🤖 AI Summary
Autonomous network defense agents suffer from poor generalization and limited knowledge transferability under dynamic threats, heterogeneous network environments, and multiple conflicting objectives.
Method: This paper proposes a unified modeling framework based on Open-ended Reinforcement Learning (OEL). It jointly encodes attack patterns, network topologies, and defense objectives via task embeddings to establish a consistent state-action-reward space; integrates multi-objective reward shaping with a scalable simulation environment to enable continual learning and cross-task policy transfer.
Contribution/Results: Experiments demonstrate significant improvements in agent robustness and adaptability to unseen attack types and network topologies. The framework provides a reusable training paradigm and principled guidelines for benchmark design in AI-driven autonomous network defense.
📝 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.