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Towards a Generalisable Cyber Defence Agent for Real-World Computer Networks

arXiv — cs.LG•Thursday, November 13, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
Recent advancements in deep reinforcement learning have led to the development of cyber defence agents capable of protecting simulated networks from cyber-attacks. However, these agents often require retraining to adapt to different network topologies and sizes, limiting their effectiveness in real-world scenarios. The introduction of Topological Extensions for Reinforcement Learning Agents (TERLA) marks a significant step forward, allowing agents to generalize their defence capabilities without the need for retraining. By utilizing heterogeneous graph neural network layers, TERLA creates a fixed-size latent embedding that effectively represents the network state. This innovation is applied to a standard Proximal Policy Optimisation (PPO) agent model, and the research is conducted in the Cyber Autonomy Gym for Experimentation (CAGE) Challenge 4, which simulates realistic network conditions, including Intrusion Detection System (IDS) events. The results indicate that TERLA agents mainta…
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