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Considering Quarantine in the SIRA Malware Propagation Model

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  • José Roberto C. Piqueira
  • Cristiane M. Batistela

Abstract

As the beginning of the century was marked by a strong development in data science and, consequently, in computer networks, models for designing preventive actions against intruding, data stealing, and destruction became mandatory. Following this line, several types of epidemiological models have been developed and improved, considering different operational approaches. The development of the research line using traditional SIR(Susceptible, Infected, Removed) model for data networks started in the 1990s. In 2005, an epidemiological compartmental model containing antidotal nodes, SIRA (Susceptible, Infected, Removed, Antidotal), was introduced to study how the antivirus policies affect the network reliability. The idea here is to study the consequence of quarantine actions in a network by modifying the SIRA model, introducing quarantine nodes generating the SIQRA (Susceptible, Infected, Quarantine, Removed, Antidotal) model. Analytical and numerical approaches result in parameter conditions for the existence and stability of disease-free and endemic equilibrium points for two different cases: saturation and nonsaturation of the quarantine population block. Based on these results, operational actions can be planned to improve the network reliability.

Suggested Citation

  • José Roberto C. Piqueira & Cristiane M. Batistela, 2019. "Considering Quarantine in the SIRA Malware Propagation Model," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, November.
  • Handle: RePEc:hin:jnlmpe:6467104
    DOI: 10.1155/2019/6467104
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    Cited by:

    1. Piqueira, José Roberto C. & Cabrera, Manuel A.M. & Batistela, Cristiane M., 2021. "Malware propagation in clustered computer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    2. Giovanni Dieguez & Cristiane Batistela & José R. C. Piqueira, 2023. "Controlling COVID-19 Spreading: A Three-Level Algorithm," Mathematics, MDPI, vol. 11(17), pages 1-39, September.
    3. Avcı, İbrahim & Hussain, Azhar & Kanwal, Tanzeela, 2023. "Investigating the impact of memory effects on computer virus population dynamics: A fractal–fractional approach with numerical analysis," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    4. Yu, Zhenhua & Gao, Hongxia & Wang, Dan & Alnuaim, Abeer Ali & Firdausi, Muhammad & Mostafa, Almetwally M., 2022. "SEI2RS malware propagation model considering two infection rates in cyber–physical systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    5. Jose D. Hernandez Guillen & Angel Martin del Rey & Roberto Casado-Vara, 2021. "Propagation of the Malware Used in APTs Based on Dynamic Bayesian Networks," Mathematics, MDPI, vol. 9(23), pages 1-16, November.
    6. Batistela, Cristiane M. & Correa, Diego P.F. & Bueno, Átila M & Piqueira, José Roberto C., 2021. "SIRSi compartmental model for COVID-19 pandemic with immunity loss," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

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