Network security situation forecast based on improved general regression neural network
Hongyan Shen · Journal of North China Electric Power University · 2011
Focused on network security situation forecast(NSSF),a novel NSSF method based on improved general regression neural network(GRNN-PSO)was proposed,in order to improve forecast accuracy.With sliding time window(STW),all of the network security situation value(NSSV)of every discrete-time monitoring sites had been constructed into multi regression data sequence,which sequence was part of the linear correlation.In order to obtain NSSF model,the multi regression data sequence was trained as sample set by GRNN-PSO.In GRNN-PSO training process,it can overcome the deficiency of selecting difficultly GRNN's training parameters.Particle swarm optimization(PSO)was used to search the best training parameters.Finally,the experiments show that the NSSF method based on GRNN-PSO has better performance compared with the traditions.