| Title | Network Information Security Pipeline Based on Grey Relational Cluster and Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Gong, Jianhu |
| Conference Name | 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) |
| Keywords | artificial neural network, Artificial neural networks, Collaboration, Complexity theory, Computational modeling, cyber physical systems, data mining, Grey Relational Cluster, information entropy, information science, Information security, Intrusion detection, machine learning, Metrics, Neural Network Security, Neural networks, Pipelines, policy-based governance, pubcrawl, resilience, Resiliency |
| Abstract | Network information security pipeline based on the grey relational cluster and neural networks is designed and implemented in this paper. This method is based on the principle that the optimal selected feature set must contain the feature with the highest information entropy gain to the data set category. First, the feature with the largest information gain is selected from all features as the search starting point, and then the sample data set classification mark is fully considered. For the better performance, the neural networks are considered. The network learning ability is directly determined by its complexity. The learning of general complex problems and large sample data will bring about a core dramatic increase in network scale. The proposed model is validated through the simulation. |
| DOI | 10.1109/ICCMC51019.2021.9418311 |
| Citation Key | gong_network_2021 |
Network Information Security Pipeline Based on Grey Relational Cluster and Neural Networks