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<p>This paper introduces a prolonged SEIR epidemic model in computer networks, extending the classical SEIR (Susceptible–Exposed–Infectious–Recovered) framework to address the transmission dynamics of a virus and how it can be controlled. By employing artificial neural networks (ANNs) and training them using the Levenberg-Marquardt (LM) and Bayesian regularization backpropagation (trainbr) algorithm, we study the behavior of the model and forecast virus spreading in the network. We find the basic reproduction number (R<sub>0</sub>) of the model and provide essential insight into the threshold for virus persistence or eradication. By stability analysis, we verify that the model has equilibrium at both disease-free and endemic states, which is crucial for designing efficient antivirus measures. The research tests the performance of the model with three split data cases, namely 60-20-20, 70-15-15, and 80-10-10, to assess the training accuracy versus generalization trade-off. For the numerical solution of the differential equations, the Runge-Kutta-Fehlberg method (RKF45) was applied using MATLAB’s built-in ode45 solver. Numerical methods used through MATLAB enable us to compute graphical solutions illustrating the long-term behavior of virus diffusion. This study deepens our knowledge of virus transmission in computer networks and facilitates the creation of stronger defenses against network-based cyber attacks.</p>

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Modeling virus spread in computer networks: an extended SEIR approach using artificial neural networks with Levenberg-marquardt algorithm

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • Saktiprasad Mohanty,
  • Chandrakanta Parida,
  • Prasant Kumar Nayak,
  • Ganeswar Mahanta

摘要

This paper introduces a prolonged SEIR epidemic model in computer networks, extending the classical SEIR (Susceptible–Exposed–Infectious–Recovered) framework to address the transmission dynamics of a virus and how it can be controlled. By employing artificial neural networks (ANNs) and training them using the Levenberg-Marquardt (LM) and Bayesian regularization backpropagation (trainbr) algorithm, we study the behavior of the model and forecast virus spreading in the network. We find the basic reproduction number (R0) of the model and provide essential insight into the threshold for virus persistence or eradication. By stability analysis, we verify that the model has equilibrium at both disease-free and endemic states, which is crucial for designing efficient antivirus measures. The research tests the performance of the model with three split data cases, namely 60-20-20, 70-15-15, and 80-10-10, to assess the training accuracy versus generalization trade-off. For the numerical solution of the differential equations, the Runge-Kutta-Fehlberg method (RKF45) was applied using MATLAB’s built-in ode45 solver. Numerical methods used through MATLAB enable us to compute graphical solutions illustrating the long-term behavior of virus diffusion. This study deepens our knowledge of virus transmission in computer networks and facilitates the creation of stronger defenses against network-based cyber attacks.