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A physical signal-based anomaly detection for industrial terminal
Open accessAbstract
With the development of industry, industrial control systems are widely used in critical infrastructures. As one of the most significant components, the industrial terminals include Programmable Logic Controller, Distributed Control Systems, Remote Terminal Unit, and etc. Attacks against industrial terminals can cause catastrophic damage since they are directly related to business in the field. Unfortunately, traditional protection methods, such as intrusion detection systems and antivirus software, are not applicable to industrial terminals which are resource-constrained. In this paper, we propose an anomaly detection method for industrial controllers based on physical signals. The basic idea is to detect abnormal execution in a industrial terminal through analyzing its physical signals, including system power consumption and CPU usage. To effectively analyze the physical signals, a Transformer-based neural network is trained on the time series dataset to predict the next normal physical signals. The abnormal execution is detected by comparing the predicted signals and actual ones. We evaluate the proposed method on a real world dataset collected from a smart grid terminal, and the results show that the detection accuracy is over 97%.
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“First, they deliberately move beyond conventional network and process data to monitor artifacts that are closer to the root cause of cyber-physical compromise-such as PLC control logic structure, acoustic side-channel signals, host-level physical resource usage, and power-consumption traces-thereby covering attack surfaces that packet- or sensor-centric detectors inherently miss [142-146].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [145]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This makes it possible to detect semantically stealthy or encrypted attacks by flagging violations of these auxiliary consistency relations, even when primary cyber channels are partially hidden or manipulated [142,144-146].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [145]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Third, most approaches are designed as lightweight, mostly unsupervised endpoint monitors that can operate on resource-constrained devices or external side channels without requiring deep modifications to existing control networks, suggesting a practical way to complement network- and process-based IDS in real deployments [142,145,146].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [145]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“During runtime, if the L2 error between predicted and observed signals exceeded a predefined threshold, the terminal was classified as executing anomaly code, experiencing a crash, or operating in an anomalous state.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [145]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Recognizing that resource-constrained industrial devices (PLC, DCS, RTU) cannot host traditional IDS or antivirus solutions, the authors trained a Transformer-based time-series predictor solely on normal operational data to forecast future physical signals.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [145]
Experimental — accuracy not yet measured
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In the citing paper
“In a smart-grid industrial terminal environment, Mei et al. [145] collected fivedimensional physical signals-power consumption, CPU idle time, CPU occupancy rate, interrupt counts, and system call counts-at one-second intervals and proposed a physicalsignal-based anomaly detection approach.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [145]
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Citations
Cites
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Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series ForecastingPreprint
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Lightweight Classification of IoT Malware Based on Image Recognition
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Industrial Control System Network Intrusion Detection by Telemetry Analysis
Ponomarev, S., & Atkison, T. (2015) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2015.2443793 · Cited by 147
Nazari, A., Sehatbakhsh, N., Alam, M., Zajic, A. G., & Prvulović, M. (2017) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3079856.3080223 · Cited by 111
Watch Me, but Don't Touch Me! Contactless Control Flow Monitoring via Electromagnetic Emanations
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On Code Execution Tracking via Power Side-Channel
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Cited by
Sorted by citations
Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15
Yu, Z. H., Pei, J., & Zhu, M. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3621208
Metadata
- Authors
- Mei, W., Liu, W., Chen, J., & Li, K.
- Affiliations
- State Grid Corporation of China (China) (CN)
- NARI Group (China) (CN)
- State Grid Shandong Electric Power Company (China) (CN)
- Venue
- Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
- Publisher
- ACM
- Published
- 2023-10-20
- Pages
- 651-658
- OpenAlex
- W4394898112
- Cited by
- 2
- References
- 13
- Citations by year
- 2025: 2
- FWCI
- 0.60
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Electronic Information Technology and Computer Engineering (EITCE) (publishedVersion)
- Funders
- State Grid Corporation of China (Grand No. 5700-202358294A-1-1-ZN)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- anomaly detection, physical signals, power consumption, CPU utilization, transformer models, time series forecasting, smart grid, industrial control systems, resource-constrained devices, cyberattack detection
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- CoChat. (2026). Citation check: A physical signal-based anomaly detection for industrial terminal. https://app.cochat.ai/papers/10.1145/3650400.3650508
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- Mei, W., Liu, W., Chen, J., & Li, K. (2023). A physical signal-based anomaly detection for industrial terminal. Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering. https://doi.org/10.1145/3650400.3650508
