Bravos, George, Cabrera, Antonio J., Correa, Camilo, Danilovic ́, Dragan, Evangeliou, Nikolaos, Ezov, Gilad, Gajica, Zoran, Jakovetic ́, Dušan, Kallipolitis, Leonidas, Lukic, Milan, Mascolo, Julien, Masera, Davide, Mazo, Raul, Mezei, Ivan, Miaoudakis, Andreas, Miloševic ́, Nemanja, Oliff, William, Robin, Jacques, Smyrlis, Michail, Sakellari, Georgia ORCID: 0000-0001-7238-8700 , Stamatis, Giorgos, Stamenkovic ́, Dušan, Škrbic ́, Srd ̄an, Souveyet, Carine, Vantolas, Spyridon, Vasiliadis, Giorgos and Vukobratovic ́, Dejan (2022) Cybersecurity for industrial Internet of Things: architecture, models and lessons learned. IEEE Access, 4:2016. pp. 1-20. ISSN 2169-3536 (Online) (doi:https://doi.org/10.1109/ACCESS.2022.3225074)

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Abstract

Modern industrial systems now, more than ever, require secure and efficient ways of communication. The trend of making connected, smart architectures is beginning to show in various fields of the industry such as manufacturing and logistics. The number of IoT (Internet of Things) devices used in such systems is naturally increasing and industry leaders want to define business processes which are reliable, reproducible, and can be effortlessly monitored. With the rise in number of connected industrial systems, the number of used IoT devices also grows and with that some challenges arise. Cybersecurity in these types of systems is crucial for their wide adoption. Without safety in communication and threat detection and prevention techniques, it can be very difficult to use smart, connected systems in the industry setting. In this paper we describe two real-world examples of such systems while focusing on our architectural choices and lessons learned. We demonstrate our vision for implementing a connected industrial system with secure data flow and threat detection and mitigation strategies on real-world data and IoT devices. While our system is not an off-the-shelf product, our architecture design and results show advantages of using technologies such as Deep Learning for threat detection and Blockchain enhanced communication in industrial IoT systems and how these technologies can be implemented. We demonstrate empirical results of various components of our system and also the performance of our system as-a-whole.

Item Type:	Article
Uncontrolled Keywords:	anomaly detection; blockchain; cybersecurity; deep learning; Internet of Things
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	07 Jul 2023 15:24
URI:	http://gala.gre.ac.uk/id/eprint/38112

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