A Quantitative Framework for the Validation of Twin-Based Cyber Defense

The validation of cyber defense strategies is a critical challenge that becomes even more critical as we consider the lack of realistic data on intrusions enabled by new attack strategies. This paper presents a framework for continuous validation using security twins. Our approach is founded on creating a high-fdelity digital model of an ICT infrastructure, the security twin, and another model of an adversary, an attacker. We use these twins to apply a Monte Carlo method that runs a number of simulations of the intrusions of the attacker. Using the output of these simulations, we generate and validate an Intrusion Graph, a model that details how vulnerabilities can be exploited to orchestrate intrusions into the infrastructure. Each step in the simulated intrusion is validated through a system of pre- and postconditions, ensuring logical and temporal consistency. The primary advantage of this approach is its non-intrusive nature and this results in a rigorous validation and the generation of high-quality synthetic data without disrupting the operational infrastructure. This validated model serves as a powerful tool for training AI-driven defense agents, evaluating countermeasures, and predicting the impact of emerging threats in a dynamic risk landscape.