A Computer Virus Propagation Model Using Delay Differential Equations With Probabilistic Contagion And Immunity

TL;DR

This paper introduces a probabilistic delay differential equation model for computer virus propagation that accounts for varying infection and immunity probabilities, enhancing realism over traditional models.

Contribution

It develops and simulates a pSEIRS model incorporating probabilistic contagion and immunity, suitable for realistic scale-free networks, extending traditional epidemiological models to computer viruses.

Findings

The model accurately captures virus spread dynamics in scale-free networks.

Probabilistic immunity significantly affects infection persistence.

Simulation results demonstrate the model's applicability to real-world networks.

Abstract

The SIR model is used extensively in the field of epidemiology, in particular, for the analysis of communal diseases. One problem with SIR and other existing models is that they are tailored to random or Erdos type networks since they do not consider the varying probabilities of infection or immunity per node. In this paper, we present the application and the simulation results of the pSEIRS model that takes into account the probabilities, and is thus suitable for more realistic scale free networks. In the pSEIRS model, the death rate and the excess death rate are constant for infective nodes. Latent and immune periods are assumed to be constant and the infection rate is assumed to be proportional to I (t) N(t), where N (t) is the size of the total population and I(t) is the size of the infected population. A node recovers from an infection temporarily with a probability p and dies from the infection with probability (1-p).