Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis
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Beschreibung
This book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS.
· Provides an introduction to the data-driven modeling of cyber-physical systems (CPS), to aid in capturing the stochastic phenomenon affecting CPS;
· Describes practical applications for securing the CPS as well as building the digital twin of the physical twin of CPS;
· Includes coverage of machine learning and artificial intelligence algorithms for data-driven modeling of the CPS;
- Provides novel algorithms for handling not just Euclidean data, but also non-Euclidean data.
This book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS.
Provides an introduction to the data-driven modeling of cyber-physical systems (CPS), to aid in capturing the stochastic phenomenon affecting CPS Describes practical applications for securing the CPS as well as building the digital twin of the physical twin of CPS Includes coverage of machine learning and artificial intelligence algorithms for data-driven modeling of the CPS Provides novel algorithms for handling not just Euclidean data, but also non-Euclidean data
Autor*in
Sujit Rokka Chhetri
Themen in »Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis«
Design Automation of Cyber-Physical Systems Security of Cyber-Physical Systems IoT-enabled Living Digital Twin Modeling stochastic phenomena affecting CPS machine learning for security in CPS