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 language | English |
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Article number | 9086444 |
Pages (from-to) | 1673-1677 |
Number of pages | 5 |
Journal | IEEE Communications Letters |
Volume | 24 |
Issue number | 8 |
DOIs | |
Publication status | Published - Aug 2020 |
Funding
Manuscript received February 8, 2020; revised March 23, 2020; accepted May 1, 2020. Date of publication May 6, 2020; date of current version August 12, 2020. This research was supported by the Faculty Development Competitive Research Grant (No. 240919FD3918), Nazarbayev University. The associate editor coordinating the review of this letter and approving it for publication was T. Han. (Corresponding author: Aresh Dadlani.) Aldiyar Dabarov, Madiyar Sharipov, and Aresh Dadlani are with the Department of ECE, Nazarbayev University, Nur-Sultan 010000, Kazakhstan (e-mail: [email protected]; [email protected]; [email protected]).
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