Newly engineered energy-based features for supervised anomaly detection in a physical model of a water supply system
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In several sensor-correlation-based studies, domain knowledge is encoded directly (e.g., power-flow relationships in smart grids, energy-conservation laws in manufacturing rigs) so that violations of physical consistency become a primary anomaly cue.
“In several cases, domain knowledge is encoded directly-such as power-flow relationships in smart grids or energy-conservation laws in manufacturing rigs-so that violations of physical consistency become a primary anomaly cue [99,101,107].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [99]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredOther sensor-correlation-based models make correlations explicit through graph structures, digital twins, energy-based physics features, or multi-instance representations.
“while others make correlations explicit through graph structures, digital twins, energy-based physics features, or multi-instance representations [94,96,99,101,104,106,107].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [99]
Correlation-based methods are particularly suitable for attacks and faults that selectively perturb a subset of variables while keeping other measurements within nominal ranges, and for scenarios where the key symptom is a collapse or distortion of inter-sensor correlation rather than a large deviation of a single signal.
“This makes them particularly suitable for attacks and faults that selectively perturb a subset of variables while keeping other measurements within nominal ranges, as well as for scenarios where the key symptom is a collapse or distortion of inter-sensor correlation rather than a large deviation of a single signal [94,96,99-101,106-108].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [99]
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- Authors
- Robles-Durazno, A., Moradpoor, N., McWhinnie, J., Russell, G., & Tan, Z.
- Affiliations
- Edinburgh Napier University (GB)
- Corresponding author
- Tan, Zhiyuan
- Venue
- Ad Hoc Networks
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 120
- Pages
- 102590
- ISSN
- 1570-8705 (ISSN-L)
- OpenAlex
- W3171827475
- Cited by
- 13
- References
- 52
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 4 · 2023: 1 · 2021: 1
- FWCI
- 0.67
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Ad Hoc Networks (publishedVersion)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion, cc-by-nc-nd)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- industrial control systems, anomaly detection, water supply systems, energy-based features, cyberattacks, physical modeling, voltage and current sensors, multilayer perceptron, feature engineering, cyber-physical security
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