Super learner ensemble for anomaly detection and cyber-risk quantification in industrial control systems
Open accessAbstract
Industrial control systems (ICSs) are integral parts of smart cities and critical to modern societies. Despite indisputable opportunities introduced by disruptor technologies, they proliferate the cybersecurity threat landscape, which is increasingly more hostile. The quantum of sensors utilized by ICS aided by artificial intelligence (AI) enables data collection capabilities to facilitate automation, process streamlining, and cost reduction. However, apart from the operational use, the sensors generated data combined with AI can be innovatively utilized to model anomalous behavior as part of layered security to increase resilience to cyberattacks. We introduce a framework to profile anomalous behavior in ICS and derive a cyber-risk score. A novel super learner ensemble for one-class classification is developed, using overlapping rolling windows with stratified,$k$-fold,$n$-repeat cross-validation applied to each base learner followed by majority voting to derive the best learner. Our approach is demonstrated on a liquid distribution sensor data set. The experimental results reveal that the proposed technique achieves an overall$F1$-score of 99.13%, an anomalous recall score of 99% detecting anomalies lasting only 17 s. The key strength of the framework is the low computational complexity and error rate. The framework is modular, generic, applicable to other ICS, and transferable to other smart city sectors.
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The dataset distribution for sensor-correlation-based methods shows heavy evaluation on SWaT, WADI, GHL, HAI, TE, and power-system benchmarks (IEEE bus systems), with additional evidence from gas-pipeline, GRFICSv2/Factory I/O, and aNormalies-based testbeds.
“Finally, the dataset distribution indicates that sensor-correlation-based methods are heavily evaluated on SWaT, WADI, GHL, HAI, TE, and power-system benchmarks (IEEE bus systems), with additional evidence from gas-pipeline, GRFICSv2/Factory I/O, and aNormalies-based testbeds [88-96,98-102,104-110].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [95]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredEnsemble and weakly supervised frameworks show that combining multiple correlation views (statistical, temporal, structural) can improve robustness under label scarcity and mixed cyber/physical disturbance conditions.
“Ensemble and weakly supervised frameworks further show that combining multiple correlation views (statistical, temporal, structural) can improve robustness under label scarcity and mixed cyber/physical disturbance conditions [95,96].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [95]
The computational-complexity discussion (p. 15) says combining learners improved anomaly-detection resilience, and the conclusion (p. 16) describes a hybrid unsupervised model detecting short anomalies. The paper does not show that combining statistical, temporal, and structural correlation views improves robustness under label scarcity or mixed cyber/physical disturbances. Its use of Spearman correlation (p. 11) is for removing highly correlated features, not combining correlation views. (LLM, conf=0.87)
Experimental — accuracy not yet measuredBy focusing on joint behavior across multiple sensors and control channels, correlation-based detectors can better separate cyber-physical attacks (which often produce inconsistent or coordinated manipulation patterns) from natural faults (which follow typical failure correlations or fault signatures).
“By focusing on joint behavior across multiple sensors and control channels, correlation-based detectors can better separate cyber-physical attacks, which often produce inconsistent or coordinated manipulation patterns, from natural faults that follow typical failure correlations or fault signatures [91,95,96,100,101].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [95]
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 measured
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Galadima, I. B., Ithnin, N., Wahab, N. H. A., Osman, M. Z., & Gabi, D. (2025) · Research Square · doi:10.21203/rs.3.rs-7396895/v1 · Cited by 1 · see citing works
Fuzzy Harmony Search Technique for Cyber Risks in Industry 4.0 Wireless Communication Networks
Diao, Z., & Sun, F. (2023) · Processes · doi:10.3390/pr11030951
Ahmadi‐Assalemi, G., Al‐Khateeb, H., Makonese, T. L., Benson, V., Khan, S., & Butt, U. (2024) · International Journal of Electronic Security and Digital Forensics · doi:10.1504/ijesdf.2025.143470
Self-citationMachine Learning for Industrial Iot Cybersecurity: A Systematic ReviewPreprint
Dohin, A., Zkik, K., Akilal, A., Omar, M., & Akli, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5388573
Cybersecurity for Industrial Control Systems Using AI
Mahanta, K., Rath, P., Maringanti, H. B., & Parija, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-95-1320-8_5
Attack Classification Using Retrieval-Augmented Generation and Large Language Models
Kawaminami, I., Rahman, M. W. U., Bai, J., & Hariri, S. (2025) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa66935.2025.11315325
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Security and Forensic Issues in Internet of Everything Networks: A Review
Judijanto, L., & Nurhayati, S. (2025) · International Conference on Computer and Applications (ICCA) · doi:10.1109/icca66035.2025.11430732
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Risk-Perception Digital Management System for Nuclear Power Engineering
Liu, Q., Yang, Y., Zhao, Y., Yang, C., Liu, X., & Zhang, S. (2026) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba68339.2026.11651844
- Authors
- Ahmadi-Assalemi, G., Al-Khateeb, H., Epiphaniou, G., & Aggoun, A.
- Affiliations
- School of Engineering, Computing and Mathematical Sciences, University of Wolverhampton, Wolverhampton, U.K.
- University of Warwick (GB)
- University of Wolverhampton (GB)
- Venue
- IEEE Internet of Things Journal
- Published
- 2022-08-01
- Volume
- 9
- Issue
- 15
- Pages
- 13279-13297
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4206015085
- Cited by
- 34
- References
- 72
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 10 · 2023: 3 · 2022: 1
- FWCI
- 2.22
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Wolverhampton Intellectual Repository and E-Theses (University of Wolverhampton) (acceptedVersion, cc-by-nc-nd)
- Aston Publications Explorer (Aston University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- 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
- Keywords
- industrial control systems, anomaly detection, cyber risk quantification, one-class classification, majority voting, stratified k-fold cross-validation, ICS cybersecurity, smart cities, critical infrastructure resilience
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