Machine learning-driven exogenous neural architecture for nonlinear fractional cybersecurity awareness model in mobile malware propagation

A vulnerable mobile device remains a critical concern for the sustainable development of information security infrastructure, and the massive increase in mobile malware propagation further amplifies the need for heightened cybersecurity awareness among mobile users. In this paper, a novel framework is presented to explore the machine learning solutions for nonlinear fractional cybersecurity awareness on mobile malware propagation (NFCSA-MMP) model by constructing multilayer autoregressive exogenous networks (MARXNs) trained iteratively by the Levenberg-Marquardt (MARXNs-LM) algorithm. The NFCSA-MMP system represented with Unaware-Susceptible, Aware-Susceptible, Latent, Breakout, Quarantine and Recovery fractional compartments models the different stages of mobile devices states during malware propagation and recovery. To scrutinize the propagation mechanism of mobile malware, the simulation data generated by utilizing Grünwald–Letnikov (GL) fractional finite difference-based computing procedure for NFCSA-MMP model for both integer and fractional ordered values corresponding to variation in the rate of security-aware mobile devices connected to a network, the rate of latent mobile devices becomes breakout, and the recovery rates of latent, breakout, and quarantined devices due to treatment. The proposed methodology MARXNs-LM is executed on acquired datasets randomly sectioned into training, testing and validation samples by achieving the minimum value of the mean square error (MSE) to determine the machine predictive solution of NFCSA-MMP for each scenario. The vigorousness of proposed MARXNs-LM scheme proven by comparative analysis on convergence trends on reduction of MSE, magnitude of absolute deviation, input-output correlation, error histograms and error autocorrelation statistics for solving stiff NFCSA-MMP model.