Cyber security in smart cities: A review of deep learning-based applications and case studies
Authors:
- Dongliang Chen,
- Paweł Wawrzyński,
- Zhihan Lv
Abstract
On the one hand, smart cities have brought about various changes, aiming to revolutionize people's lives. On the other hand, while smart cities bring better life experiences and great convenience to people's lives, there are more hidden dangers of cyber security, including information leakage and malicious cyber attacks. The current cyber security development cannot keep up with the eager adoption of global smart city technologies so correct design based on deep learning methods is essential to protect smart city cyber. This paper summarizes the knowledge and interpretation of Smart Cities (SC), Cyber Security (CS), and Deep Learning (DL) concepts as well as discussed existing related work on IoT security in smart cities. Specifically, we briefly reviewed several deep learning models, including Boltzmann machines, restricted Boltzmann machines, deep belief networks, recurrent neural networks, convolutional neural networks, and generative adversarial networks. Then we introduced cyber security applications and use cases based on deep learning technology in smart cities. Finally, we describe the future development trend of smart city cyber security.
- Record ID
- WUTab4a2c9d946043588e18242e06d50c39
- Author
- Journal series
- Sustainable Cities and Society, ISSN 2210-6715, e-ISSN 2210-6707
- Issue year
- 2021
- Vol
- 66
- Pages
- 1-12
- Publication size in sheets
- 0.55
- Keywords in English
- Smart cities,Cyber security, Deep learningA review
- ASJC Classification
- ; ; ;
- DOI
- DOI:10.1016/j.scs.2020.102655 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S2210670720308714 Opening in a new tab
- Language
- (en) English
- File
-
- File: 1
- 1-s2.0-S2210670720308714-main.pdf
-
- Score (nominal)
- 100
- Score source
- journalList
- Score
- = 100.0, 10-05-2022, ArticleFromJournal
- Publication indicators
- = 21; = 44; : 2016 = 1.271; : 2020 (2 years) = 7.587 - 2020 (5 years) =7.308
- Citation count
- 62
- Uniform Resource Identifier
- https://repo.pw.edu.pl/info/article/WUTab4a2c9d946043588e18242e06d50c39/
- URN
urn:pw-repo:WUTab4a2c9d946043588e18242e06d50c39
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or PerishOpening in a new tab system.