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The Impact of 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? How does the complexity of an adversary affect its ability to degrade performance? In this thesis, we focus on these questions in the context of graphical coordination games where an adversary can influence a given fraction of the agents in the system. Focusing on the class of ring graphs, we use potential game and resistance tree arguments to explicitly highlight how knowledge of the graph structure and agent identities can be exploited by an adversary to significantly degrade system performance. We demonstrate how the lack of such knowledge drastically reduces the potential harm an adversary can do to the system. Additionally, we show that the ability to employ more complex strategies enables an adversary to do significantly more harm to the system compared to a less capable adversary.

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