Discrete stochastic modelling of computer viruses prevalence on a reduced scale-free network Online publication date: Thu, 03-Sep-2020
by Mohamed Essouifi; Abdelfattah Achahbar
International Journal of Computer Applications in Technology (IJCAT), Vol. 63, No. 3, 2020
Abstract: We construct a stochastic version of the deterministic epidemiological model, introduced by Yang and Yang in 2014, to study computer viruses spreading across a network with two degrees. We compare the results expected by these two approaches. To be closer to real situations, where randomness is always present, we use the discrete time Markov chain method to explore the dynamic behaviour of infection and susceptibility densities of nodes in the network. Monte Carlo simulations, in good agreement with the deterministic approach especially when the proportion N1/N2 increases, lead to the conclusion that any attempt to entirely eradicate network viruses would prove unavailing. As a consequence, the only alternative choice is to contain their prevalence by adopting countermeasures. Our stochastic modelling approach is systematic and shows a good skill in predicting the dynamic behaviour of the computer virus propagation.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook