A novel anomaly detection method for digital twin data using deconvolution operation with attention mechanism
Abstract
In recent years, industrial control systems have evolved toward stability and efficiency, increasing industrial control systems interconnected with the Internet, which means that industrial control systems are facing more serious cyber threats. Thus, it is critical for enterprises to consider issues related to data privacy and network security. Digital twin enables real-time synchronization and simulation of data from various physical components of industrial control systems. However, anomaly detection of twin data is still challenging because existing methods are usually multi-stage with tedious training and detection steps. Therefore, we propose a method called end-to-end anomaly detection with the aim to accomplish real-time anomaly detection quickly and accurately. In order to seek key features, multidimensional deconvolutional network and attention mechanism are applied to our model. The results of this study indicate that our method performs well on precision and F1 score in comparison to the state-of-art methods.
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Some sensor-correlation-based models learn temporal correlations and cross-sensor dependencies implicitly using deep RNNs, LSTMs, attention mechanisms, or Transformers applied to multivariate sequences.
“Some models learn temporal correlations and cross-sensor dependencies implicitly using deep RNNs, LSTMs, attention mechanisms, or Transformers applied to multivariate sequences [88,90,93,98,108,110], while others make correlations explicit through graph structures, digital twins, energy-based physics features, or multi-instance representations [94,96,99,101,104,106,107].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [110]
Unsupervised methods such as clustering (e.g., k-means, Siamese autoencoders) are particularly suitable for anomaly detection when labelled attack data is scarce.
“In anomaly detection contexts, unsupervised methods such as clustering (e.g., k-means, Siamese autoencoders) are particularly suitable when labelled attack data is scarce [30, 31].”
From Towards enhanced cybersecurity in industrial control systems: a systematic review of context-based modeling, digital twins, and machine learning approaches (2025) · cites this paper as [31]
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- Authors
- Li, Z., Duan, M., Xiao, B., & Yang, S.
- Affiliations
- Hunan University (CN)
- Hong Kong Polytechnic University (HK)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2023-05
- Volume
- 19
- Issue
- 5
- Pages
- 7278-7286
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4312878571
- Cited by
- 45
- References
- 29
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 9 · 2023: 5
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- National Key R&D Program of China (2020AAA0107100)
- National Natural Science Foundation of China (62272149, 61876191)
- Science and Technology Innovation Program of Hunan Province (2022RC1038)
- Shenzhen Science and Technology Program (JCYJ20210324141208020)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- anomaly detection, digital twin, industrial control systems, deconvolutional networks, attention mechanism, end-to-end learning, real-time detection, cyber threats, network security, data privacy
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