Heterogeneous Projection of Disruptive Malware Prevalence in Mobile Social Networks

Aldiyar Dabarov, Madiyar Sharipov, Aresh Dadlani, Muthukrishnan Senthil Kumar, Walid Saad, Choong Seon Hong

Research output: Contribution to journalArticlepeer-review

Abstract

Segregating the latency phase from the actual disruptive phase of certain mobile malware grades offers more opportunities to effectively mitigate the viral spread in its early stages. Inspired by epidemiology, in this letter, a stochastic propagation model that accounts for infection latency of disruptive malware in both personal and spatial social links between constituent mobile network user pairs is proposed. To elucidate the true impact of unique user attributes on the virulence of the proposed spreading process, heterogeneity in transition rates is also considered in an approximated mean-field epidemic network model. Furthermore, derivations for the system equilibrium and stability analysis are provided. Simulation results showcase the viability of our model in contrasting between latent and disruptive infection stages with respect to a homogeneous population-level benchmark model.

Original languageEnglish
Article number9086444
Pages (from-to)1673-1677
Number of pages5
JournalIEEE Communications Letters
Volume24
Issue number8
DOIs
Publication statusPublished - Aug 2020

Keywords

  • disruptive virus
  • equilibrium analysis
  • heterogeneous epidemic model
  • mean-field theory
  • Mobile social networks

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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