面向网络实时风险预测的马尔可夫时变模型

摘要

In order to predict the likelihood of network risks accurately and in real-time, and help the administrators to manage network risks effectively, a Time-Varying Markov Model (TVMM ) for real-time risk probability prediction was proposed. A real-time risk probability prediction method, which is able to predict the probability of network risk in future exactly with a real-time-updating-state probability transition matrix of TVMM, was presented. Combined with the theory of feature extraction and statistical learning , the model was used to calculate the risk probability of the network at different risk level in network attack environment. The result shows that TVMM has higher real-time, objectivity and accuracy than the traditional Markov model.%为了能实时准确地预测网络风险发生的可能性,帮助管理员对网络中的风险进行有效的管理,本文提出了一种用于实时风险概率预测的马尔可夫时变模型.基于此模型,给出了风险概率预测方法,通过实时更新模型中的状态概率转移矩阵,来预测未来时刻网络的风险概率.仿真实验将此模型应用于网络攻击环境下,结合特征提取、统计学习,来预测网络在不同风险等级下的概率.同传统的马尔可夫预测模型相比,该模型具有更高的实时性、客观性和准确性.