Planning and Evaluating Mitigation Strategies for Pandemic Attacks on Cybersecurity Using Machine Learning Data

This paper investigates the mathematical modelling of cybercrime attacks on multiple devices connected to the server. This model is a very successful way for cybercrime, bio-mathematics, and artificial intelligence to investigate and comprehend the behaviour of mannerisms with harmful intentions in a computer system. In this computational model, we are studying the factors (i.e., computer viruses, disease infections, and cyberattacks) that affect connected devices. This compartmental model, SEIAR, represents the various hardware utilised during the cyberattack. The letters S, E, I, A, and R are used to represent different stages or groups of individuals in epidemiological models, helping to understand the spread and control of infectious diseases. The dynamics of the previous model are determined by a series of differential equations. The dynamics of the preceding model are determined by a system of differential equations. Numerical solutions of the model are calculated using back propagated Levenberg-Marquardt algorithm (BLMA) and a specific optimization algorithm known as the Levenberg-Marquardt algorithm (LMA). Reference solutions were obtained by using the Runge-Kutta algorithm of order 4 (RK-4). The backpropagated Levenberg-Marquardt algorithm (BLMA), commonly known as the damped least-squares (DLS) method. The outcome of our simulations ensures that our approach is capable of making precise predictions concerning the behavior of real-world phenomena under varying circumstances. The testing, validation, and training of our technique concerning the reference solutions are then used to determine the accuracy of the surrogate solutions obtained by BLMA. Convergence analysis, error histograms, regression analysis, and curve fitting were used for each differential equation to examine the robustness and accuracy of the design strategy.