Un breve análisis de la diseminación de la información en redes informáticas a partir de modelos epidemiológicos

Abstract

Currently one of the most valuable assets in computer networks is the data that travels through the various nodes that make up network topologies. In most cases, the presence of large volumes of data on the networks allows them to be damaged due to the alteration of the information by some type of virus, malware, or Trojan, which generates a spread of disease in the network. Faced with this situation, we propose the use of a classical model of infectious disease based on SEIRS, which allows us to propose a discrete space-time framework for the spread of the disease. Three types of connectivity are considered that are represented through a neighborhood relationship for which a grid of size n x n is used. The types of neighborhoods considered are those of Von Neumann, Moore, and L. Specifically, the use of local rules and the type of neighborhood define the dynamics of the spread of the infectious disease. Then we present an approach to the evolution of deterministic models of differential equations (ODE), such as the SIR and SEIRS models to estimate the parameters of this discrete model from data. We illustrate the proposed approach for computer network data using parametric data. More than that, the AIDS Epidemic in Cuba (1986-2000) and the Bubonic Plague Epidemic in India (1908) are successfully modeled from our approach. This work helps to graph equivalences between two conceptually different models and highlights that they give similar results when appropriately taking the values ​​of the parameters.