改进RBF模型的医院网络异常信息入侵意图预测

Prediction Algorithm Based on Improved RBF Model for Hospital Network Abnormal of Information Intrusion Intentions

由于在医院网络异常信息入侵意图预测过程中,没有对医院网络数据降维处理,导致预测时间较长、预测准确率较低,为此提出基于改进RBF(Radical Basis Function)模型的医院网络异常信息入侵意图预测算法。通过相关性分析去除医院网络数据冗余并排序,采用RBF多层神经网络对排序后的数据属性进行选择,完成医院网络数据降维处理;根据数据预处理结果,构建贝叶斯攻击图,获取网络潜在入侵攻击路径;在该路径中计算警报关联强度,提取入侵警报证据数据,通过警报证据的监测判断信息入侵概率,获得医院网络的异常信息入侵意图的预测结果。实验结果表明,所提方法的网络异常信息入侵意图预测效率快、准确率高、整体效果好。

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.