| Title | Research of Computer Network Security Evaluation Based on Backpropagation Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Guan, Chengli, Yang, Yue |
| Conference Name | 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) |
| Keywords | Backpropagation, backpropagation neural network, Biological neural networks, BP Neural Network, Collaboration, Communication networks, computer network security, computer network security evaluation, computer networks, computer viruses, evaluation, Indexes, loopholes, Mathematical model, Metrics, neural nets, Neural Network Security, policy-based governance, prediction, pubcrawl, security, Virus |
| Abstract | In recent years, due to the invasion of virus and loopholes, computer networks in colleges and universities have caused great adverse effects on schools, teachers and students. In order to improve the accuracy of computer network security evaluation, Back Propagation (BP) neural network was trained and built. The evaluation index and target expectations have been determined based on the expert system, with 15 secondary evaluation index values taken as input layer parameters, and the computer network security evaluation level values taken as output layer parameter. All data were divided into learning sample sets and forecasting sample sets. The results showed that the designed BP neural network exhibited a fast convergence speed and the system error was 0.000999654. Furthermore, the predictive values of the network were in good agreement with the experimental results, and the correlation coefficient was 0.98723. These results indicated that the network had an excellent training accuracy and generalization ability, which effectively reflected the performance of the system for the computer network security evaluation. |
| DOI | 10.1109/ICPICS47731.2019.8942585 |
| Citation Key | guan_research_2019 |
Research of Computer Network Security Evaluation Based on Backpropagation Neural Network