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003			ES-MaUEC
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008			210803s2021 sz | s |||| 0|eng d
020			_a9783030766139
024	7		_a10.1007/978-3-030-76613-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.8857 _b2021 EB
245	0	0	_aAI-Enabled Threat Detection and Security Analysis for Industrial IoT _cedited by Hadis Karimipour, Farnaz Derakhshan
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (VIII, 250 páginas) _b94 ilustraciones, 82 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_aArtificial 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	3		_aThis 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.
988			_aSpringer_Computer_2021
650		7	_2embne _9483083 _aInternet de los objetos
650		7	_2embne _aSeguridad informática _9158200
700	1		_aKarimipour, Hadis _eeditor literario
700	1		_aDerakhshan, Farnaz _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-76613-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eu _zSI