Multi-agent reinforcement learning (MARL) algorithms gradually learn good (ideally optimal) strategies with respect to long-term goals through trial-and-error interactions with both the opponent and the unknown dynamic environment.
The Stochastic Game (SG), together with MARL can address the environmental dynamics in security games in a systematic manner.
 A comprehensive survey of multi-agent reinforcement learning, by L. Busoniu, R. Babuska, and B. De Schutter, in IEEE Trans. Syst., Man, Cybern. C, 2008
 Improving Learning and Adaptation in Security Games by Exploiting Information Asymmetry, by Xiaofan He. Huaiyu Dai and Peng Ning, in INFOCOM 2015