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Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis/ by Sujit Rokka Chhetri, Mohammad Abdullah Al Faruque.
Author:	Rokka Chhetri, Sujit.
other author:	Al Faruque, Mohammad Abdullah.
Description:	XVI, 235 p. 111 illus., 106 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer engineering. -
Online resource:	https://doi.org/10.1007/978-3-030-37962-9
ISBN:	9783030379629

Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis
Rokka Chhetri, Sujit.

Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis[electronic resource] /by Sujit Rokka Chhetri, Mohammad Abdullah Al Faruque. - 1st ed. 2020. - XVI, 235 p. 111 illus., 106 illus. in color.online resource.

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.

ISBN: 9783030379629

Standard No.: 10.1007/978-3-030-37962-9doiSubjects--Topical Terms:

569006
Computer engineering.

LC Class. No.: TK7888.4

Dewey Class. No.: 621.3815

Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis
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