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去二存一混合机制下的病毒扩散模型及稳定性分析

王刚 陆世伟 胡鑫 马润年

王刚, 陆世伟, 胡鑫, 马润年. “去二存一”混合机制下的病毒扩散模型及稳定性分析[J]. 电子与信息学报, 2019, 41(3): 709-716. doi: 10.11999/JEIT180381
引用本文: 王刚, 陆世伟, 胡鑫, 马润年. 去二存一混合机制下的病毒扩散模型及稳定性分析[J]. 电子与信息学报, 2019, 41(3): 709-716. doi: 10.11999/JEIT180381
Gang WANG, Shiwei LU, Xin HU, Runnian MA. Virus Propagation Model and Stability Under the Hybrid Mechanism of “Two-go and One-live”[J]. Journal of Electronics & Information Technology, 2019, 41(3): 709-716. doi: 10.11999/JEIT180381
Citation: Gang WANG, Shiwei LU, Xin HU, Runnian MA. Virus Propagation Model and Stability Under the Hybrid Mechanism of “Two-go and One-live”[J]. Journal of Electronics & Information Technology, 2019, 41(3): 709-716. doi: 10.11999/JEIT180381

去二存一混合机制下的病毒扩散模型及稳定性分析

doi: 10.11999/JEIT180381
基金项目: 国家自然科学基金(61573017, 61703420)
详细信息
    作者简介:

    王刚:男,1976年生,博士,教授,硕士生导师,主要研究方向为网络空间安全和复杂网络

    陆世伟:男,1995年生,硕士生,研究方向为网络空间安全

    胡鑫:男,1993年生,硕士生,研究方向为网络空间安全

    通讯作者:

    王刚 wglxl@nudt.edu.cn

  • 中图分类号: TP393.08

Virus Propagation Model and Stability Under the Hybrid Mechanism of “Two-go and One-live”

Funds: The National Science Foundation of China (61573017, 61703420)
  • 摘要:

    随着网络信息系统的发展,网络病毒扩散方式及免疫策略成为网络安全领域研究的热点之一。该文研究了一类新型混合攻击病毒,并根据其特点将这类病毒定义为“去二存一”型病毒。通过分析新型病毒的攻击方式,构建了“去二存一”混合机制下病毒的SEIQRS信息扩散模型。在此基础上,求解对应系统的平衡点,并运用Routh-Hurwitz判据分析了系统基本再生数R0及其对系统稳定性的影响。最后,仿真验证了模型的有效性和稳定性。

  • 图  1  “去二存一”混合机制下的SEIQRS病毒扩散模型

    图  2  k=5和k=10时各状态节点数量

    图  3  不同k值下I(t)随时间的变化

    图  4  $\alpha = 0.01$$\alpha = 0.10$时各状态节点数量

    图  5  不同$\alpha $值下I(t)随时间的变化

    图  6  $\beta = 0.1$$\beta = 0.5$时各状态节点数量

    图  7  不同$\beta $值下I(t)随时间的变化

    图  8  $\sigma = 0.3$$\sigma = 1.2$时的各状态节点数量

    图  9  不同$\sigma $值下I(t)随时间的变化

  • 张书奎, 崔志明, 龚声蓉, 等. 传感器网络病毒感染传播局域控制研究[J]. 电子学报, 2009, 37(4): 877–883. doi: 10.3321/j.issn:0372-2112.2009.04.038

    ZHANG Shukui, CUI Zhiming, GONG Shengrong, et al. An investigation on local area control of compromised nodes spreading in wireless sensor networks[J]. Acta Electronica Sinica, 2009, 37(4): 877–883. doi: 10.3321/j.issn:0372-2112.2009.04.038
    王田, 吴群, 文晟, 等. 无线传感网中移动式蠕虫的抑制与清理[J]. 电子与信息学报, 2016, 38(9): 2202–2207. doi: 10.11999/JEIT151311

    WANG Tian, WU Qun, WEN Sheng, et al. The inhibition and clearup of the mobile worm in wireless sensor networks[J]. Journal of Electronics &Information Technology, 2016, 38(9): 2202–2207. doi: 10.11999/JEIT151311
    E安全. 黑客战术三十六计之" 声东击西”[OL]. https://www.easyaq.com/news/1538639872.shtml, 2017, 11.
    ZHANG Zizhen and BI Dianjie. Bifurcation analysis in a delayed computer virus model with the effect of external computers[J]. Advances in Difference Equations, 2015, 2015(1): 317–330. doi: 10.1186/s13662-015-0652-y
    VALDEZ J, GUEVARA P, and AUDELO J. Numerical approaching of SIR epidemic model for propagation of computer worms[J]. IEEE Latin America Transactions, 2015, 13(10): 3452–3460. doi: 10.1109/TLA.2015.7387254
    QU Bo and WANG Huijuan. SIS epidemic spreading with correlated heterogeneous infection rates[J]. Physica A: Statistical Mechanics and its Applications, 2017, 472(1): 13–24.
    TANG Qian and TENG Zhidong. A new Lyapunov function for SIRS epidemic models[J]. Bulletin of the Malaysian Mathematical Sciences Society, 2017, 40(1): 237–258. doi: 10.1007/s40840-016-0315-5
    顾海俊, 蒋国平, 夏玲玲. 基于状态概率转移的SIRS病毒传播模型及其临界值分析[J]. 计算机科学, 2016, 43(6): 64–67. doi: 10.11896/j.issn.1002-137X.2016.6A.014

    GU Haijun, JIANG Guoping, and XIA Lingling. SIRS epidemic model and its threshold based on state transition probability[J]. Computer Science, 2016, 43(6): 64–67. doi: 10.11896/j.issn.1002-137X.2016.6A.014
    YANG Luxing and YANG Xiaofan. The impact of nonlinear infection rate on the spread of computer virus[J]. Nonlinear Dynamics, 2015, 82(2): 85–95.
    关治洪, 亓玉娟, 姜晓伟, 等. 基于复杂网络的病毒传播模型及其稳定性[J]. 华中科技大学学报, 2011, 39(1): 114–117.

    GUAN Zhihong, QI Yujuan, JIANG Xiaowei, et al. Virus propagation dynamic model and stability on complex network[J]. Journal Huazhong University of science &Technology, 2011, 39(1): 114–117.
    XU Degang, XU Xiyang, XIE Yongfang, et al. Optimal control of an SIVRS epidemic spreading model with virus variation based on complex networks[J]. Communications in Nonlinear Science and Numerical Simulation, 2017, 48(1): 200–210.
    KHANH G H and HUY N B. Stability analysis of a computer virus propagation model with antidote in vulnerable system[J]. Acta Mathematica Scientia, 2016, 36(1): 49–61. doi: 10.1016/S0252-9602(15)30077-1
    WANG Xu, NI Wei, ZHENG Kangfeng, et al. Virus propagation modeling and convergence analysis in large scale networks[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(10): 2241–2254. doi: 10.1109/TIFS.2016.2581305
    TIAN Daxin, LIU Chao, SHENG Zhengguo, et al. Analytical model of spread of epidemics in open finite regions[J]. IEEE Access, 2017, 5(2): 9673–9681.
    LIU Siyu, JIN Jiyu, and WANG Zhisen. Influence of node mobility on virus spreading behaviors in multi-hop network[J]. EURASIP Journal on Wireless Communications and Networking, 2016, 2016(1): 172–182. doi: 10.1186/s13638-016-0667-4
    ZHANG Chunming and HUANG Haitao. Optimal control strategy for a novel computer virus propagation model on scale-free networks[J]. Physica A: Statistical Mechanics and Its Applications, 2016, 451(1): 251–265.
    HAN Dun, SUN Mei, and LI Dandan. The virus variation model by considering the degree-dependent spreading rate[J]. Physica A: Statistical Mechanics and Its Applications, 2015, 433(1): 42–50.
    XU Qichao, SU Zhou, and YANG Kan. Optimal control theory based epidemic information spreading scheme for mobile social users with energy constraint[J]. IEEE Access, 2017, 5(1): 3536–3547.
    王小娟, 宋梅, 郭世泽, 等. 基于有向渗流理论的关联微博转发网络信息传播研究[J]. 物理学报, 2015, 64(4): 4502–4510. doi: 10.7498/aps.64.044502

    WANG Xiaojuan, SONG Mei, GUO Shize, et al. Information spreading in correlated microblog reposting network based on directed percolation theory[J]. Acta Physica Sinica, 2015, 64(4): 4502–4510. doi: 10.7498/aps.64.044502
    LI Ming and WANG Binghong. Percolation on networks with dependence links[J]. Chinese Physics B, 2014, 23(7): 6402–6411.
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  • 被引次数: 0
出版历程
  • 收稿日期:  2018-04-25
  • 修回日期:  2018-09-13
  • 网络出版日期:  2018-09-25
  • 刊出日期:  2019-03-01

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