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.

翻译：本文是一项进展中研究，介绍了一种基于图神经网络（GNN）的原型方法，用于估计网络攻击模拟中的隐藏状态。通过结合元攻击语言（MAL）与关系动态决策语言（RDDL）一致性模拟，本框架旨在映射网络攻击的复杂性与模拟中数量庞大的可能攻击向量。尽管该原型尚未完成与验证，我们探讨了其基础概念、架构以及对计算机安全领域的潜在影响。