Resilient Adaptively Distributed Nash Equilibrium Seeking Against Unknown False Data Injection Attacks in Noncooperative Games

This article addresses an attack-resilient adaptively distributed Nash Equilibrium (NE) seeking issue for noncooperative games under malicious false data injection (FDI) attacks. Different from these distributed NE seeking in many existing literature under ideal environments, it is challenging to achieve distributed NE seeking under the impacts of unknown attacks. An attack-resilient NE seeking framework that is distributed (i.e., independent of global information), is established by using the consensus-based gradient play together with an identifier design to compensate for the adverse impacts of FDI attacks. Another key characteristic is that FDI attacks are allowed to be completely unknown and unbounded. Lastly, simulation results are presented to illustrate the designs' effectiveness.