Multi-Stage Attack Graph Security Games: Heuristic Strategies, with Empirical Game-Theoretic Analysis.

We study the problem of allocating limited security countermeasures to protect network data from cyber-attacks, for scenarios modeled by Bayesian attack graphs. We consider multi-stage interactions between a network administrator and cybercriminals, formulated as a security game. This formulation is capable of representing security environments with significant dynamics and uncertainty, and very large strategy spaces. For the game model, we propose parameterized heuristic strategies for both players. Our heuristics exploit the topological structure of the attack graphs and employ different sampling methodologies to overcome the computational complexity in determining players' actions. Given the complexity of the game, we employ a simulation-based methodology, and perform empirical game analysis over an enumerated set of these heuristic strategies. Finally, we conduct experiments based on a variety of game settings to demonstrate the advantages of our heuristics in obtaining effective defense strategies which are robust to the uncertainty of the security environment.