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  4. Resource‐Efficient Anomaly Detection in Industrial Control Systems with Quantized Recurrent Variational Autoencoder
 
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2025
Journal Article
Title

Resource‐Efficient 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 resource‐constrained environments. At its core, the quantized gated recurrent unit variational autoencoder (Q‐GRU‐VAE) 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 short‐term memory (LSTM) cells within a variational autoencoder (VAE) framework, and employing channel‐wise dynamic post‐training 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 resource‐constrained environments, marking a significant leap forward in enhancing the resilience and efficiency of ICSs.
Author(s)
FƤhrmann, Daniel  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Ihlefeld, Malte
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kuijper, Arjan  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Journal
IET Collaborative Intelligent Manufacturing  
Project(s)
Next Generation Biometric Systems  
Next Generation Biometric Systems  
Funder
Bundesministerium für Bildung und Forschung -BMBF-  
Hessisches Ministerium für Wissenschaft und Kunst -HMWK-  
Open Access
File(s)
IET Collab Intel Manufact - 2025 - FƤhrmann - Resource‐Efficient Anomaly Detection in Industrial Control Systems With.pdf (1.29 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.1049/cim2.70032
10.24406/publica-4575
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Branche: Information Technology

  • Research Line: Machine learning (ML)

  • LTA: Monitoring and control of processes and systems

  • LTA: Machine intelligence, algorithms, and data structures (incl. semantics)

  • Industrial automation

  • Machine learning

  • Industrie 4.0

  • ATHENE

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