Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis
Autor Sujit Rokka Chhetri, Mohammad Abdullah Al Faruqueen Limba Engleză Hardback – 9 feb 2020
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Specificații
ISBN-13: 9783030379612
ISBN-10: 3030379612
Pagini: 235
Ilustrații: XVI, 235 p. 111 illus., 106 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.53 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030379612
Pagini: 235
Ilustrații: XVI, 235 p. 111 illus., 106 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.53 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
1. Introduction.- 2. Data-Driven Attack Modeling using Acoustic Side-Channel.-3. Aiding Data-Driven Attack Model with a Compiler Modification.-4. Data-Driven Defense through Leakage Minimization.-5. Data-Driven Kinetic-Cyber Attack Detection.-6. Data-Driven Security Analysis using Generative Adversarial Networks.-7. Dynamic Data-Driven Digital Twin Modeling.-8. IoT-enabled Living Digital Twin Modeling.-9. Non-Euclidean Data-Driven Modeling using Graph Covolutional.-10. Dynamic Graph Graph Embedding.
Notă biografică
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 Chair from October 2012 till July 2015. Before, he was with Siemens Corporate Research and Technology in Princeton, NJ. His current research is focused on the system-level design of Internet-of-Things (IoT), Embedded Systems, and Cyber-Physical-Systems (CPS) with special interests on design automation methodologies, data-driven modeling techniques including machine learning for design, CPS security, etc. His work involves novel hardware and software design for various CPS application areas, including Industry 4.0 (manufacturing), smart-grid, and autonomous vehicles. Prof. Al Faruque received his B.Sc. degree in Computer Science and Engineering (CSE) from Bangladesh University of Engineering and Technology (BUET) in 2002, and M.Sc. and Ph.D. degrees in Computer Science from Aachen Technical University and Karlsruhe Institute of Technology, Germany in 2004 and 2009, respectively.Prof. Al Faruque received the Thomas Alva Edison Patent Award 2016 from the Edison foundation, the 2016 DATE Best Paper Award, the 2015 DAC Best Paper Award, the 2009 IEEE/ACM William J. McCalla ICCAD Best Paper Award, the 2016 NDSS Distinguished Poster Award, the 2008 HiPEAC Paper Award, the 2015 Hellman Fellow Award, the 2015 Kane Kim Fellowship Award, the 2017 ICCAD Best Paper Award Nomination, the 2017DAC Best Paper Award Nomination, the 2012 DATE Best IP Award Nomination, the 2005 DAC Best Paper Award Nomination, the EECS Professor of the year 2015-16Award, and the 2015 UCI Chancellor’s Award for Excellence in Fostering Undergraduate Research. Besides 100+ IEEE/ACM publications in the premier journals and conferences, Prof. Al Faruque holds 8 US patents.Prof. Al Faruque is currently serving as the associate editor of the ACM Transactions on Design Automation on Electronics and Systems and IEEE Design & Test. He is an IEEE senior member and an ACM senior member.
Textul de pe ultima copertă
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 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.
Caracteristici
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