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.