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

## TL;DR

This paper introduces an end-to-end learning method for security games that directly optimizes defender utility, outperforming traditional two-stage models especially with limited data.

## Contribution

The paper proposes a novel game-focused learning approach that trains adversary models end-to-end to maximize defender utility, improving generalization and performance.

## Key findings

- Game-focused approach outperforms two-stage models in experiments.
- Method achieves higher defender utility with limited data.
- Theoretical analysis supports empirical results.

## Abstract

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.