How to build smart, adaptable cyber defenses with LLMs?
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense
October 24, 2024
https://arxiv.org/pdf/2410.17351This paper proposes a new method called H-MARL for creating autonomous cybersecurity systems using multi-agent reinforcement learning. H-MARL breaks down the complex task of defending a network into smaller, manageable sub-tasks like investigating suspicious activity or recovering compromised machines.
Key points for LLM-based multi-agent systems:
- Hierarchical approach tackles complexity: Instead of learning one massive policy, H-MARL uses a master policy to choose between specialized sub-policies, simplifying the learning process. This is particularly useful for LLMs, which often struggle with tasks requiring long sequences of actions.
- Domain expertise enhances learning: H-MARL utilizes cybersecurity knowledge to tailor the information each agent receives, making training more efficient. Similarly, LLMs can benefit from domain-specific knowledge to guide their actions.
- Transfer learning enables adaptation: Trained sub-policies can be adapted to new cyberattacks, eliminating the need to retrain from scratch. This is analogous to how LLMs can be fine-tuned for new tasks without starting training from the beginning.