AI-enabled threat detection and security analysis for industrial IoT / Hadis Karimipour, Farnaz Derakhshan, editors.
Karimipour, Hadis, editor.; Derakhshan, Farnaz, editor.
2021
TK5105.8857 .A5 2021
Available Online

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Title AI-enabled threat detection and security analysis for industrial IoT / Hadis Karimipour, Farnaz Derakhshan, editors.
ISBN 9783030766139 (electronic bk.)
3030766136 (electronic bk.)
9783030766122
3030766128
DOI https://doi.org/10.1007/978-3-030-76613-9
Published Cham : Springer, [2021]
Copyright ©2021
Language English
Description 1 online resource (252 pages) : illustrations (chiefly color)
Item Number 10.1007/978-3-030-76613-9 doi
Call Number TK5105.8857 .A5 2021
Dewey Decimal Classification 004.67/8
Summary 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 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.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Karimipour, Hadis, editor.
Derakhshan, Farnaz, editor.
Available in Other Form AI-Enabled Threat Detection and Security Analysis for Industrial IoT.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
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.

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