| 引用本文: | 王刚,陆世伟,胡鑫,马润年.潜伏机制下网络病毒传播SEIQRS模型及稳定性分析[J].哈尔滨工业大学学报,2019,51(5):131.DOI:10.11918/j.issn.0367-6234.201805136 |
| WANG Gang,LU Shiwei,HU Xin,MA Runnian.Network virus spreading SEIQRS model and its stability under escape mechanism[J].Journal of Harbin Institute of Technology,2019,51(5):131.DOI:10.11918/j.issn.0367-6234.201805136 |
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| 潜伏机制下网络病毒传播SEIQRS模型及稳定性分析 |
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王刚1,陆世伟1,胡鑫1,2,马润年1
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(1.空军工程大学信息与导航学院,西安 710077;2.中国人民解放军95507部队,贵阳 550025)
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| 摘要: |
| 针对新型潜伏病毒传播特性,研究潜伏机制下的网络病毒传播模型及其稳定性,为潜伏型病毒扩散机理及防御策略研究提供理论参考. 首先,从新型网络病毒的潜伏特点入手,分析处于潜伏状态的网络节点存在的三种转化模式,提出了潜伏机制下的网络病毒传播模型；其次,运用劳斯稳定判据,论证了网络病毒传播平衡点的局部稳定性,求解系统的基本再生数R0；最后,给出了潜伏状态下三种转移参数对系统稳定性影响的仿真验证. 理论分析和仿真结果表明,当R0≤1时,网络在无病毒平衡点局部渐近稳定；当R0>1时,网络在感染源平衡点局部渐近稳定. 通过调整优化潜伏状态下三种转移概率,可以减小系统基本再生数R0,使网络维持在无病毒健康状态. 增大潜伏状态到易感状态转移概率θ,减小潜伏状态到感染状态转移概率γ和增大潜伏状态到免疫状态转移概率b,可以将潜伏病毒入侵的网络控制稳定在无病毒平衡点,从而维护网络安全. |
| 关键词: 网络安全 潜伏机制 病毒传播 模型 稳定性 |
| DOI：10.11918/j.issn.0367-6234.201805136 |
| 分类号:TP309 |
| 文献标识码:A |
| 基金项目:国家自然科学基金项目(7,0) |
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| Network virus spreading SEIQRS model and its stability under escape mechanism |
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WANG Gang1,LU Shiwei1,HU Xin1,2,MA Runnian1
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(1. Information and Navigation Institute, Air Force Engineering University, Xi’an 710077, China; 2. The Chinese People’s Liberation The 95507 troop, Guiyang 550025, China）
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| Abstract: |
| According to the spreading characteristic of new escape virus, a novel network virus spreading model and its stability were researched under the escape mechanism to provide a theoretical reference for defenders. Firstly, three transformation modes were derived based on the exposed characteristic of new network virus from the exposed nodes, and then a novel network virus spreading model was constructed under the exposed mechanism. Secondly, by using Routh stability criterion, the stability of system equilibrium point was demonstrated and the basic reproductive number R0was calculated. Finally, simulations were demonstrated for the influence of three transfer parameters on system stability. Theoretical research and simulations showed that if the basic reproductive number R0≤1, the disease-free equilibrium was locally asymptotically stable; if R0>1, the endemic equilibrium was locally asymptotically stable. By adjusting the probability of three kinds of metastasis in the escape state, the basic reproductive number of the system could be reduced, and the network was maintained in a disease-free healthy state. The network could be controlled in the disease-free equilibrium by increasing transition probability θfrom escape state to susceptible state, bfrom escape state to removed state, or decreasing γ from escape state to infected state, so as to maintain network security. |
| Key words: cyber security escape mechanism virus spreading model stability 〖FQ(+20mm。22,ZX-W〗收稿日期: 2018-05-23 基金项目: 国家自然科学基金项目（7,0） 作者简介: 王刚（1976—）,男,教授,硕士生导师通信作者: 王刚,wglxl@nudt.edu.cn |
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