Design and analysis of cyber warfare model with intelligent predictive stochastic networks for attack-defend strategies on critical infrastructures

The purpose of this study is to develop a novel multi-class differential system (target network, protected network, inside attacker, and outside attacker) for a cyber war model that imitating the cyber war scenarios on different attack surfaces in the presence of multifarious attack vectors and analysis has been presented by stupendous knacks of intelligent predictive stochastic networks (IPSNs). The theoretical convergence analysis through attack-free and attack-invasion equilibrium in terms of basic reproduction number R-0 has been presented along with the global stability of attack-free equilibrium points using the Lyapunov function. The reference solutions for studying the dynamics of all four classes of cyber war models in an air-gapped environment have also been determined in this paper for various scenarios by varying the protection strategy parameter rho, the recovery strategy parameter epsilon, and the rate of attack from an outside/inside attacker using the Adams numerical method. The obtained data has been used in IPSNs as input/targets for training, validation, and testing in order to compute the approximate solution of the model. Extensive simulations have been run to determine the accuracy of the proposed framework for a complex advanced persistent attack targeting industrial systems. The outcomes of IPSNs have shown a fine resemblance with the matching of order 10(-7) from the observed data. Also, the analysis based on mean square error convergence, error histogram, and regression index has further verified the strength and worth of the presented strategy.