基于贝叶斯网络的信息安全预警模型

Information Security Early Warning Model Based on Bayesian Network

信息安全预警模型受到病毒入侵、网络攻击手段的影响,导致预警的效果变差。为了增强信息安全预警效果,提出了基于贝叶斯网络的信息安全预警模型。通过对攻击网络信息样本的聚类中心进行计算,扩展处理径向基函数的宽度,对攻击网络信息样本进行分类,实现攻击网络信息样本的自适应分类,根据贝叶斯网络模型结构,建立网络输入输出误差函数,计算信息安全预警指标量化值,对信息安全预警指标进行标准化处理,确定预警目标函数,通过调整贝叶斯网络模型参数,实现对贝叶斯网络模型参数的调整,从而实现对网络信息安全的预警。实验结果表明,基于贝叶斯网络的信息安全预警模型不仅能有效地缩短网络信息数据识别时间,而且能有效地获得最佳适应值,最终评价信息安全性能。

Due to the influence of virus invasion and network attack,the effect of information security early warning becomes worse.In order to enhance the effect of information security early warning,an information security early warning model is proposed based on Bayesian network.By calculating the clustering center of attack network information samples,expanding the width of processing radial basis function,the attack network information samples are classified,and the adaptive classification of attack network information samples is realized.According to the Bayesian network model structure,the network input and output error function is established,the quantitative value of information security early warning index is calculated,and the information security early warning index is marked.The parameters of Bayesian network model can be adjusted,so as to realize the early warning of network information security.The experimental results show that the information security early warning model based on Bayesian network can not only effectively shorten the network information data recognition time,but also effectively obtain the best fitness value,and finally evaluate the information security performance.