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.
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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
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Cited by
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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
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Yu, J., Dong, Y., & Qin, S. J. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3567382 · Cited by 9
A Case Study for Enhancing Maintenance of Cyber-Physical Systems With Digital Twin
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Self-citationA Secure Transmission and Attack Detection Approach Based on Autoencoder
Ma, Z.-L., & Li, X. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3558763 · Cited by 7
Chen, L., Zhang, X., & Xiao, F. (2025) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2025.3573309 · Cited by 7
A Data-Driven Encrypted Transmission and Security Monitoring Approach for Cyber-Physical Systems
Wu, S., Luo, H., Zhang, J., Tian, J., Jiang, Y., & Yin, S. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3575131 · Cited by 7
Self-citationA Fault Detection Approach for Nonlinear Systems Based on Deep Learning-Aided Kernel Representations
Wu, S., Zhu, Y., Luo, H., Wang, H., Zhang, J., Qiao, X., & Tian, J. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3563536 · Cited by 6
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Hierarchical Testing With Rabbit Optimization for Industrial Cyber-Physical Systems
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Optimal Stealthy Attack Against Remote State Estimation With Parameterized $\epsilon$-Stealthiness
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Multi-Terminal Secure Transmission Method for Due Diligence Data Based on the Present Block Cipher
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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
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