Harnessing the Power of Deception in Attack Graph-Based Security Games

Abstract

We study the use of deception in attack graph-based Stackelberg security games. In our setting, in addition to allocating defensive resources to protect important targets from attackers, the defender can strategically manipulate the attack graph through three main types of deceptive actions. We show that finding the optimal deception and defense strategy is at least NP-hard. We provide two techniques for efficiently solving this problem: a mixed-integer linear program for layered directed acyclic graphs (DAGs) and neural architecture search for general DAGs. We empirically demonstrate that using deception on attack graphs gives the defender a significant advantage, and the algorithms we develop scale gracefully to medium-sized problems.