Abstract
In the actual network attack and defense, the attack-defense behaviors generally change dynamically and continuously. Besides, since kinds of random disturbance is inevitable, the evolution of network security state actually is random. To model and analyze network security problems more accurately, we used the Gaussian white noise to describe the random disturbance. Then from the perspective of real-time attack and defense, we characterized the random and continuous evolution of network security state referring to dynamic epidemical model and the Itó stochastic differential equations. Based on previous statements, the attack and defense stochastic differential game model was constructed, and the saddle point strategy for the game was proposed. Additionally, we designed an optimal defense strategy selection algorithm to achieve real-time selection of the optimal defense strategies in continuous and random attack-defense process, which has greater timeliness and accuracy. Finally, simulations demonstrated that the proposed model and method are valid, and we offered specific recommendations for network defense based on the experimental data.
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References
Fang, B.-X.: A hierarchy model on the research fields of cyberspace security technology. Netw. Inf. Secur. 1(1), 1–6 (2015)
Gordon, L., Loeb, M.: Budgeting process for information security expenditures. Commun. ACM 49(10), 121–125 (2016)
Zhu, J.-M., Song, B., Huang, Q.-F.: Evolution game model of offensive-defense for network security based on system dynamics. J. Commun. 35(1), 54–61 (2014)
White, J., Park, J.S., Kamhoua, C.A., Kwiat, K.A.: Game theoretic attack analysis in online social network (OSN) services. In: Proceedings of the 2014 International Conference on Social Networks Technology, San Diego, USA, pp. 1012–1019. IEEE Press (2015)
Shordon, L., Miao, Q.: Network survivability analysis based on signaling game model. Multimed. Inf. Netw. Secur. 55(5), 199–204 (2016)
Lin, W.-Q., Wang, Hui, Liu, J.-H.: Research on active defense technology in network security based on non-cooperative dynamic game theory. J. Comput. Res. Dev. 48(2), 306–316 (2014)
Zhang, H.-W., Yu, D.-K.: Network security threat assessment based on signaling game. J. Xidian Univ. 43(3), 137–143 (2016)
Yeung, D.W.K., Petrosyan, L.A.: Differential Games Theory. Springer Press, New York (2014)
Jiang, W., Fang, B.-X., Tian, Z.-H.: Research on defense strategies selection based on attack-defense stochastic game model. J. Comput. Res. Dev. 47(10), 1714–1723 (2014)
Wang, Y.-Z., Lin, C., Cheng, X.-Q., Fang, B.-X.: Evolutionary game model and analysis methods for network group behavior. J. Comput. Sci. Technol. 38(2), 282–300 (2014)
Nilim, A., Ghaoui, L.E.: Robust control of Markov decision processes with uncertain transition matrices. Oper. Res. 53(5), 780–798 (2016)
Chun-lei, W., Qing, M., Yi-qi, D.: Network survivability analysis based on stochastic game model. Multimed. Inf. Netw. Secur. 55(10), 199–204 (2015)
Yu, M., Liu, C., Qiu, X.-L., Zhao, S.: Modelling and analysis of phishing attack using stochastic game. Cybersp. Technol. 46(3), 300–305 (2016)
Zhang, H.-W., Wang, J.-D., Li, T.: Defense policies selection method based on attack-defense signaling game model. J. Commun. 37(5), 32–43 (2016)
Scalable simulation framework[DB/OL], 08 November 2012–23 September 2016. http://www.ssfnet.org
Shen, S.G., Li, Y., Xu, H.Y.: Signaling game based strategy of intrusion detection in wireless sensor networks. Comput. Math Appl. 62(6), 2404–2416 (2015)
Dadsk, A.: Preventing DDoS attacks in wireless sensor networks: a repeated game theory approach. ACM Trans. Inf. Syst. Secur. 13(2), 145–153 (2015)
Zhang, H.-W., Li, T.: Optimal active defense based on multi-stage attack-defense signaling game. Acta electron. Sin. 45(2), 431–439 (2017)
Zhuang, W.-Y.: Study on Emergency Decision Making of Major Projects Based on Dynamic Differential Game Theory. School of Mathematics, Shandong University, Jinan (2014)
Moore, D., Shannon, C., Voelker, G.M.: Internet quarantine: requirements for containing self-propagating code. In: Proceedings of the 22’th International Conference of the IEEE Computer and Communications Societies, pp. 169–179. IEEE Press, Houston (2015)
Gordon, L., Loeb, M., Lucyshyn, W., Richardson, R.: CSI/FBI computer crime and security survey. In: Proceedings of the 2015 Computer Security Institute, pp. 48–64. IEEE Press, San Francisco (2015)
Matlab 2014 user’s guide and reference manual[EB/OL], 16 March 2014–23 August 2017. http://www.mathworks.com/
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Huang, S., Zhang, H., Wang, J., Huang, J. (2018). Network Defense Decision-Making Method Based on Stochastic Differential Game Model. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11067. Springer, Cham. https://doi.org/10.1007/978-3-030-00018-9_44
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DOI: https://doi.org/10.1007/978-3-030-00018-9_44
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