Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents. (arXiv:2304.01244v1 [cs.LG])

Autonomous cyber agents may be developed by applying reinforcement and deep
reinforcement learning (RL/DRL), where agents are trained in a representative
environment. The training environment must simulate with high-fidelity the
network Cyber Operations (CyOp) that the agent aims to explore. Given the
complexity of net-work CyOps, a good simulator is difficult to achieve. This
work presents a systematic solution to automatically generate a high-fidelity
simulator in the Cyber Gym for Intelligent Learning (CyGIL). Through
representation learning and continuous learning, CyGIL …

agent autonomous complexity continuous cyber cyber operations emulation environment fidelity high may network operations representation simulation simulator solution training work