Optimizing a Model-Agnostic Measure of Graph Counterdeceptiveness via Reattachment

Abstract

Recognition of an adversary's objective is a core problem in physical security and cyber defense. Prior work on target recognition focuses on developing optimal inference strategies given the adversary's operating environment. However, the success of such strategies significantly depends on features of the environment. We consider the problem of optimal counterdeceptive environment design: construction of an environment which promotes early recognition of an adversary's objective, given operational constraints. Viewed as a bounded-length graph-design problem, we introduce a metric for counterdeception and a novel heuristic that maximizes it based on iterative reattachment of trees. We benchmark the performance of this algorithm on synthetic networks as well as a graph inspired by a real-world high-security environment, verifying that the proposed algorithm is computationally feasible and yields meaningful network designs.