Deep learning for security and privacy preservation in IoT /

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Bibliographic Details
Imprint:	Singapore : Springer, 2021.
Description:	1 online resource (1 volume) : illustrations (black and white, and color).
Language:	English
Series:	Signals and communication technology Signals and communication technology.
Subject:	Computer networks -- Security measures. Internet of things -- Security measures. Deep learning (Machine learning) Computer networks -- Security measures. Deep learning (Machine learning) Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/13398257

Hidden Bibliographic Details
Other authors / contributors:	Makkar, Aaisha, editor. Kumar, Neeraj (Computer scientist), editor.
ISBN:	9789811661860 9811661863 9811661855 9789811661853
Notes:	Includes bibliographical references. Print version record.
Summary:	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.
Other form:	Print version: MAKKAR, AAISHA. KUMAR, NEERAJ. DEEP LEARNING FOR SECURITY AND PRIVACY PRESERVATION IN IOT. [Place of publication not identified] : SPRINGER VERLAG, SINGAPOR, 2022 9811661855
Standard no.:	10.1007/978-981-16-6186-0

Table of Contents:

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.

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