Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis Hardback - 2020
by Rokka Chhetri, Sujit (Author)/ Al Faruque, Mohammad Abdullah (Author)
- New
- Hardback
Standard delivery: 7 to 14 days
Details
- Title Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis
- Author Rokka Chhetri, Sujit (Author)/ Al Faruque, Mohammad Abdullah (Author)
- Binding Hardback
- Condition New
- Pages 235
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2020
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # x-3030379612
- ISBN 9783030379612 / 3030379612
- Weight 1.17 lbs (0.53 kg)
- Dimensions 9.21 x 6.14 x 0.63 in (23.39 x 15.60 x 1.60 cm)
- Category Technology & Industrial Arts
- Quantity available 2
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From the publisher
From the rear cover
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.