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Edge-based modeling of computer virus contagion on a tripartite graph

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  • Pan, Wei
  • Jin, Zhen

Abstract

As a typical computer virus, a file virus can parasitize in executable files and infect other files when the host files are executed. Due to the strong similarity between computer viruses and their biological counterparts, in this paper we adapt the epidemiologically compartmental models to study the computer virus contagion. To trace the transmission process of file viruses and determine effective control measures, we derive a pairwise mathematical model by taking account of edge-based contagions. By constructing a tripartite graph, we can determine the potential edges on which contagions take place. The sensitivity analysis for some parameters is performed, indicating that the contagion of file viruses can be effectively restrained by reducing the use of portable storage devices with computers which have not installed antivirus softwares or by reducing the transmission rate from infected web pages to susceptible computers. It is also found that the final number of infected computers is much lower in scale-free networks than in Poisson degree distributed networks.

Suggested Citation

  • Pan, Wei & Jin, Zhen, 2018. "Edge-based modeling of computer virus contagion on a tripartite graph," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 282-291.
    • Handle: RePEc:eee:apmaco:v:320:y:2018:i:c:p:282-291
    DOI: 10.1016/j.amc.2017.09.044
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    References listed on IDEAS

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    1. Song, Li-Peng & Jin, Zhen & Sun, Gui-Quan, 2011. "Modeling and analyzing of botnet interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 347-358.
    2. Zhang, Hai-Feng & Shu, Pan-Pan & Wang, Zhen & Tang, Ming & Small, Michael, 2017. "Preferential imitation can invalidate targeted subsidy policies on seasonal-influenza diseases," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 332-342.
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    Cited by:

    1. Li, Jingwei & Li, Shouwei, 2023. "Immunization of systemic risk in trade–investment networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    2. Li, Shudong & Zhao, Dawei & Wu, Xiaobo & Tian, Zhihong & Li, Aiping & Wang, Zhen, 2020. "Functional immunization of networks based on message passing," Applied Mathematics and Computation, Elsevier, vol. 366(C).
    3. Raja, Muhammad Asif Zahoor & Mehmood, Ammara & Ashraf, Sadia & Awan, Khalid Mahmood & Shi, Peng, 2022. "Design of evolutionary finite difference solver for numerical treatment of computer virus propagation with countermeasures model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 409-430.
    4. Zhu, Linhe & Liu, Mengxue & Li, Yimin, 2019. "The dynamics analysis of a rumor propagation model in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 118-137.

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