Towards Robust Autonomous Cyber Defence Agents Using Hybrid AI Models

Recent developments in Software-Defined Defence (SDD) provide the interfaces for multi-layer monitoring and control to realise Autonomous Cyber Defence (ACD) in critical network infrastructure used by the military. As a result, autonomous agents can enforce cybersecurity measures at different layers (link, IP, transport and application) using hybrid Artificial Intelligence (AI) models. For example, Multi-Agent Reinforcement Learning (MARL) can be combined with symbolic AI (knowledge graphs) and augmented/fine-tuned Large Language Models (LLMs) to detect, predict, protect, respond and recover from cyberattacks. This thesis starts from the hypothesis that the robustness of ACD agents can be formally verified, ensuring their safe development in mission critical environments. By applying Formal Verification (FV) techniques to MARL systems, the aim is to provide mathematically grounded guarantees such as correctness, safety, and adversarial resilience, thereby enabling trustworthy autonomous decision-making in SDD-enabled infrastructures.