Defending Against Man-In-The-Middle Attack in Repeated Games

The Man-in-the-Middle (MITM) attack has become widespread in networks nowadays. The MITM attack would cause serious information leakage and result in tremendous loss to users. Previous work applies game theory to analyze the MITM attack-defense problem and computes the optimal defense strategy to minimize the total loss. It assumes that all defenders are cooperative and the attacker know defenders’ strategies beforehand. However, each individual defender is rational and may not have the incentive to cooperate. Furthermore, the attacker can hardly know defenders’ strategies ahead of schedule in practice. To this end, we assume that all defenders are self-interested and model the MITM attack-defense scenario as a simultaneous-move game. Nash equilibrium is adopted as the solution concept which is proved to be always unique. Given the impracticability of computing Nash equilibrium directly, we propose practical adaptive algorithms for the defenders and the attacker to learn towards the unique Nash equilibrium through repeated interactions. Simulation results show that the algorithms are able to converge to Nash equilibrium strategy efficiently.