Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis （2020）

Rokka Chhetri, Sujit/ Al Faruque, Mohammad Abdullah

Springer Nature Switzerland AG（2021/02発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 235 p.
• 言語 ENG
• 商品コード 9783030379643

Full Description

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.

Contents

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.