The Impact of Complex and Informed Adversarial Behavior in Graphical Coordination Games
Keith Paarporn, Brian Canty, Philip N. Brown, Mahnoosh Alizadeh, Jason, R. Marden

TL;DR
This paper investigates how the complexity and information level of adversaries affect their ability to disrupt networked control systems modeled as graphical coordination games, revealing that adversary influence power determines whether strategy sophistication or information is more critical.
Contribution
It characterizes the impact of adversarial strategy complexity and network information on system degradation in graphical coordination games, highlighting the influence of adversary power.
Findings
Minimally connected ring graphs are most vulnerable.
Sophistication outweighs information when influence is weak.
Information becomes more critical with stronger adversarial influence.
Abstract
How does system-level information impact the ability of an adversary to degrade performance in a networked control system? How does the complexity of an adversary's strategy affect its ability to degrade performance? This paper focuses on these questions in the context of graphical coordination games where an adversary can influence a given fraction of the agents in the system, and the agents follow log-linear learning, a well-known distributed learning algorithm. Focusing on a class of homogeneous ring graphs of various connectivity, we begin by demonstrating that minimally connected ring graphs are the most susceptible to adversarial influence. We then proceed to characterize how both (i) the sophistication of the attack strategies (static vs dynamic) and (ii) the informational awareness about the network structure can be leveraged by an adversary to degrade system performance.…
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