Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis [electronic resource] / by Sujit Rokka Chhetri, Mohammad Abdullah Al Faruque.
By: Rokka Chhetri, Sujit [author.]
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Contributor(s): Al Faruque, Mohammad Abdullah [author.] | SpringerLink (Online service).
Material type: 
BookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XVI, 235 p. 111 illus., 106 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030379629.Subject(s): Electronic circuits | Cooperating objects (Computer systems) | Microprocessors | Computer architecture | Electronic Circuits and Systems | Cyber-Physical Systems | Processor ArchitecturesAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.3815 Online resources: Click here to access online
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Springer Nature eBookSummary: 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.
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