Zero-day Virus Transmission Model and Stability Analysis
-
摘要: 针对零日病毒特点和传播规律，该文研究了零日病毒传播模型及稳定性。首先，分析了零日病毒传播机理，在易感-感染-移除-易感(SIRS)病毒传播模型基础上，重新定义了感染状态节点，引入执行状态节点和毁损状态节点，建立了零日病毒传播的易感-初始感染-零日-毁损-移除(SIZDR)病毒传播动力学模型；其次，运用劳斯稳定性判据，分析了系统平衡点的局部稳定性，基本再生数
${R_0}$ 及其对病毒传播规模的影响。最后，仿真验证了模型局部稳定性，分析了节点感染率、节点度和节点毁损率等因素对零日病毒传播的影响。理论分析与仿真结果表明，该模型能客观反映零日病毒传播规律，零日病毒扩散规模与节点度、节点感染率正相关，与节点毁损率负相关，对已知病毒的针对性防控可有效提升对零日病毒的防御效果。Abstract: According to the characteristics and propagation law of zero-day virus, the propagation model and stability of zero-day virus are studied. Firstly, the mechanism of zero-day virus transmission is analyzed. Based on the Susceptible-Infected-Removed-Susceptible(SIRS) virus transmission model, the node of infection state is redefined, the node of execution state and the node of damage state are introduced, and the zero-day virus transmission Susceptible - Initial-state-of infection- Zero-day - Damaged – Recovery (SIZDR) dynamic model is established. Secondly, the local stability of the system equilibrium point, the basic regeneration number and its influence on the scale of virus transmission are analyzed by using Rous stability criterion. Finally, the local stability of the model is verified by simulation, and the influence of node infection rate, node degree and node damage rate on zero-day virus transmission is analyzed. Theoretical analysis and simulation results show that the proposed model can objectively reflect the law of zero-day virus transmission, and the magnitude of zero-day virus spread is positively correlated with node degree and node infection rate, and negatively correlated with node damage rate. Targeted prevention and control of known viruses can effectively improve the defense effect against zero-day viruses.-
Key words:
- Zero-day attack /
- Virus propagation model /
- Stability
-
[1] ZETTER K. Countdown to Zero Day[M]. ZETTER K. Countdown to Zero Day: Stuxnet and the Launch of the World’s First Digital Weapon. Broadway Books, 2015. [2] Cnbeta. 卡巴斯基透露有黑客同时利用Windows 10和Chrome零日漏洞发动攻击[EB/OL]. http://cnnvd.org.cn/web/xxk/yjxwById.tag?id=11,170, 2019. [3] Cnbeta. 网络安全研究人员发现新漏洞: 或成另一个WannaCry[EB/OL]. http://cnnvd.org.cn/web/xxk/yjxwById.tag?id=8,486, 2017. [4] SUN Xiaoyan, DAI Jun, LIU Peng, et al. Using Bayesian networks for probabilistic identification of zero-day attack paths[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(10): 2506–2521. doi: 10.1109/TIFS.2018.2821095 [5] SUN Xiaoyan, DAI Jun, LIU Peng, et al. Using Bayesian Networks to Fuse Intrusion Evidences and Detect Zero-day Attack Paths[M]. WANG Lingyu, JAJODIA S, and SINGHAL A. Network Security Metrics. Cham: Springer, 2017. [6] DIRO A A and CHILAMKURTI N. Distributed attack detection scheme using deep learning approach for internet of things[J]. Future Generation Computer Systems, 2018, 82: 761–768. doi: 10.1016/j.future.2017.08.043 [7] KIM J Y, BU S J, and CHO S B. Zero-day malware detection using transferred generative adversarial networks based on deep Autoencoders[J]. Information Sciences, 2018, 460/461: 83–102. doi: 10.1016/j.ins.2018.04.092 [8] 张瑜, 潘小明, LIU Qingzhong, 等. APT攻击与防御[J]. 清华大学学报: 自然科学版, 2017, 57(11): 1127–1133. doi: 10.16511/j.cnki.qhdxxb.2017.21.024ZHANG Yu, PAN Xiaoming, LIU Qingzhong, et al. APT attacks and defenses[J]. Journal of Tsinghua University:Science &Technology, 2017, 57(11): 1127–1133. doi: 10.16511/j.cnki.qhdxxb.2017.21.024 [9] ZHANG Mengyuan, WANG Lingyu, JAJODIA S, et al. Network diversity: A security metric for evaluating the resilience of networks against zero-day attacks[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(5): 1071–1086. doi: 10.1109/TIFS.2016.2516916 [10] VALDEZ J S, GUEVARA P, AUDELO J, et al. 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 [11] 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(S1): 64–67.GU Haijun, JIANG Guoping, and XIA Lingling. SIRS epidemic model and its Threshold based on state transition probability[J]. Computer Science, 2016, 43(S1): 64–67. [12] 王刚, 陆世伟, 胡鑫, 等. 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): 131–137. doi: 10.11918/j.issn.0367-6234.201805136WANG Gang, LU Shiwei, HU Xin, et al. Network virus spreading SEIQRS model and its stability under escape mechanism[J]. Journal of Harbin Institute of Technology, 2019, 51(5): 131–137. doi: 10.11918/j.issn.0367-6234.201805136 [13] 王刚, 陆世伟, 胡鑫, 等. “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/JEIT180381WANG Gang, LU Shiwei, HU Xin, et al. 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 [14] 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 [15] WANG Lei, YAO Changhua, YANG Yuqi, et al. Research on a dynamic virus propagation model to improve smart campus security[J]. IEEE Access, 2018, 6: 20663–20672. doi: 10.1109/ACCESS.2018.2817508 [16] 秦李, 黄曙光, 陈骁. 病毒传播下的因特网级联故障模型构建与仿真[J]. 计算机应用研究, 2016, 33(4): 1228–1231, 1235. doi: 10.3969/j.issn.1001-3695.2016.04.059QIN Li, HUANG Shuguang, and CHEN Xiao. Model and simulation for cascading failure on internet based on virus propagation[J]. Application Research of Computers, 2016, 33(4): 1228–1231, 1235. doi: 10.3969/j.issn.1001-3695.2016.04.059 [17] 巩永旺, 宋玉蓉, 蒋国平. 移动环境下网络病毒传播模型及其稳定性研究[J]. 物理学报, 2012, 61(11): 110205. doi: 10.7498/aps.61.110205GONG Yongwang, SONG Yurong, and JIANG Guoping. Epidemic spreading model and stability of the networks in mobile environment[J]. Acta Physica Sinica, 2012, 61(11): 110205. doi: 10.7498/aps.61.110205 -
下载:
图(4)
计量
- 文章访问数: 947
- HTML全文浏览量: 626
- PDF下载量: 61
- 被引次数: 0
下载:
