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Industrial Internet...
Industrial Internet of Things Security enhanced with Deep Learning Approaches for Smart Cities
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- Fontes, Afonso, 1987 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik, Software Engineering (GU),Institutionen för data- och informationsteknik, Software Engineering (GU)
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(creator_code:org_t)
- 2021
- 2021
- Engelska.
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Ingår i: IEEE Internet of Things Journal. - 2327-4662.
- Relaterad länk:
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https://gup.ub.gu.se...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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 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 deep learning techniques to enhance IIoT security.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Smart cities
- Sensors
- Security
- Intelligent sensors
- Business
- Sensor systems
- Malware
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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