國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Deep learning for security and priva...

Kumar, Neeraj.

Deep learning for security and privacy preservation in IoT

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning for security and privacy preservation in IoTedited by Aaisha Makkar, Neeraj Kumar.
其他作者:	Makkar, Aaisha.
出版者:	Singapore :Springer Singapore :2021.
面頁冊數:	1 online resource (xii, 179 p.) :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computer networksSecurity measures.
電子資源:	https://doi.org/10.1007/978-981-16-6186-0
ISBN:	9789811661860$q(electronic bk.)

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$q(electronic bk.)

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

185597
Computer networks
--Security measures.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.8

Deep learning for security and privacy preservation in IoT
LDR:02750nmm a2200361 a 4500 001 614765
003 DE-He213
005 20220403213713.0
006 m o d
007 cr nn 008maaau
008 220802s2021 si s 0 eng d
020 $a 9789811661860$q(electronic bk.)
020 $a 9789811661853$q(paper)
024 7 $a 10.1007/978-981-16-6186-0 $2 doi
035 $a 978-981-16-6186-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.A25
072 7 $a UR $2 bicssc
072 7 $a UTN $2 bicssc
072 7 $a COM053000 $2 bisacsh
072 7 $a UR $2 thema
072 7 $a UTN $2 thema
082 0 4 $a 005.8 $2 23
090 $a QA76.9.A25 $b D311 2021
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.
650 0 $a Computer networks $x Security measures. $3 185597
650 0 $a Internet of things $x Security measures. $3 770009
650 0 $a Deep learning (Machine learning) $3 913129
650 1 4 $a Data and Information Security. $3 913130
650 2 4 $a Internet of Things. $3 913131
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Makkar, Aaisha. $3 913128
700 1 $a Kumar, Neeraj. $3 865640
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Signals and communication technology. $3 557664
856 4 0 $u https://doi.org/10.1007/978-981-16-6186-0
950 $a Computer Science (SpringerNature-11645)