Enabling A Network AI Gym for Autonomous Cyber Agents

• URL: http://arxiv.org/abs/2304.01366v1
• Date: Mon, 3 Apr 2023 20:47:03 GMT
• Title: Enabling A Network AI Gym for Autonomous Cyber Agents
• Authors: Li Li, Jean-Pierre S. El Rami, Adrian Taylor, James Hailing Rao, Thomas Kunz
• Abstract summary: This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL) The required RL training environment is particularly challenging, as it must balance the need for high-fidelity, best achieved through real network emulation, with the need for running large numbers of training episodes, best achieved using simulation. A unified training environment namely the Cyber Gym for Intelligent Learning (CyGIL) is developed where an emulated CyGIL-E automatically generates a simulated CyGIL-S.
• Score: 2.789574233231923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work aims to enable autonomous agents for network cyber operations (CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The required RL training environment is particularly challenging, as it must balance the need for high-fidelity, best achieved through real network emulation, with the need for running large numbers of training episodes, best achieved using simulation. A unified training environment, namely the Cyber Gym for Intelligent Learning (CyGIL) is developed where an emulated CyGIL-E automatically generates a simulated CyGIL-S. From preliminary experimental results, CyGIL-S is capable to train agents in minutes compared with the days required in CyGIL-E. The agents trained in CyGIL-S are transferrable directly to CyGIL-E showing full decision proficiency in the emulated "real" network. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real for leveraging RL agents in real-world cyber networks.

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