Transformer-Driven Multicenter Manifold Modeling Deep SVDD for Anomaly Detection in IIoT Networks

Anomaly detection in Industrial Internet of Things (IIoT) systems is crucial for ensuring operational safety and product quality. However, challenges such as heterogeneous sensor data, dynamic network structures, and complex spatio-temporal dependencies hinder existing methods. To address these issues, we propose a novel Transformer-based anomaly detection framework designed for dynamic IIoT networks. Our framework integrates self-supervised graph contrastive learning with multi-center Support Vector Data Description (SVDD) for the first time. Specifically, we construct temporal graph snapshots from normal time windows. Graph Attention Networks (GAT) are used to extract structural features, while a lightweight Transformer with temporal window attention captures long-range dependencies among sensor sequences, producing robust spatio-temporal node representations. To improve the model’s generalization without requiring labeled anomalies, a self-supervised contrastive loss is introduced. The resulting node embeddings are clustered via k-Medoids, and an SVDD sub-model is trained per cluster to define multiple hyperspheres representing normal behavior. During inference, the proximity of test representations to these hyper-spheres enables accurate and interpretable anomaly detection. Extensive experiments on real-world IIoT datasets, including SWaT and WADI, show our method outperforms state-of-the-art baselines in AUC and F1 score, demonstrating its effectiveness and practical value for IIoT security applications.