### 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 | |

State | Published - 13 May 2002 |

### Fingerprint

### Keywords

- Computer virus
- Differential equation models
- Markov models

### Cite this

*Physics Letters, Section A: General, Atomic and Solid State Physics*,

*297*(3-4), 261-266. https://doi.org/10.1016/S0375-9601(02)00152-4

}

*Physics Letters, Section A: General, Atomic and Solid State Physics*, vol. 297, no. 3-4, pp. 261-266. https://doi.org/10.1016/S0375-9601(02)00152-4

**A unified prediction of computer virus spread in connected networks.** / Billings, Lora; Spears, William M.; Schwartz, Ira B.

Research output: Contribution to journal › Article

TY - JOUR

T1 - A unified prediction of computer virus spread in connected networks

AU - Billings, Lora

AU - Spears, William M.

AU - Schwartz, Ira B.

PY - 2002/5/13

Y1 - 2002/5/13

N2 - 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.

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.

KW - Computer virus

KW - Differential equation models

KW - Markov models

UR - http://www.scopus.com/inward/record.url?scp=0037071123&partnerID=8YFLogxK

U2 - 10.1016/S0375-9601(02)00152-4

DO - 10.1016/S0375-9601(02)00152-4

M3 - Article

AN - SCOPUS:0037071123

VL - 297

SP - 261

EP - 266

JO - Physics Letters, Section A: General, Atomic and Solid State Physics

JF - Physics Letters, Section A: General, Atomic and Solid State Physics

SN - 0375-9601

IS - 3-4

ER -