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A prediction model of cloud security situation based on evolutionary functional network

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Abstract

Aiming at the dynamic uncertainty and prediction accuracy of security situation prediction in complex cloud network environment, a prediction model of cloud security situation based on evolutionary functional network is proposed. Firstly, the evolutionary functional network model is constructed by combining the evolutionary algorithm with the functional network, which solves the problem of basis function selection and basis function coefficient correction of the prediction model. Secondly, the stochastic approximation algorithm is used to process and comprehend the cloud security situation elements, and the computational complexity of the prediction model is reduced by the dimensionality reduction method. Finally, by constructing the credibility matrix of the uncertain influence relationship of security situation elements, we use the multivariate non-linear regression algorithm to predict the cloud security situation. The simulation results show that compared with BP model and RAN-RBF model, the prediction accuracy of the proposed model is improved by 8.2% and 6.9% respectively, and the convergence efficiency is improved by 12.3% and 10.8% respectively.

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Acknowledgements

This present research work was supported by the National Natural Science Foundation of China (61202458, 61403109), the Natural Science Foundation of Heilongjiang Province of China (F2017021) and the Harbin Science and Technology Innovation Research Funds (2016RAQXJ036).

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Authors and Affiliations

  1. College of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China

    Baowen Xie, Guosheng Zhao & Mianxing Chao

  2. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150001, China

    Jian Wang

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  1. Baowen Xie
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  2. Guosheng Zhao
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  3. Mianxing Chao
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  4. Jian Wang
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Correspondence to Guosheng Zhao.

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Xie, B., Zhao, G., Chao, M. et al. A prediction model of cloud security situation based on evolutionary functional network. Peer-to-Peer Netw. Appl. 13, 1312–1326 (2020). https://doi.org/10.1007/s12083-020-00875-9

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  • DOI: https://doi.org/10.1007/s12083-020-00875-9

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