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.

Research on Application of Neural Network in Computer Network Security Evaluation

2014 ◽  
Vol 8 (1) ◽  
pp. 766-771
Author(s):  
Shujuan Jin
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Evaluation System ◽  
Evaluation Method ◽  
Computer Network ◽  
Evaluation Model ◽  
Indicator System ◽  
Security Evaluation ◽  
Model Based ◽  
Computer Network Security

Purpose: discuss the role of the neural network (NN) theory in the computer network security evaluation. Method: propose three-level and four-class indicator system suitable for network security evaluation, establish the network security evaluation system model based on NN, optimize the NN model by using the particle swarm, collect 100- group data on the computer network security evaluation of different scales via expert scoring, and normalize them; Result: the evaluation model based on NN is simple and practicable to network security evaluation and can eliminate disturbance of the subjective factors of the human being. The simulation results indicate that the system can reduce relative output error and improve correctness rate of evaluation. Conclusion: The NN model is very valuable in research on the computer network security evaluation system, which can offset weaknesses of the past evaluation methods to some extent, improve precision of the evaluation results, and provide reference to prediction and control of the network security problems in future.

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