A New BRB Model for Cloud Security-State Prediction Based on the Large-Scale Monitoring Data

Hang Wei, Guan Yu Hu, Xiaoxia Han, Peili Qiao, Zhiguo Zhou, Zhi Chao Feng, Xiao Jing Yin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Considering the reliability of the cloud computing system, this paper aims to predict the security state with multiple large-scale attributes in cloud computing system. A double-layer method for predicting the security state of cloud computing system based on the belief rule-base model is proposed, where the evidential reasoning (ER) algorithm is employed to fuse the multiple system indicators of actual cloud system and make a reasonable assessment to describe the cloud security state. This method can utilize quantitative and qualitative information simultaneously. By using the ER algorithm to integrate multiple indicators whose attributes contain much uncertain information, the security state of the cloud computing system can be predicted accurately. Moreover, due to the initial parameters of the proposed models are given by experts that might cause imprecise results, the constraint CMA-ES algorithm is employed as the optimization tool to obtain the optimal parameters. A practical study about the cloud security-state prediction is verified to indicate the potential applications about the proposed prediction model in a cloud computing platform.

Original languageEnglish (US)
Pages (from-to)11907-11920
Number of pages14
JournalIEEE Access
StatePublished - Dec 1 2017


  • Belief rule base (BRB)
  • cloud computing
  • multi-attributes integration
  • security-state prediction

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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