Abstract
The threat of computer viruses has become an increasingly important issue, so it is necessary to improve the protection of computer networks against computer viruses. This article puts forward the idea of using the curve area method to calculate the resilience of computer networks. In addition, three factors affecting computer networks in terms of computer viruses and four repair strategies for computer networks are studied. The results show that the increased infection rate and the increase in the number of initial infected nodes will not cause the network to crash; the medial-based attacks have the greatest impact on the network; the medial-based repair strategy is better than the balanced repair strategy and has the best repair effect on the network.
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References
Mishra, B.K., Jha, N.: SEIQRS model for the transmission of malicious objects in computer network. Appl. Math. Model. 34(3), 710–715 (2010)
Mishra, B.K., Pandey, S.K.: Dynamic model of worms with vertical transmission in computer network. Appl. Math. Comput. 217(21), 8438–8446 (2011)
Mishra, B.K., Saini, D.K.: SEIRS epidemic model with delay for transmission of malicious objects in computer network. Appl. Math. Comput. 188(2), 1476–1482 (2007)
Donohue, I., et al.: Navigating the complexity of ecological stability. Ecol. Lett. 19(9), 1172–1185 (2016)
Jung, E., Lenhart, S., Feng, Z.: Optimal control of treatments in a two-strain tuberculosis model. Discrete Continuous Dyn. Syst. Ser. B 4, 473–482 (2002)
Zaman, G., Kang, Y.-H., Jung, I.-H.: Optimal treatment of an SIR epidemic model with time delay. Biosystems 1, 43–50 (2009)
Ren, J.G., Yang, X.F., Zhu, Q.Y., et al.: A novel computer virus model and its dynamics. Nonlinear Anal. Real World Appl. 1, 376–384 (2012)
Fei, S.-U., Lin, Z.-W., Yan, M.-A.: Modeling and analysis of Internet worm propagation. J. China Univ. Posts Telecommun. 17(4), 63–68 (2010)
Yuan, J.L., Yang, Z.D.: Global dynamics of an SEI model with acute and chronic stages. J. Comput. Appl. Math. 2, 465–476 (2008)
Gao, J., Baruch, B., Albert-László, B.: Author Correction: Universal resilience patterns in complex networks. Nature 568(7751), 5–6 (2019)
Holling, C.-S.: Resilience and stability of ecological systems. Ann. Rev. Ecol. Syst. 4(1), 1–23 (1973)
Fang, Y., Pedroni, N., Zio, E.: Resilience-based component importance measures for critical infrastructure network systems. IEEE Trans. Reliab. 65(2), 502–512 (2016)
Barker, K., Ramirez-Marquez, J.E., Rocco, C.M.: Resilience-based network component importance measures. Reliab. Eng. Syst. Saf. 117, 89–97 (2013)
Cimellaro, G.P., Reinhorn, A.M., Bruneau, M.: Framework for analytical quantification of disaster resilience. Eng. Struct. 32(11), 3639–3649 (2010)
Baroud, H., et al.: Importance measures for inland waterway network resilience. Transp. Res. Part E Logist. Transp. Rev. 62, 55–67 (2014)
Wang, X.: Harm and prevention of computer virus. Private Sci. Technol. 11, 91 (2015)
Fang, B., Cui, X., Wang, W.: Overview of botnets. Comput. Res. Dev. 48(08), 1315–1331 (2011)
Hu, Z., Li, X.: Contagion and bailout strategy in complex financial network. Finan. Trade Econ. 38(04), 101–114 (2017)
Daron, A., Asuman, O., Alireza, T.-S.: Systemic risk and stability in financial networks. Am. Econ. Rev. 105(2), 564–608 (2015)
Huser, A.-C.: Too interconnected to fail: a survey of the interbank networks literature. J. Netw. Theor. Finan. 1(3), 1–50 (2015)
Acknowledgments
The authors are highly thankful for National Key Research Program (2019YFB1706001), Industrial Internet Innovation Development Project (TC190H46B), National Natural Science Foundation of China (61773001).
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Hong, S., Wang, Y. (2020). Attack and Repair Strategies for Computer Network About Viruses. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_43
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DOI: https://doi.org/10.1007/978-3-030-62223-7_43
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