DADN: A Dynamic Anomaly Detection Network for Multivariate Time Series Data of the Industrial Internet of Things

Industrial Internet of Things (IIoT) faces significant security challenges such as data privacy and vulnerabilities. Unsupervised anomaly detection aims to identify abnormal patterns by monitoring multivariate time series data of IIoT without anomaly annotation. Previous deep-learning-based methods have high-computation cost, which hinders their deployment in edge devices. In this article, we propose a dynamic anomaly detection network (DADN), which introduces a dynamic anomaly detection mechanism to enable efficient inference. Specifically, a bilateral early-exit mechanism is designed so that each sample can dynamically exit at a certain layer during the forward process to support the anomaly judgement, and the layer where sample exits is adaptively determined at the inference stage. Experimental results show that DADN significantly reduces computational costs and enhances F1 scores in industrial anomaly-detection benchmarks, as shown by a 58.22% decrease in GFLOPS on the SWAT dataset, outperforming previous representative method (anomaly transformer).