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（GNN）的网络入侵检测系统（NIDS）新方法。第一种方法STEG（Scattering Transform with E-GraphSAGE）利用散射变换对边特征向量进行多分辨率分析，可提供对网络流量中细微异常识别至关重要的精细表征。第二种方法通过采用Node2Vec初始化节点表示（区别于使用统一值的常规方法），从而捕获更准确全面的网络拓扑信息。实验表明，在基准NIDS数据集上，我们的方法相较于现有最优方法显示出显著性能提升。