Multiview Spatial–Temporal Interaction Attention- Based Multivariate Time Series Anomaly Detection for Distributed Industrial Control Networks

Artificial Intelligence-empowered Industrial Control Networks coordinate massive heterogeneous devices and contain multi-node spatial-temporal information. Multivariate Time Series Anomaly Detection (MTS-AD) can discover data-fault behaviors for ensuring the security of distributed networks. However, existing studies tend to rely heavily on single temporal features or neglect the rich spatial-temporal correlations, which leads to the serious underutilization of interactive embeddings between the time and space domains. In this article, a novel Multiview Spatial-Temporal Interaction Attention Network (MSTIA-Net) scheme is proposed for the unsupervised MTS-AD task to better tackle these challenges. MSTIA-Net focuses on jointly modeling the comprehensive spatial-temporal dependencies by means of incorporating complex interactive contents and dynamic relations from multiview patterns. To fully leverage the content-oriented interactions, a spatial-temporal interactions aggregation module is presented to explicitly learn content-aware representations with a parallel-attention mechanism and a low-rank bilinear fusion manner. Simultaneously, considering the potential correlations among different variables as contextual cues, a spatial-temporal correlations learning module is developed to adaptively capture the relevant context for relation-aware representations. On this basis, both types of aware clues are further integrated by the dual attention-enhanced contrastive reconstruction, which can enrich the cross-aware fusion representations and generate the local and global outputs through a cross-view contrastive learning strategy. Experiments conducted on six benchmark datasets demonstrate the superiority of our MSTIA-Net over state-of-the-art baselines.