The mathematical modeling of cybersecurity decision-making heavily relies on cybersecurity metrics. However, achieving precision in these metrics is notoriously challenging, and their inaccuracies can significantly influence model outcomes. This paper explores resilience to uncertainties in the effectiveness of security controls. We ...
The mathematical modeling of cybersecurity decision-making heavily relies on cybersecurity metrics. However, achieving precision in these metrics is notoriously challenging, and their inaccuracies can significantly influence model outcomes. This paper explores resilience to uncertainties in the effectiveness of security controls. We employ probabilistic attack graphs to model threats and introduce two resilient models: minmax regret and min-product of risks, comparing their performance.
Building on previous Stackelberg game models for cybersecurity, our approach leverages totally unimodular matrices and linear programming (LP) duality to provide efficient solutions. While minmax regret is a well-known approach in robust optimization, our extensive simulations indicate that, in this context, the lesser-known min-product of risks offers superior resilience.
To demonstrate the practical utility and robustness of our framework, we include a multi-dimensional decision support case study focused on home IoT cybersecurity investments, highlighting specific insights and outcomes. This study illustrates the framework’s effectiveness in real-world settings.
