The early research report explores the possibility of using Graph Neural Networks (GNNs) for anomaly detection in internet traffic data enriched with information. While recent studies have made significant progress in using GNNs for anomaly detection in finance, multivariate time-series, and biochemistry domains, there is limited research in the context of network flow data. In this report, we explore the idea that leverages information-enriched features extracted from network flow packet data to improve the performance of GNN in anomaly detection. The idea is to utilize feature encoding (binary, numerical, and string) to capture the relationships between the network components, allowing the GNN to learn latent relationships and better identify anomalies.

翻译：本早期研究报告探讨了在图神经网络（GNNs）中利用信息增强的互联网流量数据进行异常检测的可能性。尽管近期研究在金融、多元时间序列和生物化学领域使用GNN进行异常检测已取得显著进展，但在网络流数据方面的研究仍较为有限。在本报告中，我们探讨了利用从网络流数据包中提取的信息增强特征来提升GNN在异常检测中性能的思路。其核心在于利用特征编码（二进制、数值型和字符串型）来捕获网络组件之间的关系，从而使GNN能够学习潜在关联并更有效地识别异常。