IoT Time-series Anomaly Detection Using a Hybrid Transformer-GRU Fusion Model

Abstract

The rapid growth of Internet of Things (IoT) systems has generated massive, complex, and highly dynamic time-series data, making anomaly detection essential for system security and operational stability. However, traditional methods based on pointwise representations, statistical thresholds, or simple pairwise associations often struggle to capture complex temporal dependencies and feature interactions in IoT data. To address these challenges, we propose Residual GRU-Attention Anomaly Detector (RGAAD), an unsupervised framework for IoT time-series anomaly detection. RGAAD integrates residual GRU modeling, adaptive self-attention, and gated multi-scale feature fusion to jointly capture temporal dependencies and feature correlations. Extensive experiments on SMD, SWaT, and MSL demonstrate that RGAAD achieves highly competitive performance and consistently outperforms strong baseline methods. These results confirm that explicitly modeling pointwise anomalies and temporal relationships is effective for real-world IoT monitoring.