Novel machine intelligent expedition with adaptive autoregressive exogenous neural structure for nonlinear multi-delay differential systems in computer virus propagation

Abstract

Computer viruses are significant from the perspective of reliable computer security for control infrastructure development that connotes the essential requirements for understanding virus spread and growth. This study investigates a delayed epidemiological computer virus model by applying artificial intelligence-inspired computing to analyze the behavior of a multi-delay differential system, incorporating the influence of the latent period on the dynamics of susceptible, infected, quarantined, and recovered computers using an adaptive autoregressive exogenous neural structure with Levenberg-Marquardt backpropagation. The reference data for executing networks is generated using the Adams numerical solver, incorporating parameters such as the computer infection rate, the saturation coefficient for calculating inhibitory effects, the immune loss rate of recovered computers, the number of infected computers in quarantine, the recovery rate, the mortality rate for each differential class, and delays associated with temporary immunity and the incubation period. The designed networks operate on generated synthetic data that are randomly distributed for testing, validation, and training samples to determine the approximate response of the nonlinear delay computer virus systems. The predictive solutions consistently aligned with the reference numerical outcomes indicating an error with negligible magnitude. The accuracy, stability, convergence, and efficiency of the designed intelligent networks are established on a thorough investigation by employing a variety of assessment metrics in terms of mean square error-based convergence curves, time series analysis of predicted outcomes, control parameter adaptation, error frequency distribution analysis, and exhaustive statistics on input-output and cross-correlations.