Self-Supervised Learning of Graph Representations for Network Intrusion Detection

Lorenzo Guerra, Thomas Chapuis, Guillaume Duc, Pavlo Mozharovskyi, Van-Tam Nguyen

Keywords: Self-supervised learning, Graph neural networks, Masked autoencoders, Anomaly detection, Intrusion detection, Network security, Representation learning

TL;DR: We propose a self-supervised framework that combines GNNs and a Transformer-based masked autoencoder to detect network intrusions by reconstructing flow representations and flagging high-error patterns as anomalies.

Abstract: Detecting intrusions in network traffic is a challenging task, particularly under limited supervision and constantly evolving attack patterns. While recent works have leveraged graph neural networks for network intrusion detection, they often decouple representation learning from anomaly detection, limiting the utility of the embeddings for identifying attacks. We propose GraphIDS, a self-supervised intrusion detection model that unifies these two stages by learning local graph representations of normal communication patterns through a masked autoencoder. An inductive graph neural network embeds each flow with its local topological context to capture typical network behavior, while a Transformer‑based encoder-decoder reconstructs these embeddings, implicitly learning global co-occurrence patterns via self-attention without requiring explicit positional information. During inference, flows with unusually high reconstruction errors are flagged as potential intrusions. This end-to-end framework ensures that embeddings are directly optimized for the downstream task, facilitating the recognition of malicious traffic. On diverse NetFlow benchmarks, GraphIDS achieves up to 99.98% PR‑AUC and 99.61% macro F1-score, outperforming baselines by 5–25 percentage points.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 28895