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Research Article | Open Access

A reliable numerical algorithm based on an operational matrix method for treatment of a fractional order computer virus model

Jagdev Singh1,3,4( )Jitendra Kumar1Devendra kumar2,4Dumitru Baleanu4,5
Department of Mathematics, JECRC University, Jaipur, 303905, Rajasthan, India
Department of Mathematics, University of Rajasthan, Jaipur, 302004, Rajasthan, India
Department of Mathematics, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Korea
Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
Institute of Space Sciences, Magurele-Bucharest, Romania
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Abstract

A computer network can detect potential viruses through the use of kill signals, thereby minimizing the risk of virus propagation. In the realm of computer security and defensive strategies, computer viruses play a significant role. Understanding of their spread and extension is a crucial component. To address this issue of computer virus spread, we employ a fractional epidemiological SIRA model by utilizing the Caputo derivative. To solve the fractional-order computer virus model, we employ a computational technique known as the Jacobi collocation operational matrix method. This operational matrix transforms the problem of arbitrary order into a system of nonlinear algebraic equations. To analyze this system of arbitrary order, we derive an approximate solution for the fractional computer virus model, also considering the Vieta Lucas polynomials. Numerical simulations are performed and graphical representations are provided to illustrate the impact of order of the fractional derivative on different profiles.

CLC number: 26A33, 33C45, 65L05, 92D30

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AIMS Mathematics
Pages 3195-3210

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Cite this article:
Singh J, Kumar J, kumar D, et al. A reliable numerical algorithm based on an operational matrix method for treatment of a fractional order computer virus model. AIMS Mathematics, 2024, 9(2): 3195-3210. https://doi.org/10.3934/math.2024155

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Received: 22 October 2023
Revised: 16 November 2023
Accepted: 28 November 2023
Published: 15 February 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)