Strategic Decision-Making in Uncertain Turn-Based Security Games

Summary

This paper proposes a robust optimization framework for cybersecurity decision-making in turn-based security games over probabilistic attack graphs. It handles uncertainty in the attacker's state and in control effectiveness using repeated leader-multi-follower games, with a solution that minimizes the geometric mean across all possible worlds. The authors prove the solution is Pareto optimal, equivalent to a standard leader-follower game over a sequence of scenarios, and robust, and report experiments where it outperforms minmax regret.