Multivariate Time-Series Anomaly Detection in IoT Using Attention-Based Gated Recurrent Unit

Abstract

The explosively-growing Internet of Things (IoT) is generating massive amount of multivariate time series data. Anomaly detection for multivariate time series is of great importance in detecting and locating system failures, device malfunctions and malicious attacks in IoT. In this paper, we propose a new anomaly detection model, called Graph Attention-based Gated Recurrent Unit (GAGRU), to exploit the complex spatiotemporal features in multivariate time series. The proposed GAGRU is basically an attention-based Gated Recurrent Unit (GRU), which embeds the graph attention mechanism into the GRU to integrate the spatial features while exploiting the temporal dependency of data using GRU. Moreover, the GAGRU model is also designed to automatically learn the graph structure and score anomalies under their own TopK criterion. Experiments on two real-world datasets demonstrate the performance improvement of the proposed GAGRU model.