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.

通过使用一种新颖的编码方法来捕捉抓取数据包序列中的信息，并使用改进的消息传递算法更好地表示物理网络中的依赖关系，我们提出了一种基于图神经网络的解决方案，旨在更好地捕捉真实网络场景的复杂性。我们展示了该方案能够学习并推广到未见过的抓取网络场景。