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2025
Journal Article
Title
ResourceāEfficient Anomaly Detection in Industrial Control Systems with Quantized Recurrent Variational Autoencoder
Abstract
This work presents a novel solution for multivariate time series anomaly detection in industrial control systems (ICSs), specifically tailored for resourceāconstrained environments. At its core, the quantized gated recurrent unit variational autoencoder (QāGRUāVAE) architecture, a significant evolution from conventional methods, offers an extremely lightweight yet highly effective solution. By integrating gated recurrent units (GRUs) in place of long shortāterm memory (LSTM) cells within a variational autoencoder (VAE) framework, and employing channelāwise dynamic postātraining quantization (DPTQ), this model dramatically reduces hardware resource demands. The proposed solution exhibits performance on par with existing methods on the widely used secure water treatment (SWaT) and water distribution (WADI) benchmarks, while being tailored towards applications where computational resources are limited. This dual achievement of minimal resource consumption and preserved model efficacy paves the way for deploying advanced anomaly detection in resourceāconstrained environments, marking a significant leap forward in enhancing the resilience and efficiency of ICSs.
Author(s)
Open Access
Rights
CC BY 4.0: Creative Commons Attribution
Language
English