Multi-Agent Reinforcement Learning for Maritime Operational Technology
  Cyber Security
Alec Wilson Ryan Menzies Neela Morarji David Foster Marco Casassa Mont Esin Turkbeyler Lisa Gralewski
This paper demonstrates the potential for autonomous cyber defence to be
applied on industrial control systems and provides a baseline environment to
further explore Multi-Agent Reinforcement Learning's (MARL) application to this
problem domain. It introduces a simulation environment, IPMSRL, of a generic
Integrated Platform Management System (IPMS) and explores the use of MARL for
autonomous cyber defence decision-making on generic maritime based IPMS
Operational Technology (OT). OT cyber defensive actions are less mature than
they are for Enterprise IT. This is due to the relatively brittle nature of OT
infrastructure originating from the use of legacy systems, design-time
engineering assumptions, and lack of full-scale modern security controls. There
are many obstacles to be tackled across the cyber landscape due to continually
increasing cyber-attack sophistication and the limitations of traditional
IT-centric cyber defence solutions. Traditional IT controls are rarely deployed
on OT infrastructure, and where they are, some threats aren't fully addressed.
In our experiments, a shared critic implementation of Multi Agent Proximal
Policy Optimisation (MAPPO) outperformed Independent Proximal Policy
Optimisation (IPPO). MAPPO reached an optimal policy (episode outcome mean of
1) after 800K timesteps, whereas IPPO was only able to reach an episode outcome
mean of 0.966 after one million timesteps. Hyperparameter tuning greatly
improved training performance. Across one million timesteps the tuned
hyperparameters reached an optimal policy whereas the default hyperparameters
only managed to win sporadically, with most simulations resulting in a draw. We
tested a real-world constraint, attack detection alert success, and found that
when alert success probability is reduced to 0.75 or 0.9, the MARL defenders
were still able to win in over 97.5% or 99.5% of episodes, respectively.