End-to-End Game-Focused Learning of Adversary Behavior in Security Games

Understand

Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary.

• Motivated by green security, where the defender may only observe an adversary's response to defense on a limited set of targets, we study the problem of learning a defense that generalizes well to a new set of targets with novel feature values and combinations.
• Traditionally, this problem has been addressed via a two-stage approach where an adversary model is trained to maximize predictive accuracy without considering the defender's optimization problem.
• We develop an end-to-end game-focused approach, where the adversary model is trained to maximize a surrogate for the defender's expected utility.