  Currently the state of the art network models are based or depend on Discrete
Event Simulation (DES). While DES is highly accurate, it is also
computationally costly and cumbersome to parallelize, making it unpractical to
simulate high performance networks. Additionally, simulated scenarios fail to
capture all of the complexities present in real network scenarios. While there
exists network models based on Machine Learning (ML) techniques to minimize
these issues, these models are also trained with simulated data and hence
vulnerable to the same pitfalls. Consequently, the Graph Neural Networking
Challenge 2023 introduces a dataset of captured traffic traces that can be used
to build a ML-based network model without these limitations. In this paper we
propose a Graph Neural Network (GNN)-based solution specifically designed to
better capture the complexities of real network scenarios. This is done through
a novel encoding method to capture information from the sequence of captured
packets, and an improved message passing algorithm to better represent the
dependencies present in physical networks. We show that the proposed solution
it is able to learn and generalize to unseen captured network scenarios.
