Training RL Agents for Multi-Objective Network Defense Tasks

TL;DR

This paper introduces a novel training approach inspired by open-ended learning to develop robust, generalizable AI agents for multi-objective network defense, addressing key technical challenges in task representation and transferability.

Contribution

It proposes a task representation framework enabling AI agents to adapt across diverse cybersecurity scenarios, advancing open-ended learning applications in cyber defense.

Findings

OEL principles improve robustness of cyber defense agents

Proposed task representation maintains consistent interfaces across tasks

Agents trained with this approach generalize better to varied attack scenarios

Abstract

Open-ended learning (OEL) -- which emphasizes training agents that achieve broad capability over narrow competency -- is emerging as a paradigm to develop artificial intelligence (AI) agents to achieve robustness and generalization. However, despite promising results that demonstrate the benefits of OEL, applying OEL to develop autonomous agents for real-world cybersecurity applications remains a challenge. We propose a training approach, inspired by OEL, to develop autonomous network defenders. Our results demonstrate that like in other domains, OEL principles can translate into more robust and generalizable agents for cyber defense. To apply OEL to network defense, it is necessary to address several technical challenges. Most importantly, it is critical to provide a task representation approach over a broad universe of tasks that maintains a consistent interface over goals, rewards and action spaces. This way, the learning agent can train with varying network conditions, attacker behaviors, and defender goals while being able to build on previously gained knowledge. With our tools and results, we aim to fundamentally impact research that applies AI to solve cybersecurity problems. Specifically, as researchers develop gyms and benchmarks for cyber defense, it is paramount that they consider diverse tasks with consistent representations, such as those we propose in our work.