Deception Against Data-Driven Linear-Quadratic Control
The paper addresses how a defender can use deceptive feedback to mislead an adversary into learning a suboptimal attack policy against an unknown system. The goal is to steer the adversary's learned gain towards a desired benign setting while maintaining system stability and minimizing the deception effort. A numerical method, block successive over-relaxation, is proposed to solve the resulting coupled algebraic Riccati and Lyapunov equations.