Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies

• URL: http://arxiv.org/abs/2211.15557v2
• Date: Wed, 30 Nov 2022 14:35:42 GMT
• Title: Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies
• Authors: Melody Wolk, Andy Applebaum, Camron Dennler, Patrick Dwyer, Marina Moskowitz, Harold Nguyen, Nicole Nichols, Nicole Park, Paul Rachwalski, Frank Rau, Adrian Webster
• Abstract summary: This work evaluates several reinforcement learning approaches implemented in the second edition of the CAGE Challenge. We find that the ensemble RL technique performs strongest, outperforming our other models and taking second place in the competition. In unseen environments, all of our approaches perform worse, with varied degradation based on the type of environmental change.
• Score: 0.8785883427835897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 the Proximal Policy Optimization (PPO) family of algorithms, and include hierarchical RL, action masking, custom training, and ensemble RL. We find that the ensemble RL technique performs strongest, outperforming our other models and taking second place in the competition. To understand applicability to real environments we evaluate each method's ability to generalize to unseen networks and against an unknown attack strategy. In unseen environments, all of our approaches perform worse, with degradation varied based on the type of environmental change. Against an unknown attacker strategy, we found that our models had reduced overall performance even though the new strategy was less efficient than the ones our models trained on. Together, these results highlight promising research directions for autonomous network defense in the real world.

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