Deep learning for security and privacy preservation in IoT /
Saved in:
| 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: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/13398257 |
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| 245 | 0 | 0 | |a Deep learning for security and privacy preservation in IoT / |c Aaisha Makkar, Neeraj Kumar, editors. |
| 264 | 1 | |a Singapore : |b Springer, |c 2021. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations (black and white, and color). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Signals and communication technology | |
| 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. | ||
| 504 | |a Includes bibliographical references. | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Computer networks |x Security measures. |0 http://id.loc.gov/authorities/subjects/sh94001277 | |
| 650 | 0 | |a Internet of things |x Security measures. | |
| 650 | 0 | |a Deep learning (Machine learning) |0 http://id.loc.gov/authorities/subjects/sh2021006947 | |
| 650 | 6 | |a Réseaux d'ordinateurs |x Sécurité |x Mesures. | |
| 650 | 6 | |a Internet des objets |x Sécurité |x Mesures. | |
| 650 | 7 | |a Computer networks |x Security measures. |2 fast |0 (OCoLC)fst00872341 | |
| 650 | 7 | |a Deep learning (Machine learning) |2 fast |0 (OCoLC)fst02032663 | |
| 655 | 0 | |a Electronic books. | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Makkar, Aaisha, |e editor. | |
| 700 | 1 | |a Kumar, Neeraj |c (Computer scientist), |e editor. |0 http://id.loc.gov/authorities/names/n2020028855 | |
| 776 | 0 | 8 | |i Print version: |a MAKKAR, AAISHA. KUMAR, NEERAJ. |t DEEP LEARNING FOR SECURITY AND PRIVACY PRESERVATION IN IOT. |d [Place of publication not identified] : SPRINGER VERLAG, SINGAPOR, 2022 |z 9811661855 |w (OCoLC)1264139321 |
| 830 | 0 | |a Signals and communication technology. |0 http://id.loc.gov/authorities/names/no2002055234 | |
| 856 | 4 | 0 | |u https://link.springer.com/10.1007/978-981-16-6186-0 |y Springer Nature |
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| 928 | |t Library of Congress classification |a QA76.9.A25 |l Online |c UC-FullText |u https://link.springer.com/10.1007/978-981-16-6186-0 |z Springer Nature |g ebooks |i 13539463 | ||