| Title | A Security Situation Prediction Method Based on Improved Deep Belief Network |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, Y., Zhou, Y., Hu, K., Sun, N., Ke, K. |
| Conference Name | 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT |
| Keywords | active defense, belief networks, Clustering algorithms, Communication networks, Cyber-physical systems, deep belief network, electricity information network, Prediction algorithms, Predictive models, pubcrawl, Resiliency, security, security situation prediction, Sociology, Statistics |
| Abstract | With the rapid development of smart grids and the continuous deepening of informatization, while realizing remote telemetry and remote control of massive data-based grid operation, electricity information network security problems have become more serious and prominent. A method for electricity information network security situation prediction method based on improved deep belief network is proposed in this paper. Firstly, the affinity propagation clustering algorithm is used to determine the depth of the deep belief network and the number of hidden layer nodes based on sample parameters. Secondly, continuously adjust the scaling factor and crossover probability in the differential evolution algorithm according to the population similarity. Finally, a chaotic search method is used to perform a second search for the best individuals and similarity centers of each generation of the population. Simulation experiments show that the proposed algorithm not only enhances the generalization ability of electricity information network security situation prediction, but also has higher prediction accuracy. |
| DOI | 10.1109/ICCASIT50869.2020.9368850 |
| Citation Key | li_security_2020 |
A Security Situation Prediction Method Based on Improved Deep Belief Network