Wow! eBook
AI-Enabled Threat Detection and Security Analysis for Industrial IoT 1st ed. 2021 Edition
Author(s): Hadis Karimipour (Editor), Farnaz Derakhshan
- Publisher: Springer
- Publication Date: 6 Aug. 2022
- Edition: 1st ed. 2021
- Language: English
- Print length: 258 pages
- ISBN-10: 3030766152
- ISBN-13: 9783030766153
Book Description
Each chapter corresponds to an important snapshot including an overview of the opportunities and challenges of realizing the AI in IIoT environments, issues related to data security, privacy and application of blockchain technology in the IIoT environment. This book also examines more advanced and specific topics in AI-based solutions developed for efficient anomaly detection in IIoT environments. Different AI/ML techniquesincluding deep representation learning, Snapshot Ensemble Deep Neural Network (SEDNN), federated learning and multi-stage learning are discussed and analysed as well. Researchers and professionals working in computer security with an emphasis on the scientific foundations and engineering techniques for securing IIoT systems and their underlying computing and communicating systems will find this book useful as a reference. The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, cyber security, and information systems. It also applies to advanced-level students studying electrical engineering and system engineering, who would benefit from the case studies.
Editorial Reviews
From the Back Cover
Each chapter corresponds to an important snapshot including an overview of the opportunities and challenges of realizing the AI in IIoT environments, issues related to data security, privacy and application of blockchain technology in the IIoT environment. This book also examines more advanced and specific topics in AI-based solutions developed for efficient anomaly detection in IIoT environments. Different AI/ML tecniques including deep representation learning, Snapshot Ensemble Deep Neural Network (SEDNN), federated learning and multi-stage learning are discussed and analysed as well. Researchers and professionals working in computer security with an emphasis on the scientific foundations and engineering techniques for securing IIoT systems and their underlying computing and communicating systems will find this book useful as a reference. The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, cyber security, and information systems. It also applies to advanced-level students studying electrical engineering and system engineering, who would benefit from the case studies.
About the Author
Hadis Karimipour is the director of Smart Grid Lab in the School of Engineering, University of Guelph, Ontario, Canada. She received a Ph.D. degree in Energy System from the Department of Electrical and Computer Engineering in the University of Alberta in Feb. 2016. Before joining the University of Guelph, she was a postdoctoral fellow in University of Calgary working on cybersecurity of the smart power grids. She is currently an Assistant Professor at the School of Engineering, Engineering Systems and Computing Group, at the University of Guelph, Ontario, Canada. Her research interests include large-scale power system state estimation, cyber-physical modeling, cyber-security of the smart grids, and parallel and distributed computing. She is a member of IEEE and IEEE Computer Society. She serves as the Chair of the IEEE Women in Engineering (WIE) and Chapter Chair of IEEE Information Theory in the Kitchener-Waterloo section.
Farnaz Derakhshan is Assistant Professor, and the Director of Multi-Agent Systems laboratory at Faculty of Electrical and Computer Engineering, University of Tabriz, Iran. She received her PhD in Artificial Intelligence from the University of Liverpool, in the UK. Her main research interests include multi-agent systems and its applications, normative multi-agent systems, multi-agent learning, Internet of Things and swarm intelligence.
