Intrusion Detection Systems (IDS) are key components for securing critical infrastructures, capable of detecting malicious activities on networks or hosts. However, the efficiency of an IDS depends primarily on both its configuration and its precision. The large amount of network traffic that needs to be analyzed, in addition to the increase in attacks' sophistication, renders the optimization of intrusion detection an important requirement for infrastructure security, and a very active research subject. In the state of the art, a number of approaches have been proposed to improve the efficiency of intrusion detection and response systems. In this article, we review the works relying on decision-making techniques focused on game theory and Markov decision processes to analyze the interactions between the attacker and the defender, and classify them according to the type of the optimization problem they address. While these works provide valuable insights for decision-making, we discuss the limitations of these solutions as a whole, in particular regarding the hypotheses in the models and the validation methods. We also propose future research directions to improve the integration of game-theoretic approaches into IDS optimization techniques