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The 2020 International Conference on...

MacIntyre, John.

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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy = SPIoT-2020.. Volume 2 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy/ edited by John MacIntyre, Jinghua Zhao, Xiaomeng Ma.
Reminder of title:	SPIoT-2020.
remainder title:	SPIoT-2020
other author:	MacIntyre, John.
corporate name:	SPIoT (Conference)
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xxxii, 863 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Internet of things - Congresses. - Security measures -
Online resource:	https://doi.org/10.1007/978-3-030-62746-1
ISBN:	9783030627461

The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy = SPIoT-2020.. Volume 2 /
The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and PrivacySPIoT-2020.Volume 2 /[electronic resource] :SPIoT-2020edited by John MacIntyre, Jinghua Zhao, Xiaomeng Ma. - Cham :Springer International Publishing :2021. - xxxii, 863 p. :ill., digital ;24 cm. - Advances in intelligent systems and computing,v.12832194-5357 ;. - Advances in intelligent systems and computing ;v.1283..

This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.

ISBN: 9783030627461

Standard No.: 10.1007/978-3-030-62746-1doiSubjects--Topical Terms:

3221315
Internet of things
--Security measures--Congresses.

LC Class. No.: TK5105.8857 / .S65 2020

Dewey Class. No.: 004.678

The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy = SPIoT-2020.. Volume 2 /
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300 $a xxxii, 863 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in intelligent systems and computing, $x 2194-5357 ; $v v.1283
520 $a This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.
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