Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies. (arXiv:2211.15557v2 [cs.LG] UPDATED)

Advancements in reinforcement learning (RL) have inspired new directions in
intelligent automation of network defense. However, many of these advancements
have either outpaced their application to network security or have not
considered the challenges associated with implementing them in the real-world.
To understand these problems, this work evaluates several RL approaches
implemented in the second edition of the CAGE Challenge, a public competition
to build an autonomous network defender agent in a high-fidelity network
simulator. Our approaches all build on …

autonomous beyond defense network policies