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 article 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 the sophistication of the attack strategies (static versus dynamic) and the informational awareness about the network structure can be leveraged by an adversary to degrade system performance. Focusing on the set of adversarial policies that induce stochastically stable states, our findings demonstrate that the relative importance between sophistication and information changes with the influencing power of the adversary. In particular, sophistication far outweighs informational awareness with regards to degrading system-level damage when the adversary's influence power is relatively weak. However, the opposite is true when an adversary's influence power is more substantial.

中文翻译：

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复杂和知情的对抗行为在图形协调游戏中的影响

系统级信息如何影响对手降低网络控制系统性能的能力？对手策略的复杂性如何影响其降低绩效的能力？本文将重点讨论图形协调游戏中的这些问题，其中对手可以影响系统中特定部分的代理，并且代理遵循对数线性学习（一种著名的分布式学习算法）。着眼于一类具有各种连通性的同构环形图，我们首先说明最小连接的环形图最容易受到对抗性影响。然后，我们继续描述攻击者如何利用复杂的攻击策略（静态与动态）和有关网络结构的信息意识来降低系统性能。着眼于诱导随机稳定状态的一系列对抗性策略，我们的发现表明，复杂性和信息之间的相对重要性随对手的影响力而变化。特别是，当对手的影响力相对较弱时，就降低系统级损害而言，复杂性远胜于信息意识。但是，当对手的影响力更大时，情况恰恰相反。我们的发现表明，复杂程度和信息之间的相对重要性随对手的影响力而变化。特别是，当对手的影响力相对较弱时，就降低系统级损害而言，复杂性远胜于信息意识。但是，当对手的影响力更大时，情况恰恰相反。我们的发现表明，复杂程度和信息之间的相对重要性随对手的影响力而变化。特别是，当对手的影响力相对较弱时，就降低系统级损害而言，复杂性远胜于信息意识。但是，当对手的影响力更大时，情况恰恰相反。