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Deep learning for security and priva...

Makkar, Aaisha.

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Deep learning for security and privacy preservation in IoT

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for security and privacy preservation in IoT/ edited by Aaisha Makkar, Neeraj Kumar.
other author:	Makkar, Aaisha.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	1 online resource (xii, 179 p.) :ill., digital ;24 cm.
[NT 15003449]:	Metamorphosis of Industrial IoT using Deep Leaning -- Deep Learning Models and their Architectures for Computer Vision Applications: A Review -- IoT Data Security with Machine Learning Blockchain: Risks and Countermeasures -- A Review on Cyber Crimes on the Internet of Things -- Deep learning framework for anomaly detection in IoT enabled systems -- Anomaly Detection using Unsupervised Machine Learning Algorithms -- Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT -- Deep Learning based security preservation of IoT: An industrial machine health monitoring scenario -- Deep learning Models: An Understandable Interpretable Approaches.
Contained By:	Springer Nature eBook
Subject:	Computer networks - Security measures. -
Online resource:	https://doi.org/10.1007/978-981-16-6186-0
ISBN:	9789811661860

Deep learning for security and privacy preservation in IoT
Deep learning for security and privacy preservation in IoT[electronic resource] /edited by Aaisha Makkar, Neeraj Kumar. - Singapore :Springer Singapore :2021. - 1 online resource (xii, 179 p.) :ill., digital ;24 cm. - Signals and communication technology,1860-4870. - Signals and communication technology..

Metamorphosis of Industrial IoT using Deep Leaning -- Deep Learning Models and their Architectures for Computer Vision Applications: A Review -- IoT Data Security with Machine Learning Blockchain: Risks and Countermeasures -- A Review on Cyber Crimes on the Internet of Things -- Deep learning framework for anomaly detection in IoT enabled systems -- Anomaly Detection using Unsupervised Machine Learning Algorithms -- Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT -- Deep Learning based security preservation of IoT: An industrial machine health monitoring scenario -- Deep learning Models: An Understandable Interpretable Approaches.

This book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyber-attacks and proposes deep learning-based approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of existing solutions to cover the entire IoT network security spectrum, the book utilizes artificial neural network models, which are used to classify, recognize, and model complex data including images, voice, and text, to enhance the level of security and privacy of IoT. This is applied to several IoT applications which include wireless sensor networks (WSN), meter reading transmission in smart grid, vehicular ad hoc networks (VANET), industrial IoT and connected networks. The book serves as a reference for researchers, academics, and network engineers who want to develop enhanced security and privacy features in the design of IoT systems.

ISBN: 9789811661860

Standard No.: 10.1007/978-981-16-6186-0doiSubjects--Topical Terms:

579161
Computer networks
--Security measures.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.8

Deep learning for security and privacy preservation in IoT
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245 0 0 $a Deep learning for security and privacy preservation in IoT $h [electronic resource] / $c edited by Aaisha Makkar, Neeraj Kumar.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a 1 online resource (xii, 179 p.) : $b ill., digital ; $c 24 cm.
490 1 $a Signals and communication technology, $x 1860-4870
505 0 $a Metamorphosis of Industrial IoT using Deep Leaning -- Deep Learning Models and their Architectures for Computer Vision Applications: A Review -- IoT Data Security with Machine Learning Blockchain: Risks and Countermeasures -- A Review on Cyber Crimes on the Internet of Things -- Deep learning framework for anomaly detection in IoT enabled systems -- Anomaly Detection using Unsupervised Machine Learning Algorithms -- Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT -- Deep Learning based security preservation of IoT: An industrial machine health monitoring scenario -- Deep learning Models: An Understandable Interpretable Approaches.
520 $a This book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyber-attacks and proposes deep learning-based approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of existing solutions to cover the entire IoT network security spectrum, the book utilizes artificial neural network models, which are used to classify, recognize, and model complex data including images, voice, and text, to enhance the level of security and privacy of IoT. This is applied to several IoT applications which include wireless sensor networks (WSN), meter reading transmission in smart grid, vehicular ad hoc networks (VANET), industrial IoT and connected networks. The book serves as a reference for researchers, academics, and network engineers who want to develop enhanced security and privacy features in the design of IoT systems.
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650 0 $a Internet of things $x Security measures. $3 3217048
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650 2 4 $a Internet of Things. $3 3538511
650 2 4 $a Artificial Intelligence. $3 769149
650 0 $a Deep learning (Machine learning) $3 3538509
700 1 $a Makkar, Aaisha. $3 3538508
700 1 $a Kumar, Neeraj. $3 3443379
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830 0 $a Signals and communication technology. $3 1565543
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