Deep Learning for Security and Privacy Preservation in IoT (Hardcover, 1st ed. 2021)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Signals and Communication Technology
Release date:	April 2022
First published:	2021
Editors:	Aaisha Makkar • Neeraj Kumar
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	179
Edition:	1st ed. 2021
ISBN-13:	978-981-16-6185-3
Categories:	Books > Computing & IT > Computer communications & networking > Network security Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	981-16-6185-5
Barcode:	9789811661853

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