Multivariate Time-Series Anomaly Detection in IoT with a Bi-Dual GM GRU Autoencoder

Effective anomaly detection is vital to minimize the economic impacts of security issues that Internet of Things (IoT) and Industrial Internet of Things (IIoT) face more frequently in recent years. Conventional anomaly detection methods have difficulties in finding new and complex anomalies. machine learning (ML) algorithms have proven to be excellent in anomaly detection with the development of ML research. However, labeled data is hard to get in real situations, so we need unsupervised learning methods. We suggest a new unsupervised learning method for detecting anomalies in multivariate time series, named Bi-Dual-GM GRU-AE. It has two Gate Mechanisms to better pick the combination part from different aspects of input features. These features are derived from different hidden sizes of GRU encoders that take both forward and backward time windows as inputs. We evaluated the proposed method on real-world IoT and IIoT datasets and showed that it outperforms the latest methods in the multivariate time series anomaly detection task. The proposed approach shows superior detection performance particularly in scenario where anomalies are relatively rare, offering a better detection solution to the modern challenges in IoT and IIoT security.