| Title | Security Situation Prediction Method of Industrial Control Network Based on Ant Colony-RBF Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wang, Xiaoyu, Han, Zhongshou, Yu, Rui |
| Conference Name | 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) |
| Keywords | AI, ant colony algorithm, artificial neural network, Artificial neural networks, Collaboration, Communication networks, cyber physical systems, game theory algorithms, Internet of Things, Market research, Metrics, Neural Network Security, Neural networks, policy-based governance, Prediction algorithms, Predictive models, pubcrawl, RBF neural network, resilience, Resiliency, security |
| Abstract | To understand the future trend of network security, the field of network security began to introduce the concept of NSSA(Network Security Situation Awareness). This paper implements the situation assessment model by using game theory algorithms to calculate the situation value of attack and defense behavior. After analyzing the ant colony algorithm and the RBF neural network, the defects of the RBF neural network are improved through the advantages of the ant colony algorithm, and the situation prediction model based on the ant colony-RBF neural network is realized. Finally, the model was verified experimentally. |
| DOI | 10.1109/ICBAIE52039.2021.9389864 |
| Citation Key | wang_security_2021 |
Security Situation Prediction Method of Industrial Control Network Based on Ant Colony-RBF Neural Network