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This work-in-progress paper introduces a prototype for a novel Graph NeuralNetwork (GNN) based approach to estimate hidden states in cyber attacksimulations. Utilizing the Meta Attack Language (MAL) in conjunction withRelational Dynamic Decision Language (RDDL) conformant simulations, ourframework aims to map the intricate complexity of cyber attacks with a vastnumber of possible vectors in the simulations. While the prototype is yet to becompleted and validated, we discuss its foundational concepts, thearchitecture, and the potential implications for the field of computersecurity.

Towards a Graph Neural Network-Based Approach for Estimating Hidden States in Cyber Attack Simulations