Deep Learning for Security and Privacy Preservation in IoT / edited by Aaisha Makkar, Neeraj Kumar.

Format:

Book

Contributor:

Makkar, Aaisha., Editor.

Kumar, Neeraj, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Signals and communication technology 1860-4870

Signals and Communication Technology, 1860-4870

Language:

English

Subjects (All):

Data protection.

Internet of things.

Artificial intelligence.

Data and Information Security.

Internet of Things.

Artificial Intelligence.

Local Subjects:

Data and Information Security.

Internet of Things.

Artificial Intelligence.

Physical Description:

1 online resource (XII, 179 pages) : 58 illustrations, 44 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2021.

System Details:

text file PDF

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.

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.

Other Format:

Printed edition:

ISBN:

978-981-16-6186-0

9789811661860

Access Restriction:

Restricted for use by site license.