Cybersecurity for Industrial Internet of Things: Architecture, Models and Lessons Learned(Article)(Open Access)
- aInformation Technology for Market Leadership (ITML), Athina, Athens, 115 25, Greece
- bInfineon Technologies Ag (IFAG), Neubiberg, 85579, Germany
- cUniversity of Paris 1 Panthéon-Sorbonne (UP1PS), Paris, 75231, France
- dA1, Serbia (VIP), Belgrade, 11070, Serbia
- eIbm Research (IBM), Haifa, 3498825, Israel
- fUniversity of Novi Sad, Faculty of Sciences (UNSPMF), Novi Sad, 21000, Serbia
- gAegis It Research (AEGIS), Braunschweig, 38106, Germany
- hUniversity of Novi Sad, Faculty of Technical Sciences, Novi Sad, 21000, Serbia
- iCentro Ricerche Fiat (CRF), Orbassano, 10043, Italy
- jENSTA-Bretagne, Brest, 29200, France
- kSphynx Technology Solutions Ag (STS), Zug, 6300, Switzerland
- lUniversity of Greenwich (UoG), School of Computing and Mathematical Sciences, London, SE10 9LS, United Kingdom
- mEsiea, Ivry-sur-Seine, 94200, France
- nFoundation for Research and Technology (FORTH), Heraklion, 70013, Greece
- oHellenic Mediterranean University, Department of Management Science and Technology, Chania, 731 33, Greece
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. © 2013 IEEE.
Indexed keywords
| Engineering controlled terms: | Accident preventionArchitectureBlockchainDeep learningEngineering educationProduct design |
|---|---|
| Engineering uncontrolled terms | Anomaly detectionArchitecture modelingBlock-chainCyber securityDeep learningIndustrial systemsInternet of things architecturesReal-worldSmart architecturesThreat detection |
| Engineering main heading: | Internet of things |
Funding details
| Funding sponsor | Funding number | Acronym |
|---|---|---|
| Horizon 2020 Framework Programme See opportunities by H2020 | H2020 | |
| European Commission See opportunities by EC | 833828 | EC |
| European Commission See opportunities by EC | EC |
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1
This work was supported by the H2020 Project C4IIoT Funded by the European Commission under Agreement 833828.
- ISSN: 21693536
- Source Type: Journal
- Original language: English
- DOI: 10.1109/ACCESS.2022.3225074
- Document Type: Article
- Publisher: Institute of Electrical and Electronics Engineers Inc.
Milosevic, N.; University of Novi Sad, Faculty of Sciences (UNSPMF), Novi Sad, Serbia;
© Copyright 2022 Elsevier B.V., All rights reserved.
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