Propagating mixed uncertainties in cyber attacker payoffs: Exploration of two-phase Monte Carlo sampling and probability bounds analysis

Cyber-system security on a continual basis against a multitude of adverse events is a challenging undertaking. Cybersystem administrators operating with limited protective resources need to account for uncertainties associated with system behavior and types of attackers targeting a system. These uncertainties may arise due to inherent randomness or incomplete knowledge about system behavior and events affecting the system. As a result, uncertainty quantification of attacker payoff functions within stochastic cybersecurity games is a critical area of research interest. This study focuses on operationalizing a probabilistic framework for quantifying attacker payoffs, within a notional case study, through: (1) representation of uncertain attacker and system-related modeling variables as probability distributions and mathematical intervals, and (2) exploration of uncertainty propagation techniques including two-phase Monte Carlo sampling and probability bounds analysis. Enhanced uncertainty representations of payoffs may contribute to further understanding of dynamics between cyber attackers and defenders and advance the state-of-the-art in proactive cyber-system defense and strategic decision-making.