Researchers from Imperial College London have introduced a novel approach to autonomous cyber defence, leveraging entity-based reinforcement learning to enhance the adaptability of defensive agents in dynamic network environments. The team, led by Isaac Symes Thompson, Alberto Caron, Chris Hicks, and Vasilios Mavroudis, has developed a method that reframes the challenge of cyber defence using a more flexible, entity-centric framework.
Traditional deep reinforcement learning methods often struggle with generalisation across diverse network topologies and configurations. This limitation arises because standard approaches, which rely on fixed-size observation and action spaces, are ill-suited to the dynamic nature of real-world networks. In an enterprise network, for example, devices frequently join and leave, creating a constantly shifting landscape that traditional reinforcement learning agents find difficult to navigate effectively.
To address this challenge, the researchers have proposed an entity-based reinforcement learning framework. This approach decomposes the observation and action spaces into a collection of discrete entities, allowing for more specialised policy parameterisations. By doing so, the agents can better adapt to varying network topologies and configurations, a critical capability for autonomous cyber defence.
The team trained a Transformer-based policy on the Yawning Titan cyber-security simulation environment, testing its generalisation capabilities across different network topologies. The results were promising: the entity-based approach significantly outperformed a multi-layer perceptron (MLP)-based policy when trained across fixed-size networks of varying topologies. Moreover, the new method matched the performance of the MLP-based policy when trained on a single network, demonstrating its versatility.
One of the most compelling findings was the potential for zero-shot generalisation. The entity-based reinforcement learning agents showed the ability to adapt to networks of different sizes than those encountered during training. This capability is particularly valuable in real-world scenarios where network configurations can change rapidly and unpredictably.
The implications of this research are substantial for the field of autonomous cyber defence. By enabling more generalisable policies, entity-based reinforcement learning could enhance the effectiveness of defensive agents in real-world network environments. This advancement could lead to more robust and adaptable cyber defence systems, better equipped to handle the complexities and dynamism of modern networks.
As the threat landscape continues to evolve, the need for adaptive and intelligent cyber defence solutions becomes increasingly critical. The work of Thompson, Caron, Hicks, and Mavroudis represents a significant step forward in this domain, offering a promising path towards more resilient and effective autonomous cyber defence systems. Read the original research paper here.