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In hierarchical network security situation assessment model, there are many problems, such as subjective index weight factor, large evaluation index system, large amount of calculation and low efficiency. A network security situation assessment model and quantification method based on AHP is proposed to solve this problem. The AHP analytic hierarchy process (AHP) is combined with the hierarchical model of situation assessment to simplify the situation assessment problem. The D–S evidence theory is used to fuse the fuzzy results of multi-source equipment to solve the problem of single information source and large deviation of accuracy. Through the construction of three evaluation indexes of risk situation, basic operation situation and damage situation, the problem of large evaluation index system and low evaluation efficiency is solved. AHP analytic hierarchy process is used to determine the weights of different index items, so as to avoid the subjectivity and randomness of weighting factors. The simulation results show that the model can reflect the security status of the network as a whole, and the situation assessment is accurate and can better serve the high-level decision-making.
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Hu, G. Y., & Qiao, P. L. (2015). An efficient improvement of CMA-ES algorithm for the network security situation prediction. The Open Automation and Control Systems Journal,7(1), 1499–1517. CrossRef
Wang, Y. Z., Li, J. Y., Meng, K., et al. (2013). Modeling and security analysis of enterprise network using attack–defense stochastic game Petri nets. Security and Communication Networks,6(I), 89–99. CrossRef
Liu, X. W., Wang, H. Q., Lu, H. W., Yu, J. G., & Zhang, S. W. (2016). Fusion-based cognitive awareness-control model for network security situation. Journal of Software,27(8), 2099–2114. MathSciNet
Wang, H., Gu, J., Su, W., Zhao, Z. P. (2016). An evaluation method of multi-source information fusion based on network security situation. In The 7th international symposium on computational intelligence and industrial applications (pp. 1–7). ISCIIA2016.
Xie, L. X., Wang, Y. C., & Yu, J. B. (2013). Network security situation awareness based on neural networks. Journal Tsinghua University: Science & Technology,23(12), 1750–1760.
Kotenko, I, Doynikova, E. (2014). Security evaluation for cyber situational awareness. In Proceedings of the high performance computing and communications (pp. 1197–1204).
Ramasso, E., & Denoeux, T. (2014). Making use of partial knowledge about hidden States in HMMS: An approach based on belief functions. IEEE Transactions On Fuzzy System,22(2), 395–405. CrossRef
Luo, Y. X., Zhao, M. H., Zhang, Q. Y., & Zou, A. J. (2011). Network security situation awareness model-inspired by immune. Advanced Materials Research,267, 635–638. CrossRef
Anthony, C., Chao, Y., Sirisak, K., et al. (2007). Network-based accessibility measures for vulnerability analysis of degradable transportation networks. Networks and spatial economics,3, 241–256. MATH
Tony, H. G., Timothy, C. M., Alan, T. M., et al. (2008). Comparative approaches for assessing network vulnerability. International regional science review,1, 88–112.
Erik, J., & Lars, G. M. (2012). Road network vulnerability analysis of area-covering disruptions: A grid-based approach with case study. Transportation research, part A. Policy and Practice,5, 746–760.
Wang, X. Z. (2016). Network information security situation assessment based on bayesian network. International Journal of Security and its Applications,5, 129–138. CrossRef
Wang, Y. (2016). Research on network security situation prediction based on markov game theory. International Journal of Security and its Applications,9, 301–308.
Wang, H., Gu, J., Di, X. Q., Liu, D., Zhao, J. P., & Sui, X. (2017). Research on classification and recognition of attacking factors based on radial basis function neural network. Cluster Computing. https://doi.org/10.1007/s10586-017-1371-9.
Liu, N., Li, G., Liu, Y. (2011). A method of network security situation prediction based on gray neural network model. In International conference on mechanical engineering, industry and manufacturing engineering (MEIME2011) (pp. 928–931). Trans Tech Publications.
Xiao, J. Q. (2012). Prediction of networks security situation based on wavelet kernel function network. In China Japan international conference on numbers, intelligence, manufacturing technology and machinery automation (MAMT 2011) (pp. 51–56). Trans Tech Publications.
Mo, X. J. (2014). Study on information security of network-based manufacturing environment. In International conference on green power, materials and manufacturing technology and applications (pp. 483–486). Trans Tech Publications.
Lu, S.S., Wang, X. F., Mao, L.,(2014). Network security situation awareness based on network simulation. In IEEE workshop on electronics, computer and applications (pp. 512–517). Institute of Electrical and Electronics Engineers.
Wang, Y. Z., Yu, M., Li, J. Y., et al. (2012). Stochastic game net and applications in security analysis for enterprise network. International Journal of Information Security,11(1), 41–52. CrossRef
Yan, G. H., Lee, R., Kent, A., et al.(2012). Towards a Bayesian network game framework for evaluating DDoS attacks and defense. In CCS’12 Proceedings of the 2012 ACM conference on computer and communications security (pp. 553–566). USA:ACM.
Liu, G., Li, Q. M., & Zhang, H. (2013). Defense strategy generation method for network security based on state attack-defense graph. Journal of Computer Applications,33(S1), 121–125.
- Research on Network Security Situation Assessment and Quantification Method Based on Analytic Hierarchy Process
- Springer US