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FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0002-2772-4661
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-1954-760x
Northeastern University, USA.
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0001-8192-0893
2023 (English)In: ACM Trans. Internet Things, ISSN 2691-1914, Vol. 4, no 3Article in journal (Refereed) Published
Abstract [en]

Unidentified devices in a network can result in devastating consequences. It is, therefore, necessary to fingerprint and identify IoT devices connected to private or critical networks. With the proliferation of massive but heterogeneous IoT devices, it is getting challenging to detect vulnerable devices connected to networks. Current machine learning-based techniques for fingerprinting and identifying devices necessitate a significant amount of data gathered from IoT networks that must be transmitted to a central cloud. Nevertheless, private IoT data cannot be shared with the central cloud in numerous sensitive scenarios. Federated learning (FL) has been regarded as a promising paradigm for decentralized learning and has been applied in many different use cases. It enables machine learning models to be trained in a privacy-preserving way. In this article, we propose a privacy-preserved IoT device fingerprinting and identification mechanisms using FL; we call it FL4IoT. FL4IoT is a two-phased system combining unsupervised-learning-based device fingerprinting and supervised-learning-based device identification. FL4IoT shows its practicality in different performance metrics in a federated and centralized setup. For instance, in the best cases, empirical results show that FL4IoT achieves ∌99% accuracy and F1-Score in identifying IoT devices using a federated setup without exposing any private data to a centralized cloud entity. In addition, FL4IoT can detect spoofed devices with over 99% accuracy.

Place, publisher, year, edition, pages
Association for Computing Machinery , 2023. Vol. 4, no 3
Keywords [en]
identification, Internet of things, fingerprinting, machine learning, federated learning
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:ri:diva-65760DOI: 10.1145/3603257OAI: oai:DiVA.org:ri-65760DiVA, id: diva2:1787368
Available from: 2023-08-14 Created: 2023-08-14 Last updated: 2023-11-06Bibliographically approved

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Publisher's full texthttps://doi.org/10.1145/3603257

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Wang, HanEklund, DavidRaza, Shahid

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