GRU-Based Interpretable Multivariate Time Series Anomaly Detection in Industrial Control System

Abstract

Interpretable multivariate time series anomaly detection is an important technology to prevent accidents and ensure the reliable operation of Industrial Control Systems. A key limitation lies in the lack of a model to achieve better detection performance and more reliable interpretability, and keep a balance between performance efficiency and training optimization. In this paper, we propose GRN, an Interpretable Multivariate Time Series Anomaly Detection method based on neural graph networks and gated recurrent units (GRU). GRN can automatically learn potential correlations between sensors from multidimensional industrial control time series data, quickly mine long-term and short-term dependencies, to improve detection performance and help users to infer the root cause of detected anomalies. Based on GRU, GRN preserves the original advantages of processing the sequences and capturing the time series dependencies, moreover solves the problem of gradient disappearance and gradient explosion. We compare the performance of nine state-of-the-art algorithms on two real water treatment datasets (SWaT, WADI). GRN achieves better detection precision and recall. Meanwhile, the comparison of Area Under the Curve (AUC) demonstrates that GRN has the effect of maintaining balance between detection performance and training optimization. Compared with a Graph Deviation Network(GDN), GRN has achieved greater interpretability.