In this paper, we present two novel methods in Network Intrusion Detection Systems (NIDS) using Graph Neural Networks (GNNs). The first approach, Scattering Transform with E-GraphSAGE (STEG), utilizes the scattering transform to conduct multi-resolution analysis of edge feature vectors. This provides a detailed representation that is essential for identifying subtle anomalies in network traffic. The second approach improves node representation by initiating with Node2Vec, diverging from standard methods of using uniform values, thereby capturing a more accurate and holistic network picture. Our methods have shown significant improvements in performance compared to existing state-of-the-art methods in benchmark NIDS datasets.

翻译：本文提出了两种基于图神经网络的网络入侵检测系统新方法。第一种方法"散射变换与E-GraphSAGE"（STEG）利用散射变换对边特征向量进行多分辨率分析，从而获得对网络流量中细微异常识别至关重要的精细表征。第二种方法通过引入Node2Vec初始化节点表示，突破了传统均匀值初始化方法的局限，能够捕捉更准确、更全面的网络拓扑特征。在基准网络入侵检测数据集上的实验表明，与现有最先进方法相比，我们的方法在性能上取得了显著提升。