Anomaly detection based on data super-resolution in industrial cyber–physical systems with multirate sampling
Abstract
In industrial cyber-physical control systems (ICPSs), different types of variables are typically sampled at diverse intervals, ranging from sub-seconds to minutes or even longer, resulting in multi-rate sampling data. The variation in sampling rates can lead to data gaps, which impede the performance of traditional anomaly detection techniques. To address this challenge, a new anomaly detection approach is presented for ICPSs with multi-rate data. Employing a mechanism model, it performs data super-resolution in both temporal and spatial domains, thus filling in the missing data across various sampling rates and expanding additional virtual spatial states. Moreover, the approach integrates an anomaly-forced autoencoder. The autoencoder is trained to improve anomaly detection by injecting anomalies randomly during the training phase, thereby enhancing its capacity to accurately reconstruct normal behavior under anomalous conditions. Experimental studies are conducted on a petrochemical fractionation unit simulation testbed and also a pubic benchmark dataset BATADAL (Battle of the Attack Detection ALgorithms) to demonstrate the proposed approach. Experimental results from the testbed show that the proposed approach consistently maintains stable detection accuracy, ranging between 95.7% and 96.5%. On BATADAL dataset, the detection accuracy varies between 87.5% and 91.5% across different multi-rate sampling scenarios. This performance not only outperforms state-of-the-art baseline methods but also exhibits similar superior results when applied to other datasets.
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Du et al. [77] combined data super-resolution with anomaly detection in a multirate sampling environment using simulation data from a petrochemical fractionation process and the BATADAL dataset.
“Using simulation data from a petrochemical fractionation process and the BATADAL dataset, Du et al. [77] combined data super-resolution with anomaly detection in a multirate sampling environment.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [77]
Autoencoder-like models (AE, VAE, OC-NN) and PCA-based approaches can flag process states that lie outside the learned normal manifold, including zero-day attack scenarios that were not explicitly seen during training.
“By learning a compact representation of normal operating conditions and monitoring reconstruction error or boundary-based anomaly scores, autoencoder-like models (AE, VAE, OC-NN) and PCA-based approaches can flag process states that lie outside the learned normal manifold, including zero-day attack scenarios that were not explicitly seen during training [77,79,81-83,86,87].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [77]
Reconstruction-error-based methods are particularly effective for detecting sensor/actuator faults, false data injection, command manipulation, and generic injection-type attacks that cause local or global distortions in multivariate process trajectories.
“First, these methods are particularly effective for detecting sensor/actuator faults, false data injection, command manipulation, and generic injection-type attacks that cause local or global distortions in multivariate process trajectories [77,79,81-83,86,87].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [77]
This necessitates careful design of feature selection, mode handling, and adaptive thresholding, as well as periodic model updates, to avoid an excessive false-positive burden in real deployments.
“This necessitates careful design of feature selection, mode handling, and adaptive thresholding, as well as periodic model updates, to avoid an excessive false-positive burden in real deployments [77,79,83,86,87].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [77]
Some approaches rely almost exclusively on reconstruction error derived from AE or VAE models trained on normal process data, while others treat boundary distances in one-class neural networks or PCA-residual statistics as functionally equivalent 'reconstruction-like' anomaly measures.
“Some approaches rely almost exclusively on reconstruction error derived from AE or VAE models trained on normal process data [77,79,83], while others treat boundary distances in one-class neural networks or PCA-residual statistics as functionally equivalent 'reconstruction-like' anomaly measures [81,86].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [77]
The dataset distribution for reconstruction-error-based anomaly detection studies shows a strong reliance on a limited set of public process benchmarks (SWaT, HAI, BATADAL, and TE) supplemented by a small number of custom-built CAN fieldbus and wind-turbine SCADA datasets.
“Finally, the dataset distribution again reveals a strong reliance on a limited set of public process benchmarks-SWaT, HAI, BATADAL, and TE-supplemented by a small number of custom-built CAN fieldbus and wind-turbine SCADA datasets [77,79,81-83,85-87].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [77]
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- Authors
- Du, X., Zhou, C., Tian, Y.-C., & Wang, K.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Queensland University of Technology (AU)
- Venue
- IEEE Sensors Journal
- Published
- 2024-05-15
- Volume
- 24
- Issue
- 10
- Pages
- 16478-16490
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W4394585889
- Cited by
- 13
- References
- 44
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 2
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Sensors Journal (publishedVersion)
- Funders
- National Natural Science Foundation of China (62127808, 62320106005)
- Australian Research Council (ARC) through the Discovery Project Scheme (DP220100580)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- anomaly detection, industrial cyber-physical systems, multirate sampling, missing data imputation, mechanism model, attack detection
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