Adaptive Epidemic Dynamics in Networks

Abstract

Theoretical modeling of computer virus/worm epidemic dynamics is an important problem that has attracted many studies. However, most existing models are adapted from biological epidemic ones. Although biological epidemic models can certainly be adapted to capture some computer virus spreading scenarios (especially when the so-called homogeneity assumption holds), the problem of computer virus spreading is not well understood because it has many important perspectives that are not necessarily accommodated in the biological epidemic models. In this article, we initiate the study of such a perspective, namely that of adaptive defense against epidemic spreading in arbitrary networks. More specifically, we investigate a nonhomogeneous Susceptible-Infectious-Susceptible (SIS) model where the model parameters may vary with respect to time. In particular, we focus on two scenarios we call semi-adaptive defense and fully adaptive defense, which accommodate implicit and explicit dependency relationships between the model parameters, respectively. In the semi-adaptive defense scenario, the model’s input parameters are given; the defense is semi-adaptive because the adjustment is implicitly dependent upon the outcome of virus spreading. For this scenario, we present a set of sufficient conditions (some are more general or succinct than others) under which the virus spreading will die out; such sufficient conditions are also known as epidemic thresholds in the literature. In the fully adaptive defense scenario, some input parameters are not known (i.e., the aforementioned sufficient conditions are not applicable) but the defender can observe the outcome of virus spreading. For this scenario, we present adaptive control strategies under which the virus spreading will die out or will be contained to a desired level.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2014
Source ID
10.1145/2555613

Entities

People

  • Li Xu
  • Shouhuai Xu
  • Wenlian Lu
  • Zhenxin Zhan

Organizations

  • Air Force Office of Scientific Research
  • Fudan University
  • Office of Naval Research
  • University of Texas at San Antonio

Tags

Fields of Study

  • Mathematics

Readers

  • Computational Fluid Dynamics (CFD)
  • Infectious Disease/Epidemiology
  • Systems Analysis and Design