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Digital Twin-based ...
Digital Twin-based Intrusion Detection for Industrial Control Systems
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- Varghese, Seba (author)
- RISE,KTH,Programvaruteknik och datorsystem, SCS,RISE Research Institutes of Sweden.,KTH Royal Institute of Technology, Sweden
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- Dehlaghi Ghadim, Alireza (author)
- Mälardalens universitet,RISE,Industriella system,Inbyggda system,RISE Research Institute of Sweden, Västerås, Sweden
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- Balador, Ali (author)
- Mälardalens universitet,RISE,Inbyggda system,RISE Research Institutes of Sweden.
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- Alimadadi, Zahra (author)
- KTH,Programvaruteknik och datorsystem, SCS,KTH Royal Institute of Technology, Sweden
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- Papadimitratos, Panagiotis (author)
- KTH,Programvaruteknik och datorsystem, SCS
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- Papadimitratos, Panos (author)
- KTH Royal Institute of Technology, Sweden
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- English.
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In: 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665416474 ; , s. 611-617
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Abstract
Subject headings
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- Digital twins have recently gained significant interest in simulation, optimization, and predictive maintenance of Industrial Control Systems (ICS). Recent studies discuss the possibility of using digital twins for intrusion detection in industrial systems. Accordingly, this study contributes to a digital twin-based security framework for industrial control systems, extending its capabilities for simulation of attacks and defense mechanisms. Four types of process-aware attack scenarios are implemented on a standalone open-source digital twin of an industrial filling plant: command injection, network Denial of Service (DoS), calculated measurement modification, and naive measurement modification. A stacked ensemble classifier is proposed as the real-time intrusion detection, based on the offline evaluation of eight supervised machine learning algorithms. The designed stacked model outperforms previous methods in terms of F1Score and accuracy, by combining the predictions of various algorithms, while it can detect and classify intrusions in near real-time (0.1 seconds). This study also discusses the practicality and benefits of the proposed digital twin-based security framework
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Annan medicin och hälsovetenskap -- Gerontologi, medicinsk/hälsovetenskaplig inriktning (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Other Medical and Health Sciences -- Gerontology, specialising in Medical and Health Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- Digital Twin
- Industrial Control Systems
- Intrusion Detection Systems
- Machine Learning
- Stacked Ensemble Model
- Denial-of-service attack
- E-learning
- Intrusion detection
- Supervised learning
- Ensemble models
- Industrial systems
- Intrusion-Detection
- Machine-learning
- Predictive maintenance
- Security frameworks
- Simulation optimization
- Learning algorithms
- Gerontology
- specialising in Medical and Health Sciences
- Gerontologi
- medicinsk/hälsovetenskaplig inriktning
Publication and Content Type
- ref (subject category)
- kon (subject category)
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