Industrial Internet-of-Things security enhanced with deep learning approaches for smart cities

Magaia, Naercio, Fonseca, Ramon, Muhammad, Khan, Segundo, Afonso H Fontes N, Lira Neto, Aloísio Vieira and De Albuquerque, Victor Hugo C (2021) Industrial Internet-of-Things security enhanced with deep learning approaches for smart cities. IEEE Internet of Things Journal, 8 (8). pp. 6393-6405. ISSN 2327-4662

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Abstract

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.

Item Type: Article
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Keywords: Smart cities, Sensors, Security, Intelligent sensors, Business, Sensor systems, Malware, Deep learning (DL), Industrial Internet of Things (IIoT), Internet of Things (IoT), security, smart cities
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 28 Feb 2022 09:23
Last Modified: 28 Feb 2022 09:30
URI: http://sro.sussex.ac.uk/id/eprint/104594

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