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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Prediction of network security based on DS evidence theory

  • Liu, Dan (Chongqing College of Electronic Engineering)
  • Received : 2019.05.06
  • Accepted : 2019.11.14
  • Published : 2020.11.16
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Abstract

Network security situation prediction is difficult due to its strong uncertainty, but DS evidence theory performs well in solving the problem of uncertainty. Based on DS evidence theory, this study analyzed the prediction of the network security situation, designed a prediction model based on the improved DS evidence theory, and carried out a simulation experiment. The experimental results showed that the improved method could predict accurately in the case of a large conflict, and had strong anti-jamming abilities as compared with the original method. The experimental results prove the effectiveness of the improved method in the prediction of the network security situation and provide some theoretical basis for the further application of DS evidence theory.

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References

  1. Q. Wang and H. J. Chen, Computer network security and defense technology research, in Proc. Eighth Int. Conf. Meas. Tech. Mech. Autom. (Macau, China), 2016, pp. 155-157.
  2. J. Mi et al., Reliability analysis of complex multi-state system with common cause failure based on DS evidence theory and bayesian network, in Recent Advances in Multi-state Systems Reliability, Springer, 2018, pp. 19-38.
  3. X. W. Liu, Network Security Situation Quantification Aware - ness and Evaluation Based on Multi-source Fuson, Harbin Engineering University, Harbin, China, 2009.
  4. C. Yi, Q. Huang, and Y. Chen, An improve information fusion algorithm based on BP neural network and D-S evidence theory, in Proc. Third Int. Conf. Digital Manuf. Autom. (CuiLin, China), 2012, pp. 179-181.
  5. T. Leibovich and D. Ansari, The symbol-grounding problem in numerical recognition: A review of theory, evidence, and outstanding questions, Can. J. Exp. Psychol. 70 (2016), no. 1, 12-23. https://doi.org/10.1037/cep0000070
  6. W. Q. Zhang and Y. Deng, Combining conflicting evidence using the DEMATEL method, Soft Comput. 23 (2018), 8207-8216.
  7. Y. Wang, K. Zhang, and Y. Deng, Base belief function: an efficient method of conflict management, J. Amb. Intel. Hum. Comp. 10 (2019), 3427-3437. https://doi.org/10.1007/s12652-018-1099-2
  8. F. Xiao, Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy, Inform. Fusion 46 (2019), 23-32. https://doi.org/10.1016/j.inffus.2018.04.003
  9. F. Y. Xiao and W. P. Ding, Divergence measure of Pythagorean fuzzy sets and its application in medical diagnosis, Appl. Soft Comput. 79 (2019), 254-267. https://doi.org/10.1016/j.asoc.2019.03.043
  10. J. Wu et al., Big data analysis-based security situational awareness for smart grid, IEEE Trans. Big Data 4 (2016), no. 3, 408-417. https://doi.org/10.1109/tbdata.2016.2616146
  11. F. Xiang and Z. Jian, A D-S evidence weight computing method for conflict evidence, Comput. Eng. 2016-2 (2016).