Cybersecurity in a Scalable Smart City Framework Using Blockchain and Federated Learning for Internet of Things (IoT)

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dc.authorscopusidSeyed Salar Sefati / 57222602957
dc.authorscopusidBahman Arasteh / 39861139000
dc.authorwosidSeyed Salar Sefati / AAU-2556-2021
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.contributor.authorSefati, Seyed Salar
dc.contributor.authorCraciunescu, Razvan
dc.contributor.authorArasteh, Bahman
dc.contributor.authorHalunga, Simona
dc.contributor.authorFratu, Octavian
dc.contributor.authorTal, Irina
dc.date.accessioned2025-05-09T09:30:53Z
dc.date.available2025-05-09T09:30:53Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractHighlights: What are the main findings? Implementation of blockchain enhances the security and scalability of smart city frameworks. Federated Learning enables efficient and privacy-preserving data sharing among IoT devices. What are the implications of the main finding? The proposed framework significantly reduces the risk of data breaches in smart city infrastructures. Improved data privacy and security can foster greater adoption of IoT technologies in urban environments. Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications. © 2024 by the authors.
dc.description.sponsorshipThis work was supported by the Project \u201CMobility and Training for beyond 5G ecosystems (MOTOR5G)\u201D funded by the European Union\u2019s Horizon 2020 Program under the Marie Sk\u0142odowska Curie Actions (MSCA) Innovative Training Network (ITN) under Grant 861219. The present work is also supported to the H2020-MSCA-RISE \u201CResearch Collaboration and Mobility for Beyond 5G Future Wireless Networks (RECOMBINE)\u201D project with GA no. 872857.
dc.identifier.citationSefati, S. S., Craciunescu, R., Arasteh, B., Halunga, S., Fratu, O., & Tal, I. (2024). Cybersecurity in a Scalable Smart City Framework Using Blockchain and Federated Learning for Internet of Things (IoT). Smart Cities, 7(5), 2802-2841.
dc.identifier.doi10.3390/smartcities7050109
dc.identifier.endpage2841
dc.identifier.issn26246511
dc.identifier.issue5
dc.identifier.scopusqualityQ1
dc.identifier.startpage2802
dc.identifier.urihttp://dx.doi.org/10.3390/smartcities7050109
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7245
dc.identifier.volume7
dc.identifier.wosWOS:001340941700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorSefati, Seyed Salar
dc.institutionauthorArasteh, Bahman
dc.institutionauthoridSeyed Salar Sefati / 0000-0002-7208-3576
dc.institutionauthoridBahman Arasteh / 0000-0001-5202-6315
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofSmart Cities
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBlockchain
dc.subjectData Privacy
dc.subjectFederated Learning
dc.subjectIoT Security
dc.subjectSmart Cities
dc.titleCybersecurity in a Scalable Smart City Framework Using Blockchain and Federated Learning for Internet of Things (IoT)
dc.typeArticle

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