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Super learner ensemble for anomaly detection and cyber-risk quantification in industrial control systems
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Ahmadi-Assalemi, Gabriela, Al-Khateeb, Haider, Epiphaniou, Gregory and Aggoun, Amar (2022) Super learner ensemble for anomaly detection and cyber-risk quantification in industrial control systems. IEEE Internet of Things Journal . doi:10.1109/JIOT.2022.3144127 (In Press)
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WRAP-super-learner-ensemble-anomaly-detection-cyber-risk-quantification-Epiphaniou-2022.pdf - Accepted Version - Requires a PDF viewer. Download (2875Kb) | Preview |
Official URL: https://doi.org/10.1109/JIOT.2022.3144127
Abstract
Industrial Control Systems (ICS) 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 utilised by ICS aided by Artificial Intelligence (AI) enables data collection capabilities to facilitate automation, process streamlining and cost reduction. However, apart from operational use, the sensors generated data combined with AI can be innovatively utilised to model anomalous behaviour as part of layered security to increase resilience to cyber-attacks. We introduce a framework to profile anomalous behaviour 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 dataset. 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 seconds. 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.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | H Social Sciences > HD Industries. Land use. Labor Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Library of Congress Subject Headings (LCSH): | Control theory, Industries -- Computer networks -- Security measures, Computer security , Automatic control -- Security measures , Computer networks -- Security measures , Cooperating objects (Computer systems) -- Automatic control, Machine learning, Human-machine systems , Industry 4.0 , Internet of things -- Security measures, Smart cities | ||||||
| Journal or Publication Title: | IEEE Internet of Things Journal | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2327-4662 | ||||||
| Official Date: | 20 January 2022 | ||||||
| Dates: |
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| DOI: | 10.1109/JIOT.2022.3144127 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | In Press | ||||||
| Publisher Statement: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Restricted or Subscription Access |
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