游戏学术速递[1.10]
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cs.GT游戏，共计2篇
【1】 Asymptotic Security using Bayesian Defense Mechanisms with Application to Cyber Deception
标题：基于贝叶斯防御机制的渐近安全性及其在网络欺骗中的应用
链接：https://arxiv.org/abs/2201.02351
备注：16 pages
摘要：This study addresses the question whether model knowledge can prevent a
defender from being deceived or not in cyber security. As a specific
model-based defense scheme, this study treats Bayesian defense mechanism, which
monitors the system's behavior, forms a belief on existence of the attacker,
and chooses appropriate reactions. Sophisticated attackers aim at achieving her
objective while avoiding being detected by deceiving the defender. In this
paper, their dynamic decision making is formulated as a stochastic signaling
game. It is revealed that the belief on the true scenario has a limit in a
stochastic sense at an equilibrium based on martingale analysis. This fact
implies that there are only two possible cases: the defender asymptotically
detects the attack with a firm belief or the attacker takes actions such that
the system's behavior becomes nominal after a certain finite time step.
Consequently, if the dynamics admits no stealthy attacks, the system is
guaranteed to be secure in an asymptotic manner provided that effective
countermeasures are implemented. The result concludes that model knowledge can
prevent deception in an asymptotic sense. As an application of the finding, a
defensive deception utilizing asymmetric recognition on vulnerabilities
exploited by the attacker is analyzed. It is shown that, the attacker possibly
stops the attack even if the defender is unaware of the vulnerabilities as long
as the defender's unawareness is concealed by the defensive deception. Those
results indicate the powerful defense capability achieved by model knowledge.
【2】 Distributed Nash Equilibrium Seeking over Time-Varying Directed Communication Networks
标题：时变有向通信网络上的分布式纳什均衡求解
链接：https://arxiv.org/abs/2201.02323
摘要：We study distributed algorithms for finding a Nash equilibrium (NE) in a
class of non-cooperative convex games under partial information. Specifically,
each agent has access only to its own smooth local cost function and can
receive information from its neighbors in a time-varying directed communication
network. To this end, we propose a distributed gradient play algorithm to
compute a NE by utilizing local information exchange among the players. In this
algorithm, every agent performs a gradient step to minimize its own cost
function while sharing and retrieving information locally among its neighbors.
The existing methods impose strong assumptions such as balancedness of the
mixing matrices and global knowledge of the network communication structure,
including Perron-Frobenius eigenvector of the adjacency matrix and other graph
connectivity constants. In contrast, our approach relies only on a reasonable
and widely-used assumption of row-stochasticity of the mixing matrices. We
analyze the algorithm for time-varying directed graphs and prove its
convergence to the NE, when the agents' cost functions are strongly convex and
have Lipschitz continuous gradients. Numerical simulations are performed for a
Nash-Cournot game to illustrate the efficacy of the proposed algorithm.
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