CO-DECYBER: Co-operative Decision Making for Cybersecurity Using Deep Multi-agent Reinforcement Learning

Autonomous decision making for cyber-defence in operational situations is desirable but challenging. This is due to the nature of operational technology (because of its cyber-physical nature) as well as the need to account for multiple contexts. Our contribution is the creation of a co-operative decision-making framework to enable autonomous cyber-defence (which we call Co-Decyber). This framework allows us to break up a big multi-contextual action space into smaller decisions that multiple agents can optimize between. We apply this framework to an autonomous vehicle platooning scenario. Results show that Co-Decyber agents are outperforming random reference agents in the cyber-attack scenarios we have tested. We aim to extend this work with more complex attack scenarios, along with training more agents to defend more of the attack surface. We conclude that this framework when mature will contribute to the goal of providing autonomous cyber-defence for operational technology.