东南大学学报（英文版）2018，Vol.34Issue(3)：288-294,7.DOI:10.3969/j.issn.1003-7985.2018.03.002
一种可生存系统生存态势的可识别性模型
A recognition model of survival situations for survivable systems
摘要
Abstract
Due to the lack of pre-recognition and postprediction in existing survivable systems,a recognition model of survival situations for survivable systems is proposed.First,the survival situation data is clustered into several survival clusters with different service levels based on the Ward method,and then the survival clusters are classified and recognized by means of the error-eliminating decision-making method,which can realize the pre-recognition of the system's survival situation.Secondly,the differentiated survival situation data is used to generate stationary predicting sequences.The autoregressive integrated moving average (ARIMA) model is constructed,and the stability,randomness and reversibility index of the model are verified by the autocorrelation function and partial auto-correlation function.Finally,fuzzy particles and the residual correction for the support vector regression (SVR) model are applied to realize the post-prediction of the survival situation.Compared with traditional decision-making methods,the simulation experiments show that the pre-recognition module can not only cluster the survival situation data and identify the service ranks,but can also recognize the illegal users.According to the prediction of abnormal situations numbers and residual correction,the model can effectively realize the postprediction of survival situations for survivable systems.关键词
可生存性/可识别性/模糊粒子/残差修正Key words
survivability/recognition/fuzzy particle/residual correction分类
信息技术与安全科学引用本文复制引用
赵国生,邵子豪,王健,李英梅..一种可生存系统生存态势的可识别性模型[J].东南大学学报（英文版）,2018,34(3):288-294,7.基金项目
The National Natural Science Foundation of China (No.61202458,61403109),the Natural Science Foundation of Heilongjiang Province (No.F2017021),Harbin Science and Technology Innovation Research Funds (No.2016RAQXJ036). （No.61202458,61403109）
