Whats in a Game? An Intelligent and Adaptive Approach to Security
Understanding the complex defender-adversary interaction in any adversarial interaction allows for the design of intelligent and adaptive defense. Game theory is a natural model for such multi-agent interaction. However, significant challenges need to be overcome in order to apply game theory in practice. In this talk, I will present my work on addressing two such challenges: scalability and learning adversary behavior. First, I will present a game model of screening of passengers at airports and a novel optimization approach based on randomized allocation and disjunctive programming techniques to solve large instances of the problem. Next, I will present an approach that learns adversary behavior and then plans optimal defensive actions, thereby bypassing standard game-theoretic assumptions such as rationality. However, a formal Probably Approximately Correct (PAC) model analysis of the learning module in such an approach reveals possible conditions under which learning followed by optimization can produce sub-optimal results. This emphasizes the need of formal compositional reasoning when using learning components in large systems.