Virus Propagation Modeling and Convergence Analysis in Large-Scale Networks

Xu Wang, Wei Ni, Kangfeng Zheng, Ren Ping Liu, Xinxin Niu

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


Biological epidemic models, widely used to model computer virus propagations, suffer from either limited scalability to large networks, or accuracy loss resulting from simplifying approximations. In this paper, a discrete-time absorbing Markov process is constructed to precisely characterize virus propagations. Conducting eigenvalue analysis and Jordan decomposition to the process, we prove that the virus extinction rate, i.e., the rate at which the Markov process converges to a virus-free absorbing state, is bounded. The bounds, depending on the infection and curing probabilities, and the minimum degree of the network topology, have closed forms. We also reveal that the minimum curing probability for a given extinction rate requirement, specified through the upper bound, is independent of the explicit size of the network. As a result, we can interpret the extinction rate requirement of a large network with that of a much smaller one, evaluate its minimum curing requirement, and achieve simplifications with negligible loss of accuracy. Simulation results corroborate the effectiveness of the interpretation, as well as its analytical accuracy in large networks.

Original languageEnglish
Article number7492191
Pages (from-to)2241-2254
Number of pages14
JournalIEEE Transactions on Information Forensics and Security
Issue number10
Publication statusPublished - 1 Oct 2016
Externally publishedYes


  • Computer virus
  • epidemic model
  • large networks
  • Markov process

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