基于生存簇识别和预测的生存态势感知模型
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摘要
可生存系统安全态势的形势日趋严峻,增强生存性的前提是识别出目标系统的生存态势,该文构建了一种基于生存簇识别和预测的生存态势感知模型。首先,对生存态势数据采用Ward增强聚类法实现不同服务等级生存簇的分类和识别;其次,使用自回归积分滑动平均(ARIMA)模型预测目标系统生存态势的未来趋势,并对预测结果进行了残差修正;最后,结合事前识别和事后预测实现了对可生存系统生存态势的感知。仿真实验表明,该模型具有良好识别效果和较高的预测准确度。
The security situation of survivable system is becoming more and more serious. The premise of enhancing survivability is to recognize the survival situation of the target system. A survival situation awareness model is constructed based on survival cluster recognition and prediction. Firstly, the improved Ward clustering method is used to realize the classification and recognition of different service levels. Secondly, the autoregressive integrated moving average(ARIMA) model is used to predict the future trend of the target system's survival situation and the residuals of the prediction results are corrected. Finally, the survival situation of survivable system is realized by the combination of pre-recognition and post-prediction. Simulation results show that the proposed model has better recognition effect and higher prediction accuracy.
The security situation of survivable system is becoming more and more serious. The premise of enhancing survivability is to recognize the survival situation of the target system. A survival situation awareness model is constructed based on survival cluster recognition and prediction. Firstly, the improved Ward clustering method is used to realize the classification and recognition of different service levels. Secondly, the autoregressive integrated moving average(ARIMA) model is used to predict the future trend of the target system's survival situation and the residuals of the prediction results are corrected. Finally, the survival situation of survivable system is realized by the combination of pre-recognition and post-prediction. Simulation results show that the proposed model has better recognition effect and higher prediction accuracy.
引文
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[2]耿技,宋旭,陈伟,等.基于系统结构和运行环境的系统生存性模型[J].电子科技大学学报,2014,43(1):101-106.GENG Ji,SONG Xu,CHEN Wei,et al.Based on system structure and runtime environment[J].Journal of University of Electronic Science and Technology of China,2014,43(1):101-106.
[3]陈长清,裴小兵,周恒,等.入侵容忍实时数据库的半马尔可夫生存能力评价模型[J].计算机学报,2011,34(10):1907-1916.CHEN Chang-qing,PEI Xiao-bing,ZHOU Heng,et al.A semi Markov evaluation model for the survivability of real time database with intrusion tolerance[J].Chinese Journal of Computers,2011,34(10):1907-1916.
[4]赵靓,邹宏,张校辉.基于随机Petri网的虚拟网可生存性模型研究[J].通信学报,2016,37(3):71-78.ZHAO Liang,ZOU Hong,ZHANG Xiao-hui.Survivability model for reconfigurable service carrying network based on the stochastic Petri net[J].Journal on Communications,2016,37(3):71-78.
[5]ZAFFAR M A,RAJAGOPALAN H K,SAYDAM C,et al.Coverage,survivability or response time:a comparative study of performance statistics used in ambulance location models via simulation optimization[J].Operations Research for Health Care,2016,11:1-12.
[6]PATRASCU A,VELCIU M A,PATRICIU V V.Cloud computing digital forensics framework for automated anomalies detection[C]//IEEE International Symposium on Applied Computational Intelligence and Informatics.[S.l.]:IEEE,2015:505-510.
[7]MISHRA P,PILLI E S,VARADHARAJAN V,et al.Intrusion detection techniques in cloud environment:A survey[J].Journal of Network&Computer Applications,2017,77(C):18-47.
[8]王健,赵国生,张楠.基于模糊关系矩阵的可生存系统认知参考模型分析[J].武汉大学学报(理学版),2015,61(1):60-66.WANG Jian,ZHAO Guo-sheng,ZHANG Nan.Analysis of cognitive reference model for survivable system based on fuzzy relation matrix[J].Journal of Wuhan University(Natural Science Edition),2015,61(1):60-66.
[9]赵国生,刘海龙,王健.可生存系统的自主可识别性机制研究[J].高技术通讯,2014,24(10):999-1006.ZHAO Guo-sheng,LIU Hai-long,WANG Jian.Study on the autonomous recognition mechanism for survivable systems[J].Chinese High Technology Letters,2014,24(10):999-1006.
[10]BENJAMIN G,IRIS P,INGE H,et al.A comparison of heuristic and model-based clustering methods for dietary pattern analysis[J].Public Health Nutrition,2015,19(2):1-10.
[11]ELIETE N P,CASSIUS T S,LUIZ A T.Time series forecasting by using a neural ARIMA,model based on wavelet decomposition[J].Independent Journal of Management&Production,2016,7(1):252-270.
[12]HUANG W,ZHAO Y,HUANGPENG Q.SOC prediction of Lithium battery based on fuzzy information granulation and support vector regression[C]//International Conference on Electrical and Electronic Engineering.[S.l.]:IEEE,2017:177-180.
[13]黄灏然,江尚乐,蔡肯.关键重要型多属性消错决策方法[J].数学的实践与认识,2015,45(11):15-20.HUANG Hao-ran,JIANG Shang-le,CAI Ken.Key important multiple attribute error-eliminating decision-making method[J].Mathematics in Practice and Theory,2015,45(11):15-20.
[14]ABDOLLAH K F,FARZANEH K F.A new hybrid correction method for short-term load forecasting based on ARIMA,SVR and CSA[J].Journal of Experimental&Theoretical Artificial Intelligence,2013,25(4):559-574.
[15]赵国生,王慧强,王健.基于灰色关联分析的网络可生存性态势评估研究[J].小型微型计算机系统,2006,27(10):1861-1864.ZHAO Guo-sheng,WANG Hui-qiang,WANG Jian.Study on situation evaluation for network survivability based on grey relation in analysis[J].Mini-Micro Systems,2006,27(10):1861-1864.
[16]黄灏然.多属性消错决策方法研究[D].广州:广东工业大学,2014.HUANG Hao-ran.The research of multiple attribute error-Eliminating decision-Making method[D].Guangzhou:Guangdong University of Technology,2014.
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