AI-Enabled Threat Detection and Security Analysis for Industrial IoT

This contributed volume provides the state-of-the-art development on security and privacy for cyber-physical systems (CPS) and industrial Internet of Things (IIoT). More specifically, this book discusses the security challenges in CPS and IIoT systems as well as how Artificial Intelligence (AI) and Machine Learning (ML) can be used to address these challenges. Furthermore, this book proposes various defence strategies, including intelligent cyber-attack and anomaly detection algorithms for different IIoT applications.
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.

Discusses anomaly detection, defensive mechanisms in critical IoT-enabled industries and cybersecurity concepts Presents emerging IoT-enabled CPSs such as precision agriculture and investigating their unique cybersecurity challenges and trade-offs between service availability and security Includes real-world problems, case studies and solutions from a wide variety of attack scenarios to provide intelligent automated IoT-enabled CPSs against cyberattack Introduces traditional IoT-enabled CPSs such smart grids

Autorentext

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.

Inhalt
Artificial Intelligence for Threat Detection and Analysis in Industrial IoT: Applications and Challenges.- Complementing IIoT Services through AI: Feasibility and Suitability.- Data Security and Privacy in Industrial IoT.- Blockchain Applications in the Industrial Internet of Things.- Application of Deep Learning on IoT-enabled Smart Grid Monitoring.- Cyber Security of Smart Manufacturing Execution Systems: A Bibliometric Analysis.- The Role of Machine Learning in IIoT Through FPGAs.- Deep Representation Learning for Cyber-Attack Detection in Industrial IoT.- Classification and Intelligent Mining of Anomalies in Industrial IoT.- A Snapshot Ensemble Deep Neural Network Model for Attack Detection in Industrial Internet of Things.- Privacy Preserving Federated Learning Solution for Security of Industrial Cyber Physical Systems.- A Multi-Stage Machine Learning Model for Security Analysis in Industrial Control System.- A Recurrent Attention Model for Cyber Attack Classification.