Piecewise Linear and Stochastic Models for the Analysis of Cyber Resilience

We model a vehicle equipped with an autonomous cyber-defense system in addition to its inherent physical resilience features. When attacked, this ensemble of cyber-physical features (i.e., ``bonware'') strives to resist and recover from the performance degradation caused by the malware's attack. We model the underlying differential equations governing such attacks for piecewise linear characterizations of malware and bonware, develop a discrete time stochastic model, and show that averages of instantiations of the stochastic model approximate solutions to the continuous differential equation. We develop a theory and methodology for approximating the parameters associated with these equations.
Comment: 6 pages, Invited Session on "Estimation and Learning in Stochastic Systems" for the 57th Annual Conference on Information Sciences and Systems. Co-sponsorship of Johns Hopkins University and the IEEE Information Theory Society