ASIDS: Acoustic side-channel based intrusion detection system for industrial robotic arms
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Other-data-type-driven anomaly detection studies 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 dete
“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 [144]
Yang et al. [144] extracted time- and frequency-domain features to train a neural network model.
“Time- and frequency-domain features were then extracted to train a neural network model.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [144]
During detection, Yang et al. [144] compared the reported trajectory transmitted over the network (SCADA-side) with the trajectory reconstructed from acoustic signals using an EWMA-based statistical test; deviations exceeding a preset threshold were flagged as malicious manipulations.
“During detection, the reported trajectory transmitted over the network (SCADA-side) was compared with the trajectory reconstructed from acoustic signals using an EWMA-based statistical test; deviations exceeding a preset threshold were flagged as malicious manipulations.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [144]
Yang et al. [144] were motivated by the observation that the mechanical motion of each robot arm axis generates distinct and consistent acoustic signatures, and they synchronized trajectory data with microphone-captured acoustic signals and performed noise filtering, axis-wise source separation, and trace alignment.
“Motivated by the observation that the mechanical motion of each robot arm axis generates distinct and consistent acoustic signatures, the authors synchronized trajectory data with microphonecaptured acoustic signals and performed noise filtering, axis-wise source separation, and trace alignment.”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [144]
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.
“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 [144]
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Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15 · see citing works
Wu, T., Ou, Q., Luo, F., Yang, Y., & Zhou, D. (2026) · Measurement · doi:10.1016/j.measurement.2026.123025
- Authors
- Yang, K., Zhang, Y., Li, T., & Sun, L.
- Affiliations
- Guangxi University (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Li, Ting
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2025-10
- Volume
- 157
- Pages
- 104586
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W4412425260
- Cited by
- 4
- References
- 35
- Citations by year
- 2026: 3 · 2025: 1
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Computers & Security (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Funders
- Guangxi Natural Science Foundation (2024GXNSFBA010009)
- Guangxi Key Research and Development Program (AB23075109)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Keywords
- intrusion detection, industrial robot arm, data tampering attacks, trajectory reconstruction, anomaly detection
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