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

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. From the Back 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. About the Author Sujit Rokka Chhetri is a recent graduate from the University of California, Irvine. He finished his Ph.D. in Computer Engineering from the Henry Samueli School of Engineering, where he was working as a graduate student researcher at Advanced Cyber-Physical Systems lab under the supervision of Prof. Mohammad Abdullah Al Faruque. He is currently a Staff Data Scientist at Palo Alto Networks. His research interest lies in data-driven modeling of cyber-physical systems. More specifically, his research focus is on analyzing the various source of analog emissions for potential side-channels. He is also interested in non-Euclidean data-driven modeling techniques including graph convolutional neural networks and knowledge graph embedding algorithms. Furthermore, his research also focusses on data-driven modeling techniques for building a digital twin of the cyber-physical systems. He has several publications in the top conferences and also holds one US patent. He received NDSS distinguished Poster award in 2016 as well. Mohammad Al Faruque is currently with the University of California Irvine (UCI), where he is an associate professor (with tenure) and directing the Cyber-Physical Systems Lab. Prof. Al Faruque is the recipient of the Henry Samueli School of Engineering Mid-Career Faculty Award for Research 2019, the IEEE Technical Committee on Cyber-Physical Systems Early-Career Award 2018 and the IEEE CEDA Ernest S. Kuh Early Career Award 2016. He is also the recipient of the UCI Academic Senate Distinguished Early Career Faculty Award for Research 2017 and the School of Engineering Early-Career Faculty Award for Research 2017. He served as an Emulex Career Development