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AuthorUnal, Devrim
AuthorHammoudeh, Mohammad
AuthorKhan, Muhammad Asif
AuthorAbuarqoub, Abdelrahman
AuthorEpiphaniou, Gregory
AuthorHamila, Ridha
Available date2023-04-04T09:09:12Z
Publication Date2021
Publication NameComputers and Security
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.cose.2021.102393
URIhttp://hdl.handle.net/10576/41668
AbstractBig data enables the optimization of complex supply chains through Machine Learning (ML)-based data analytics. However, data analytics comes with challenges such as the loss of control and privacy leading to increased risk of data breaches. Federated Learning (FL) is an approach in the ML arena that promises privacy-preserving and distributed model training. However, recent attacks on FL algorithms have raised concerns about the security of this approach. In this article, we advocate using Blockchain to mitigate attacks on FL algorithms operating in Internet of Things (IoT) systems. Integrating Blockchain and FL allows securing the trained models' integrity, thus preventing model poisoning attacks. This research presents a practical approach for the integration of Blockchain with FL to provide privacy-preserving and secure big data analytics services. To protect the security of user data and the trained models, we propose utilizing fuzzy hashing to detect variations and anomalies in FL-trained models against poisoning attacks. The proposed solution is evaluated via simulating attack modes in a quasi-simulated environment. 2021
SponsorThis publication was made possible by NPRP grant NPRP 10-0125-170250 from the Qatar National Research Fund (a member of Qatar Foundation).
Languageen
PublisherElsevier
SubjectBig data
Blockchain
Edge computing
Federated learning
IoT
TitleIntegration of federated machine learning and blockchain for the provision of secure big data analytics for Internet of Things
TypeArticle
Volume Number109
dc.accessType Abstract Only


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