  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.
We show both in theory and experimental results that our game-focused approach
achieves higher defender expected utility than the two-stage alternative when
there is limited data.
