| Title | Network Security Situation Prediction Model Based on Multi-Swarm Chaotic Particle Optimization and Optimized Grey Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Zhang, Shaobo, Shen, Yongjun, Zhang, Guidong |
| Conference Name | 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS) |
| Keywords | Collaboration, Communication networks, computer network security, convergence speed, cyber security, Data models, grey neural network, grey systems, mathematical expressions, Mathematical model, Metrics, MSCPO, multiswarm chaotic particle optimization, network security incidents, network security protection, network security situation prediction model, network situation, network situation value, neural nets, Neural Network Security, Neural networks, nonlinear mapping, Optimization, optimized grey neural network, particle swarm optimisation, policy-based governance, prediction model, Predictive models, pubcrawl, resilience, Resiliency, security |
| Abstract | Network situation value is an important index to measure network security. Establishing an effective network situation prediction model can prevent the occurrence of network security incidents, and plays an important role in network security protection. Through the understanding and analysis of the network security situation, we can see that there are many factors affecting the network security situation, and the relationship between these factors is complex., it is difficult to establish more accurate mathematical expressions to describe the network situation. Therefore, this paper uses the grey neural network as the prediction model, but because the convergence speed of the grey neural network is very fast, the network is easy to fall into local optimum, and the parameters can not be further modified, so the Multi-Swarm Chaotic Particle Optimization (MSCPO)is used to optimize the key parameters of the grey neural network. By establishing the nonlinear mapping relationship between the influencing factors and the network security situation, the network situation can be predicted and protected. |
| DOI | 10.1109/ICSESS.2018.8663841 |
| Citation Key | zhang_network_2018 |
Network Security Situation Prediction Model Based on Multi-Swarm Chaotic Particle Optimization and Optimized Grey Neural Network