This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four...This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four compartments:susceptible,latently infected,breaking-out,and antivirus-capable systems.By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems,aspects inadequately represented by traditional integer-order models.Under Lipschitz continuity and boundedness assumptions,the existence and uniqueness of solutions are rigorously established via fixed-point theory.We develop a tailored two-step Adams-Bashforth numerical scheme for the CF framework and prove its second-order accuracy.Extensive numerical simulations across various fractional orders reveal that memory effects significantly influence virus transmission and control dynamics;smaller fractional orders produce more pronounced memory effects,delaying both infection spread and antivirus activation.Further theoretical analysis,including Hyers-Ulam stability and sensitivity assessments,reinforces the model’s robustness and identifies key parameters governing virus dynamics.The study also extends the framework to incorporate stochastic effects through a stochastic CF formulation.These results underscore fractional-order modeling as a powerful analytical tool for developing robust and effective cybersecurity strategies.展开更多
This study investigates the dynamics of pneumococcal pneumonia using a novel fractal-fractional Susceptible-Carrier-Infected-Recovered model formulated with the Atangana-Baleanu in Caputo(ABC)sense.Unlike traditional ...This study investigates the dynamics of pneumococcal pneumonia using a novel fractal-fractional Susceptible-Carrier-Infected-Recovered model formulated with the Atangana-Baleanu in Caputo(ABC)sense.Unlike traditional epidemiological models that rely on classical or Caputo fractional derivatives,the proposed model incorporates nonlocal memory effects,hereditary properties,and complex transmission dynamics through fractalfractional calculus.The Atangana-Baleanu operator,with its non-singular Mittag-Leffler kernel,ensures a more realistic representation of disease progression compared to classical integer-order models and singular kernel-based fractional models.The study establishes the existence and uniqueness of the proposed system and conducts a comprehensive stability analysis,including local and global stability.Furthermore,numerical simulations illustrate the effectiveness of the ABC operator in capturing long-memory effects and nonlocal interactions in disease transmission.The results provide valuable insights into public health interventions,particularly in optimizing vaccination strategies,treatment approaches,and mitigation measures.By extending epidemiological modeling through fractal-fractional derivatives,this study offers an advanced framework for analyzing infectious disease dynamics with enhanced accuracy and predictive capabilities.展开更多
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four compartments:susceptible,latently infected,breaking-out,and antivirus-capable systems.By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems,aspects inadequately represented by traditional integer-order models.Under Lipschitz continuity and boundedness assumptions,the existence and uniqueness of solutions are rigorously established via fixed-point theory.We develop a tailored two-step Adams-Bashforth numerical scheme for the CF framework and prove its second-order accuracy.Extensive numerical simulations across various fractional orders reveal that memory effects significantly influence virus transmission and control dynamics;smaller fractional orders produce more pronounced memory effects,delaying both infection spread and antivirus activation.Further theoretical analysis,including Hyers-Ulam stability and sensitivity assessments,reinforces the model’s robustness and identifies key parameters governing virus dynamics.The study also extends the framework to incorporate stochastic effects through a stochastic CF formulation.These results underscore fractional-order modeling as a powerful analytical tool for developing robust and effective cybersecurity strategies.
基金funded by the Research,Development,and Innovation Authority(RDIA)-Kingdom of Saudi Arabia-with grant number 12803-baha-2023-BU-R-3-1-EI.
文摘This study investigates the dynamics of pneumococcal pneumonia using a novel fractal-fractional Susceptible-Carrier-Infected-Recovered model formulated with the Atangana-Baleanu in Caputo(ABC)sense.Unlike traditional epidemiological models that rely on classical or Caputo fractional derivatives,the proposed model incorporates nonlocal memory effects,hereditary properties,and complex transmission dynamics through fractalfractional calculus.The Atangana-Baleanu operator,with its non-singular Mittag-Leffler kernel,ensures a more realistic representation of disease progression compared to classical integer-order models and singular kernel-based fractional models.The study establishes the existence and uniqueness of the proposed system and conducts a comprehensive stability analysis,including local and global stability.Furthermore,numerical simulations illustrate the effectiveness of the ABC operator in capturing long-memory effects and nonlocal interactions in disease transmission.The results provide valuable insights into public health interventions,particularly in optimizing vaccination strategies,treatment approaches,and mitigation measures.By extending epidemiological modeling through fractal-fractional derivatives,this study offers an advanced framework for analyzing infectious disease dynamics with enhanced accuracy and predictive capabilities.
