Hospital communication security situation awareness method based on improved neural networks
Aiming at the problem of untimely awareness of hospital communication security situation,which can easily lead to damage of important information in hospital information systems,a hospital communication security situation awareness method based on improved neural networks is proposed.Using wavelet denoising based communication signals to remove noise and preserve key information,input into a hospital communication security situational awareness model based on an improved RBF neural network.Utilizing the flower pollination algorithm to train an improved RBF neural network.By using radial basis functions to perform nonlinear transformations on input data,the weights obtained are weighted and summed to obtain the predicted security situation of the current communication network signal.The experimental results show that the abnormal information in the hospital communication network using the method proposed in this article can be perceived within 1 second.
万方数据
维普
