[1] Zhao Guosheng, Shao Zihao, Wang Jian, Li Yingmei, et al. A recognition model of survival situations for survivable systems [J]. Journal of Southeast University (English Edition), 2018, 34 (3): 288-294. [doi:10.3969/j.issn.1003-7985.2018.03.002]

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A recognition model of survival situations for survivable systems()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

34

Issue:

2018 3

Page:

288-294

Research Field:

Computer Science and Engineering

Publishing date:

2018-09-20

Info

Title:

A recognition model of survival situations for survivable systems

Author(s):

Zhao Guosheng¹;  Shao Zihao¹;  Wang Jian²;  Li Yingmei¹

¹College of Computer Science and Technology, Harbin Normal University, Harbin 150025, China
²School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China

Keywords:

survivability;  recognition;  fuzzy particle;  residual correction

PACS:

TP393

DOI:

10.3969/j.issn.1003-7985.2018.03.002

Abstract:

Due to the lack of pre-recognition and post-prediction 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 auto-correlation 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 post-prediction of survival situations for survivable systems.

References:

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Memo

Memo:

Biography: Zhao Guosheng(1977—), male, doctor, professor, zgswj@163.com.
Foundation items: 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).
Citation: Zhao Guosheng, Shao Zihao, Wang Jian, et al. A recognition model of survival situations for survivable systems[J].Journal of Southeast University(English Edition), 2018, 34(3):288-294.DOI:10.3969/j.issn.1003-7985.2018.03.002.

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