Abstract:  In the process of predicting the intrusion intention of abnormal information in the hospital network, there is no dimension reduction processing for the hospital network data, resulting in a long prediction time and a low prediction accuracy. Therefore, an algorithm for predicting the intrusion intention of abnormal information in the hospital network based on the improved RBF(Radical Basis Function) model is proposed. The redundancy of hospital network data is removed and sorted through correlation analysis, and the sorted data attributes are selected by RBF multilayer neural network to complete the dimensionality reduction of hospital network data. According to the data preprocessing results, the Bayesian attack graph is constructed to obtain the potential network intrusion attack path. The alarm correlation strength is calculated in the path, the intrusion alarm evidence data is extracted, the information intrusion probability is determined through the monitoring of the alarm evidence, and the prediction result of the abnormal information intrusion intention of the hospital network is obtained. The experimental results show that the proposed method has high efficiency, high accuracy and good overall effect.

Key words:  ')">

 , Invasion of information, prediction of invasion intention, improving radical basis function(RBF) model, Bayesian attack chart, data reduction dimension