The Impact of Complex and Informed Adversarial Behavior in Graphical Coordination Games

How does system-level information impact the ability of an adversary to degrade performance in a networked control system? complexity adversary's strategy affect its performance? This article focuses on these questions context graphical coordination games where can influence given fraction agents system, and follow log-linear learning, well-known distributed learning algorithm. Focusing class homogeneous ring graphs various connectivity, we begin by demonstrating that minimally connected are most susceptible adversarial influence. We then proceed characterize how both sophistication attack strategies (static versus dynamic) informational awareness about network structure be leveraged system performance. set policies induce stochastically stable states, our findings demonstrate relative importance between changes with influencing power adversary. In particular, far outweighs regards degrading damage when is relatively weak. However, opposite true more substantial.