  This work-in-progress paper introduces a prototype for a novel Graph Neural
Network (GNN) based approach to estimate hidden states in cyber attack
simulations. Utilizing the Meta Attack Language (MAL) in conjunction with
Relational Dynamic Decision Language (RDDL) conformant simulations, our
framework aims to map the intricate complexity of cyber attacks with a vast
number of possible vectors in the simulations. While the prototype is yet to be
completed and validated, we discuss its foundational concepts, the
architecture, and the potential implications for the field of computer
security.
