| Title | Research on Computer Network Security Evaluation Method Based on Levenberg-Marquardt Algorithms |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Wang, Dongqi, Shuai, Xuanyue, Hu, Xueqiong, Zhu, Li |
| Conference Name | 2019 International Conference on Communications, Information System and Computer Engineering (CISCE) |
| Date Published | jul |
| Keywords | Backpropagation, BP Neural Network, Collaboration, Communication networks, computer network security, computer network security evaluation method, computer networks, evaluation, Indexes, Levenberg-Marquardt algorithms, LM-BP algorithm, local minimum point, Metrics, neural nets, Neural Network Security, Neural networks, policy-based governance, pubcrawl, Safety, security, Training, training process |
| Abstract | As we all know, computer network security evaluation is an important link in the field of network security. Traditional computer network security evaluation methods use BP neural network combined with network security standards to train and simulate. However, because BP neural network is easy to fall into local minimum point in the training process, the evalu-ation results are often inaccurate. In this paper, the LM (Levenberg-Marquard) algorithm is used to optimize the BP neural network. The LM-BP algorithm is constructed and applied to the computer network security evaluation. The results show that compared with the traditional evaluation algorithm, the optimized neural network has the advantages of fast running speed and accurate evaluation results. |
| DOI | 10.1109/CISCE.2019.00094 |
| Citation Key | wang_research_2019 |
Research on Computer Network Security Evaluation Method Based on Levenberg-Marquardt Algorithms