Cloud Belief Rule Base Model for Network Security Situation Prediction

To predict network security situations better using expert knowledge and quantitative data, a new forecasting model known as cloud belief rule base (CBRB) model is proposed. The CBRB model utilizes the cloud model to describe the referential point of belief rule, which is more accurate for describing expert knowledge. Moreover, to achieve the optimal parameters of the proposed model, a constraint covariance matrix adaptation evolution strategy (CMA-ES) algorithm is presented in this letter. A case study for network security situation prediction is conducted with CBRB and CMA-ES. The experimental results demonstrate the effectiveness and practicality of the proposed CBRB model.

[1]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[2]  Jian-Bo Yang,et al.  Belief rule-base inference methodology using the evidential reasoning Approach-RIMER , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[3]  Jian-Bo Yang,et al.  Environmental impact assessment using the evidential reasoning approach , 2006, Eur. J. Oper. Res..

[4]  X. Y. Zhang,et al.  Application of support vector machine (SVM) for prediction toxic activity of different data sets. , 2006, Toxicology.

[5]  Deyi Li,et al.  Artificial Intelligence with Uncertainty , 2004, CIT.

[6]  Jian-Bo Yang,et al.  New model for system behavior prediction based on belief rule based systems , 2010, Inf. Sci..

[7]  Chen Xiu.  Quantitative Hierarchical Threat Evaluation Model for Network Security , 2006 .

[8]  Heejo Lee,et al.  Cyber Threat Trend Analysis Model Using HMM , 2007 .

[9]  Jian-Bo Yang,et al.  Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties , 2001, Eur. J. Oper. Res..

[10]  Sun Tanfeng.  RBFNN-based Prediction of Networks Security Situation , 2006 .

[11]  Chang-Hua Hu,et al.  Hidden behavior prediction of complex systems based on semi-quantitative information , 2013, 2013 25th Chinese Control and Decision Conference (CCDC).

[12]  Tim Bass,et al.  Intrusion detection systems and multisensor data fusion , 2000, CACM.

[13]  S. K. Yang,et al.  State estimation for predictive maintenance using Kalman filter , 1999 .

[14]  Wang Huiqiang,et al.  Study of Network Security Situation Awareness Model Based on Simple Additive Weight and Grey Theory , 2006, 2006 International Conference on Computational Intelligence and Security.