A unified prediction of computer virus spread in connected networks

Lora Billings; William M. Spears; Ira B. Schwartz

doi:10.1016/S0375-9601(02)00152-4

A unified prediction of computer virus spread in connected networks

Lora Billings, William M. Spears, Ira B. Schwartz

Research output: Contribution to journal › Article › peer-review

95 Scopus citations

Abstract

We derive two models of viral epidemiology on connected networks and compare results to simulations. The differential equation model easily predicts the expected long term behavior by defining a boundary between survival and extinction regions. The discrete Markov model captures the short term behavior dependent on initial conditions, providing extinction probabilities and the fluctuations around the expected behavior. These analysis techniques provide new insight on the persistence of computer viruses and what strategies should be devised for their control.

Original language	English
Pages (from-to)	261-266
Number of pages	6
Journal	Physics Letters, Section A: General, Atomic and Solid State Physics
Volume	297
Issue number	3-4
DOIs	https://doi.org/10.1016/S0375-9601(02)00152-4
State	Published - 13 May 2002

Keywords

Computer virus
Differential equation models
Markov models

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10.1016/S0375-9601(02)00152-4

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abstract = "We derive two models of viral epidemiology on connected networks and compare results to simulations. The differential equation model easily predicts the expected long term behavior by defining a boundary between survival and extinction regions. The discrete Markov model captures the short term behavior dependent on initial conditions, providing extinction probabilities and the fluctuations around the expected behavior. These analysis techniques provide new insight on the persistence of computer viruses and what strategies should be devised for their control.",

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AB - We derive two models of viral epidemiology on connected networks and compare results to simulations. The differential equation model easily predicts the expected long term behavior by defining a boundary between survival and extinction regions. The discrete Markov model captures the short term behavior dependent on initial conditions, providing extinction probabilities and the fluctuations around the expected behavior. These analysis techniques provide new insight on the persistence of computer viruses and what strategies should be devised for their control.

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KW - Markov models

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A unified prediction of computer virus spread in connected networks

Abstract

Keywords

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