Modeling and Analysis of Network Security Situation Prediction Based on Covariance Likelihood Neural

被引：0
|
作者：
Tang, Chenghua [1 ]
Wang, Xin [1 ]
Zhang, Reixia [1 ]
Xie, Yi [2 ]
机构：
[1] Guilin Univ Elect Technol, Sch Comp Sci & Engn, Guilin 541004, Peoples R China
[2] Sun YatSen Univ, Dept Informat Sci & Technol, Guangzhou 510275, Peoples R China
来源：
BIO-INSPIRED COMPUTING AND APPLICATIONS | 2012年 / 6840卷
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
network security; situation prediction; covariance; neural;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Security situation is the premise of network security warning. For lack of self-learning on situation data processing in existing complex network, a modeling and analysis of network security situation prediction based on covariance likelihood neural is presented. With the introduction of the error covariance likelihood function, and considering the impact of sample noise, the network security situation prediction model using the situation sequences as input sequences, and in the back-propagation to achieve the parameters adjustment. Results show that the model can take advantage of the relationship characteristics between the complexity and efficiency in complex neural networks, and the method has good performance of situation prediction.
引用
收藏
页码：71 / +
页数：2
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