Industrial Internet-of-Things Security Enhanced With Deep Learning Approaches for Smart Cities

The significant evolution of the Internet of Things (IoT) enabled the development of numerous devices able to improve many aspects in various fields in the industry for smart cities where machines have replaced humans. With the reduction in manual work and the adoption of automation, cities are getting more efficient and smarter. However, this evolution also made data even more sensitive, especially in the industrial segment. The latter has caught the attention of many hackers targeting Industrial IoT (IIoT) devices or networks, hence the number of malicious software, i.e., malware, has increased as well. In this article, we present the IIoT concept and applications for smart cities, besides also presenting the security challenges faced by this emerging area. We survey currently available deep learning (DL) techniques for IIoT in smart cities, mainly deep reinforcement learning, recurrent neural networks, and convolutional neural networks, and highlight the advantages and disadvantages of security-related methods. We also present insights, open issues, and future trends applying DL techniques to enhance IIoT security.

Automated summary

The significant evolution of the Internet of Things has led to the development of smart city devices that have replaced manual labor, increasing efficiency and intelligence in cities. However, the increased sensitivity of data, especially in the industrial sector, has attracted hackers targeting Industrial IoT devices or networks, leading to a rise in the number of malware infections. This article discusses the concept and applications of IIoT in smart cities, as well as the security challenges faced in this emerging area, along with available deep learning techniques to enhance IIoT security.