文献类型：期刊文献

中文题名：大规模网络入侵下病毒扩散方向预测模型仿真

英文题名：Large-Scale Network Direction Prediction Model Simulation under the Invasion of the Spread of the Virus

作者：殷守军[1]

机构：[1]北京联合大学电子信息技术实验实训基地

第一机构：北京联合大学工科综合实验教学示范中心

年份：2016

卷号：0

期号：12

起止页码：274-277

中文期刊名：计算机仿真

外文期刊名：Computer Simulation

收录：CSTPCD;;北大核心:【北大核心2014】；CSCD:【CSCD_E2015_2016】；

语种：中文

中文关键词：大规模网络入侵;病毒扩散;预测模型

外文关键词：Large - scale network intrusion; Epidemic diffusion; Prediction model

摘要：在对网络入侵优化检测的研究中,由于大规模网络入侵下的病毒扩散方向呈现较强的多样性,扩散特征很多,需要大规模采集入侵特征才能预测出网络入侵病毒扩散方向,预测过程耗时,效率低。提出基于概论推理的大规模网络入侵下的病毒扩散方向预测建模方法。上述方法先融合于模糊推理对采集的网络病毒扩散方向特征进行模糊化分类处理,并选取概率较大网络入侵下的病毒扩散方向特征,并利用概率推理方法推理出网络入侵病毒从一个方向扩散到另一个方向的状态转移概率值,并组建大规模网络入侵下的病毒扩散方向预测模型。仿真证明,概论推理的大规模网络入侵下的病毒扩散方向预测建模可以提升网络入侵检测的精确度。
In the research of network intrusion optimized detection, there are many diffusion characteristics. The reason is that the epidemic diffusion direction has strong diversity. We need large - scale intrusion features to predict the network virus diffusion direction. The process takes time and has poor efficiency. In this paper, we proposed a model method based on probabilistic inference. Based on fuzzy inference, we classified the virus direction characteristic to fuzzification and chose the bigger probabilistic features to build the prediction model. The probabilistic inference method was adopted to obtain the probability for the virus from one direction to another. The simulation indicates that the model based on probabihstic inference can promote the accuracy for network intrusion testing.