Reinforcement Learning Attack in Cybersecurity Investments

Abstract

The growing frequency and sophistication of cyberattacks underscore the need for adaptive and collaborative defense mechanisms. This study introduces a public-goods-based game-theoretic framework to model cooperative cybersecurity investments for protecting shared cybersystems. In the proposed setting, agents—representing interconnected entities—repeatedly decide whether to contribute to a collective defense while facing uncertainty in attack outcomes. The attacker is equipped with two distinct strategies: a mixed strategy and a reinforcement learning (RL)-based strategy. Simulation results reveal that the RL-based attacker outperforms the mixed strategy, dynamically adapting to defenders’ behaviors and substantially weakening cooperative investment as the defenders’ enhancement factor increases.