM algorithm was improved, the large matrix inversion in the LM algorithm using the parallel algorithm method is improved for solving system of linear equations, and use of computer network vulnerability assessment as the computer simulation and analysis on the actual example designs a set of computer network vulnerability assessment scheme, finally the vulnerability is lower than 0.75, which is beneficial to research on related theory and application to provide the reference and help.

Researches on the Network Security Evaluation Method Based on BP Neural Network

2014 ◽  
Vol 686 ◽  
pp. 470-473 ◽  
Author(s):  
Yi Bin Zhang ◽  
Ze Quan Yan
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Computer Network ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Security Evaluation ◽  
Computer Network Security ◽  
Neural Network Algorithm

This paper first describes the basic theory of BP neural network algorithm, defects and improved methods, establishes a computer network security evaluation index system, explores the computer network security evaluation method based on BP neural network, and has designed to build the evaluation model, and shows that the method is feasible through the MATLAB simulation experiments.

Prediction and analysis of College Students’ mental health based on BP neural network

2020 ◽  
pp. 1-7
Author(s):  
Tang Yushou Su Jianhuan
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
College Students ◽  
Bp Neural Network ◽  
Structural Characteristics ◽  
Prediction System ◽  
The Neural Network ◽  
Data Processing Method ◽  
Training Samples ◽  
The Mathematical Model

College Students’ mental health is an important part of higher education, so the current research and prediction of College Students’ mental health are of great significance to better solve the problem of College Students’ mental health. Taking a local university as an example, the data from 2011 to 2019 are selected and analyzed. The normalized data processing method is used to assign weights to 11 kinds of factors that affect the health of college students. The training samples of a neural network are selected, and the structural characteristics of the neural network and the artificial neural network toolbox of MATLAB are used to establish the BP based model the mathematical model of the prediction system of College Students’ mental health based on neural network. The results show that the error between the predicted value and the measured value is only 0.88%. On this basis, this paper uses the model to predict the weight of the influencing factors of the mental health status of college students in a local university in 2020 and analyzes the causes of the prediction results, to provide the basis for the current mental health education of college students.

Intelligent Identification of Flow Regime Based on Improved BP and IMFs

2013 ◽  
Vol 816-817 ◽  
pp. 471-474
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Flow Regime ◽  
Bp Neural Network ◽  
Flow Regimes ◽  
Characteristic Vector ◽  
Soft Sensing ◽  
Mode Decomposition ◽  
Training Samples ◽  
The Difference ◽  
Regime Characteristic

Aimed at the characteristic of nonlinear and non-stationary of pressure drop, in this article a flow regime identification soft sensing method using Hilbert-Huang transformation combined with improved BP neural network is put forward. The method analyzes the intrinsic mode function (IMFs) obtained after the empirical mode decomposition (EMD), then extracts IMF energy as the characteristic vector of an improved BP neural network with self-adapted learning ratio. Learning form training samples, the network could accomplish the objective identification of the unknown flow regimes. The simulated results showed that the flow regime characteristic vector which was obtained by IMFs could reflect the difference between various flow regimes and the recognition possibility of the network could reached up to about 95 percent. This study provided a new way to identify flow regime by soft sensing.

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