MT2M: Strategic Cost-based Optimization of Cyber Defense in Variable Constraints Systems

When confronted with advanced cyber threats, traditional cybersecurity methods often struggle with system performance and cost-effectiveness. Current approaches using game theory for passive applications, such as Moving Target Defense (MTD), lack flexibility in adapting to varying resource criticalities and rely on rigid cost assumptions that overlook the interdependencies among system components. Additionally, these approaches are complex and grow exponentially more complex with the network. This paper presents the \ac{model}, a strategic \ac{mtd} framework based on Bayesian Stackelberg game theory to optimize cyber defense costs. Optimization is done while considering both resource criticality and node capacity. By transforming a complex, NP-hard cost problem into a linear one, \ac{model} enables scalable deployment. Numerical simulations demonstrate that the proposed framework achieves comparable security to traditional methods while reducing defense costs by up to $15\%$. This research establishes a framework that can be later expanded to include a more variable and complex network configuration.