Towards Autonomous Cyber Operation Agents: Exploring the Red Case

TL;DR

This paper explores the challenges of developing autonomous cyber operation agents using reinforcement learning, focusing on high-fidelity simulation and generalizability in dynamic cyber environments.

Contribution

It discusses essential requirements and potential solutions for training autonomous agents in complex, variable cyber networks, supported by preliminary experiments in a specialized testbed.

Findings

Identified key challenges in simulating cyber environments for RL training

Proposed potential solutions for improving agent generalizability

Conducted preliminary experiments demonstrating feasibility

Abstract

Recently, reinforcement and deep reinforcement learning (RL/DRL) have been applied to develop autonomous agents for cyber network operations(CyOps), where the agents are trained in a representative environment using RL and particularly DRL algorithms. The training environment must simulate CyOps with high fidelity, which the agent aims to learn and accomplish. A good simulator is hard to achieve due to the extreme complexity of the cyber environment. The trained agent must also be generalizable to network variations because operational cyber networks change constantly. The red agent case is taken to discuss these two issues in this work. We elaborate on their essential requirements and potential solution options, illustrated by some preliminary experimentations in a Cyber Gym for Intelligent Learning (CyGIL) testbed.