The DOI matches its Crossref record; 28 of the 29 references it cites were found in a registry or an index.
SIR-Aided Secure Transmission and Attack Detection for Security Management of Nonlinear Cyber-Physical System Using GRU Autoencoder
Abstract
This article designs a data-driven unsupervised defense scheme for nonlinear systems by proposing a machine learning approach called gate recurrent unit-based modified denoising and stable image representation-aided autoencoders. The proposed scheme decomposes original data into two subspaces through orthogonal projection. For secure transmission, information related to the system's dynamics, which is in the image space of the controlled system, is hidden through filtering, whereas only the dynamic-independent information is plaintext for transmission, which supplements the cryptographic encryption methods from a control perspective. Moreover, attack detection for nonstealthy and stealthy attacks is achieved simultaneously under the same framework. A case study is conducted for validation on the a hardware-in-the-loop platform with a mecanum-wheeled vehicle. The comparative experiments with well-known unsupervised data-driven methods show the high detection accuracy of the proposed defense scheme for nonstealthy and stealthy attacks and the excellent encryption capability.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Experimental results show substantially improved detection of stealthy attacks compared to single modality autoencoders.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [126]
In the citing paper
“Wuet al. [126] proposed GRU-MDSIR-AE, an unsupervised hybrid data fusion-based anomaly detection method applied to control and sensor data collected from a nonlinear CPS experimental platform.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [126]
In the citing paper
“By integrating a Stability Image Representation (SIR) with a Modified Disturbance Layer (MDL), the framework is designed to weaken the stealthiness of adversarial perturbations and sensitively capture subtle anomaly signal variations.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [126]
In the citing paper
“The model takes both control commands and sensor measurements as joint inputs and uses a GRU-based autoencoder to learn normal time-series patterns, employing reconstruction error as the anomaly score.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [126]
In the citing paper
“First, many approaches explicitly synchronize cyber-layer events with physical-layer behavior on a common time axis to detect cyber-physical inconsistencies that single-domain detectors would miss [122-129].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [126]
Citations
Cites
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051
Kraskov, A., Stögbauer, H., & Grassberger, P. (2004) · Physical Review E · doi:10.1103/physreve.69.066138 · Cited by 3,512
A secure control framework for resource-limited adversaries
Teixeira, A. M. H., Shames, I., Sandberg, H., & Johansson, K. (2012) · at - Automatisierungstechnik · doi:10.1016/j.automatica.2014.10.067 · Cited by 1,209
A survey on security control and attack detection for industrial cyber-physical systems
Ding, D., Han, Q., Xiang, Y., Ge, X., & Zhang, X. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.10.009 · Cited by 947
Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems
Ding, S. X. (2014) · Advances in industrial control · doi:10.1007/978-1-4471-6410-4 · Cited by 675
A systems and control perspective of CPS security
Dibaji, S. M., Pirani, M., Flamholz, D. B., Annaswamy, A. M., Johansson, K. H., & Chakrabortty, A. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.04.011 · Cited by 562
Covert Misappropriation of Networked Control Systems: Presenting a Feedback Structure
Smith, R. S. (2015) · IEEE Control Systems · doi:10.1109/mcs.2014.2364723 · Cited by 269
He, W., Luo, T., Tang, Y. S., Du, W., Tian, Y., & Qian, F. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2943548 · Cited by 227
A Condition Monitoring and Fault Isolation System for Wind Turbine Based on SCADA Data
Liu, X., Du, J., & Ye, Z. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3075239 · Cited by 138
Li, X., & Shen, X.-Y. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2960616 · Cited by 137
Cited by
Sorted by citations
Huang, X., Chang, C., Li, J., Xiao, S., & Su, Q. (2024) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3411618 · Cited by 12
Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15
Zhang, J., Tian, J., Luo, H., Wu, S., Yin, S., & Kaynak, O. (2024) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2024.3433492 · Cited by 11
Self-citationTang, S., Sun, Z., Hu, Y., Gong, X., Cheng, C., & Jin, L. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3582398 · Cited by 10
Luo, H., Wu, S., & Li, L. (2025) · Automatica · doi:10.1016/j.automatica.2025.112549 · Cited by 10
Self-citation
Metadata
- Authors
- Wu, S., Luo, H., Jiang, Y., Zhang, J., Tian, J., & Yin, S.
- Affiliations
- Harbin Institute of Technology (CN)
- Norwegian University of Science and Technology (NO)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2024-04
- Volume
- 20
- Issue
- 4
- Pages
- 5529-5538
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4389331434
- Cited by
- 26
- References
- 29
- Citations by year
- 2026: 5 · 2025: 17 · 2024: 4
- FWCI
- 2.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- National Natural Science Foundation of China (62073104, 62203143)
- China Postdoctoral Science Foundation (2022M710965)
- Fundamental Research Funds for the Central Universities (HIT.NSRIF202344)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- cyber-physical system security, attack detection, secure transmission, denoising autoencoder, nonlinear systems, orthogonal projection, stealthy attacks, hardware-in-the-loop
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
