Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection
This work addresses the challenge of automated monitoring in sensor-rich systems by improving anomaly detection accuracy, though it is incremental as it builds on existing graph-based methods.
The paper tackles the problem of multi-variate time series anomaly detection in cyber-physical systems by introducing the Edge Conditional Node-update Graph Neural Network (ECNU-GNN), which dynamically transforms source node representations based on edges to improve target node updates, resulting in performance gains of 5.4%, 12.4%, and 6.0% higher F1 scores on SWaT, WADI, and PSM datasets compared to baselines.
With the rapid advancement in cyber-physical systems, the increasing number of sensors has significantly complicated manual monitoring of system states. Consequently, graph-based time-series anomaly detection methods have gained attention due to their ability to explicitly represent relationships between sensors. However, these methods often apply a uniform source node representation across all connected target nodes, even when updating different target node representations. Moreover, the graph attention mechanism, commonly used to infer unknown graph structures, could constrain the diversity of source node representations. In this paper, we introduce the Edge Conditional Node-update Graph Neural Network (ECNU-GNN). Our model, equipped with an edge conditional node update module, dynamically transforms source node representations based on connected edges to represent target nodes aptly. We validate performance on three real-world datasets: SWaT, WADI, and PSM. Our model demonstrates 5.4%, 12.4%, and 6.0% higher performance, respectively, compared to best F1 baseline models.