國立虎尾科技大學 |

AI-Enabled Threat Detection and Security Analysis for Industrial IoT

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	AI-Enabled Threat Detection and Security Analysis for Industrial IoT / edited by Hadis Karimipour, Farnaz Derakhshan.
其他作者:	Derakhshan, Farnaz.
面頁冊數:	VIII, 250 p. 94 illus., 82 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-76613-9
ISBN:	9783030766139

AI-Enabled Threat Detection and Security Analysis for Industrial IoT
AI-Enabled Threat Detection and Security Analysis for Industrial IoT [electronic resource] /edited by Hadis Karimipour, Farnaz Derakhshan. - 1st ed. 2021. - VIII, 250 p. 94 illus., 82 illus. in color.online resource.

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.

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.

ISBN: 9783030766139

Standard No.: 10.1007/978-3-030-76613-9doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: TK5105.59

Dewey Class. No.: 005.8

AI-Enabled Threat Detection and Security Analysis for Industrial IoT
LDR:04072nam a22003975i 4500 001 1051387
003 DE-He213
005 20210921175353.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030766139 $9 978-3-030-76613-9
024 7 $a 10.1007/978-3-030-76613-9 $2 doi
035 $a 978-3-030-76613-9
050 4 $a TK5105.59
072 7 $a UTN $2 bicssc
072 7 $a COM043050 $2 bisacsh
072 7 $a UTN $2 thema
082 0 4 $a 005.8 $2 23
245 1 0 $a AI-Enabled Threat Detection and Security Analysis for Industrial IoT $h [electronic resource] / $c edited by Hadis Karimipour, Farnaz Derakhshan.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a VIII, 250 p. 94 illus., 82 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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.
520 $a 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.
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Cyber-physical systems, IoT. $3 1226036
650 1 4 $a Mobile and Network Security. $3 1211619
650 0 $a Machine learning. $3 561253
650 0 $a Artificial intelligence. $3 559380
650 0 $a Embedded computer systems. $3 562313
650 0 $a Internet of things. $3 1023130
650 0 $a Computer engineering. $3 569006
650 0 $a Computer networks $x Security measures. $3 555385
700 1 $a Derakhshan, Farnaz. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1355896
700 1 $a Karimipour, Hadis. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1324171
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030766122
776 0 8 $i Printed edition: $z 9783030766146
776 0 8 $i Printed edition: $z 9783030766153
856 4 0 $u https://doi.org/10.1007/978-3-030-76613-9
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)