收藏本站
《第36届中国控制会议论文集(C)》2017年
收藏 | 手机打开
二维码
手机客户端打开本文

On Network Security Situation Prediction Based on RBF Neural Network

Yang Jiang  Cheng-hai Li  Li-shan Yu  Bo Bao  
【摘要】:Aiming at the problem of network security situation prediction,this paper studies the prediction method based on RBF neural network.Through training the RBF neural network,find out the nonlinear mapping relationship between the front N data and the subsequent Mdata,and then adjust the value of N to explore the different prediction results.The simulation result shows that the proposed method can accurately predict the results of the situation.Compared with the prediction based on BP neural network,the proposed method has a more accurate prediction and a faster convergence speed,a better prediction effect achieved.
【作者单位】:College of Air and Missile Defense,Air Force Engineering University
【分类号】:TP183;TP393.08
【正文快照】:
1 Introduction In the increasingly complex network environment and the dynamic changes of attack scenarios,acquiring and understanding the changes and trends of the future network security situation,can provide guidance for security administrator's

【相似文献】
中国期刊全文数据库 前10条
1 ;Improvement of System Dynamic Precision by Dynamic Prediction[J];Semiconductor Photonics and Technology;2000年01期
2 ;Remote Sensing and GIS Application to Superlarge Mineral Deposits Prediction in Western Slope of Great Xing'an Mountains, China[J];Journal of Geoscientific Research in Northeast Asia;2000年01期
3 汪凌云,郑廷顺,刘雪峰,黄光杰;Prediction of flow stresses at high temperatures with artificial neural networks[J];Transactions of Nonferrous Metals Society of China;2001年02期
4 ;Prediction Method of Vessel Maintenance Outlay Based on the BP Neural Network[J];Journal of Systems Engineering and Electronics;2002年03期
5 Kazuhiko Sasagawa,Masataka Hasegawa,Masumi Saka,Hiroyuki Abé;Prediction of electromigration failure in passivated polycrystalline line[J];光学精密工程;2003年02期
6 ;Application of GIS in Mineral Resource Prediction of Synthetic Information[J];Journal of China University of Geosciences;2003年03期
7 ;Blind Multipath Identification: A Weighted Linear Prediction Approach[J];The Journal of China Universities of Posts and Telecommunications;2003年04期
8 王雪松,程玉虎,彭光正;Modeling and Simulation of Time Series Prediction Based on Dynamic Neural Network[J];Journal of Beijing Institute of Technology(English Edition);2004年02期
9 ;Research on Optimize Prediction Model and Algorithm about Chaotic Time Series[J];Wuhan University Journal of Natural Sciences;2004年05期
10 王琪,朱杰,于波;A Comparative Study of Three Machine Learning Methods for Software Fault Prediction[J];Journal of Shanghai Jiaotong University;2005年02期
中国重要会议论文全文数据库 前10条
1 纪志梁;;Prediction of Adverse Drug Reactions via Gene Regulation Network[A];中国化学会第27届学术年会第15分会场摘要集[C];2010年
2 程飞雄;李卫华;唐赟;;Prediction of Chemical-Protein Interactions via Weighted and Unweighted Network-Based Inference Methods[A];中国化学会第28届学术年会第14分会场摘要集[C];2012年
3 ;Link Prediction[A];第六届全国网络科学论坛暨第二届全国混沌应用研讨会论文集[C];2010年
4 ;Application of Rough Sets and Artificial Neural Network in Core Enterprise Performance Prediction[A];第二十七届中国控制会议论文集[C];2008年
5 程飞雄;黄瑾;李卫华;唐赟;;Prediction of Drug-Target Interaction and Drug Repositioning[A];2011年全国药物化学学术会议——药物的源头创新论文摘要集[C];2011年
6 程飞雄;李卫华;唐赟;;Computational Prediction of Chemical-Protein Interactions:Multitarget-QSAR versus Computational Chemogenomic Methods[A];中国化学会第28届学术年会第14分会场摘要集[C];2012年
7 ;Estimation and Prediction of Bioconcentration Factors of Nonionic Organic Compounds in Fish by iectronegativity-distance Vector[A];中国化学会第五届全国结构化学学术会议论文摘要集[C];2007年
8 ;In Silico Prediction of Spleen Tyrosine Kinase Inhibitors by Correlative Molecular Descriptors and Machine Learning Approaches[A];中国化学会第28届学术年会第14分会场摘要集[C];2012年
9 ;CO_2 Adsorptions of N-rich Metal-organic Frameworks:Theoretical Prediction and Experimental Studies[A];中国化学会第六届全国结构化学学术会议论文摘要[C];2012年
10 Gao Shuang;Dong Lei;Liao Xiaozhong;Gao Zhigang;Gao Yang;;Wind Power Prediction based on Multipositon NWP with Rough Set Theory[A];第25届中国控制与决策会议论文集[C];2013年
 快捷付款方式  订购知网充值卡  订购热线  帮助中心
  • 400-819-9993
  • 010-62791813
  • 010-62985026