|Table of Contents|

[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, (3): 288-294. [doi:10.3969/j.issn.1003-7985.2018.03.002]

A recognition model of survival situations for survivable systems()

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

2018 3
Research Field:
Computer Science and Engineering
Publishing date:


A recognition model of survival situations for survivable systems
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
survivability recognition fuzzy particle residual correction
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.


[1] Westmark V R. A definition for information system survivability [C]//Proceedings of the 37th Hawaii International Conference on System Sciences. Big Island, HI, USA, 2004:2086-2096.
[2] Yaghlane A B, Azaiez M N. Systems under attack-survivability rather than reliability: Concept, results, and applications[J]. European Journal of Operational Research, 2017, 258(3):1156-1164. DOI:10.1016/j.ejor.2016.09.041
[3] Zhao L, Zou H, Zhang X H. Survivability model for reconfigurable service carrying network based on the stochastic Petri net [J]. Journal on Communications, 2016, 37(3):71-78.(in Chinese)
[4] Raja U, Tretter M J.Defining and evaluating a measure of open source project survivability[J].IEEE Transactions on Software Engineering, 2012, 38(1):163-174. DOI:10.1109/tse.2011.39.
[5] Zhao G S, Liu H L, Wang J. Study on the autonomous recognition mechanism for survivable systems [J]. Chinese High Technology Letters, 2014, 24(10): 999-1006.(in Chinese)
[6] Wang J, Zhao G S. Cognitive model and quantitative analysis for survivable system based on SM-PEPA [J]. Journal of Huazhong University of Science and Technology(Natural Science Edition), 2015, 43(5): 99-103.(in Chinese)
[7] Dharmaraja S, Vinayak R, Trivedi K S.Reliability and survivability of vehicular ad hoc networks: An analytical approach[J].Reliability Engineering & System Safety, 2016, 153:28-38. DOI:10.13039/501100007488.
[8] Duessel P, Gehl C, Flegel U, et al. Detecting zero-day attacks using context-aware anomaly detection at the application-layer [J]. International Journal of Information Security, 2017, 16(5):475-490. DOI:10.1007/s10207-016-0344-y.
[9] Greve B, Pigeot I, Huybrechts I, et al. A comparison of heuristic and model-based clustering methods for dietary pattern analysis[J].Public Health Nutrition, 2015, 19(2):255-264. DOI:10.1017/s1368980014003243
[10] Huang H R, Jiang S L, Cai K. Key important multiple attribute error-eliminating decision-making method[J]. Mathematics in Practice and Theory, 2015, 45(11): 15-20.(in Chinese)
[11] Pati J, Kumar B, Manjhi D, et al.A comparison among ARIMA, BP-NN, and MOGA-NN for software clone evolution prediction[J].IEEE Access, 2017, 5:11841-11851. DOI:10.1109/access.2017.2707539
[12] Kavousi-Fard A, Kavousi-Fard 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. DOI:10.1080/0952813x.2013.782351
[13] Liu S F, Zeng B, Liu J F, et al.Four basic models of GM(1, 1)and their suitable sequences[J].Grey Systems: Theory and Application, 2015, 5(2):141-156. DOI:10.1108/gs-04-2015-0016
[14] Wu B, Han S J, Xiao J, et al.Error compensation based on BP neural network for airborne laser ranging[J].Optik—International Journal for Light and Electron Optics, 2016, 127(8):4083-4088. DOI:10.13039/501100004750
[15] Huang W 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. Ankara, Turkey, 2017:177-180.
[16] Zhao G S, Wang H Q, Wang J. Study on situation evaluation for network survivability based on grey relation in analysis [J]. Mini-Micro Systems, 2006, 27(10): 1861-1864. DOI:10.3969/j.issn.1000-1220.2006.10.015. (in Chinese)
[17] Huang H R. The research of multiple attribute error-eliminating decision-making method[D]. Guangzhou: School of Management, Guangdong University of Technology, 2014.(in Chinese)


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.
Last Update: 2018-09-20