Learning Security Strategies through Game Play and Optimal Stopping. (arXiv:2205.14694v1 [cs.LG])

We study automated intrusion prevention using reinforcement learning.
Following a novel approach, we formulate the interaction between an attacker
and a defender as an optimal stopping game and let attack and defense
strategies evolve through reinforcement learning and self-play. The
game-theoretic perspective allows us to find defender strategies that are
effective against dynamic attackers. The optimal stopping formulation gives us
insight into the structure of optimal strategies, which we show to have
threshold properties. To obtain the optimal defender strategies, …

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