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A lightweight IoT device identification using enhanced behavioral-based features

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Abstract

As the Internet of Things (IoT) landscape expands, new devices with various functionalities are continuously being integrated into the IoT ecosystem. When traditional systems, which involve human interaction, are replaced by devices, it becomes crucial to upgrade the conventional authorization and authentication systems. This is essential to establish a new access control system designed to manage accessibility of multiple devices. Traditional device identification approaches often struggle to accommodate the dynamic behaviors exhibited by IoT devices. In response, this paper introduces an innovative approach that leverages enhanced behavioral features to generate a representation of device behavior. This representation is then employed to train machine learning models for classifying devices based on their behaviors. Furthermore, this paper also considers special scenarios where the access management system lacks access to full network traffic data. In such cases, device identification is achieved based on HTTPS features and user agent information. We conducted experimental analyses using real data from state-of-the-art IoT device profiling datasets. The performance results indicate that the extracted behavioral-based features have the capability to identify multiple IoT devices with various functionalities and vendors.

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Data Availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors express their gratitude to the anonymous reviewers for their valuable feedback.

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Author notes

  1. Jinkun Gui, Zeming Zhou, Fatemeh Nejati, Mansur Mirani, Gunjan Piya, Igor Opushnyev, Rongxing Lu, and Ali Ghorbani contributed equally to this work.

Authors and Affiliations

  1. Canadian Institute for Cybersecurity, Faculty of Computer Science, University of New Brunswick, 46 Dineen Drive, Fredericton, E3B 5A3, New Brunswick, Canada

    Mahdi Rabbani, Jinkun Gui, Zeming Zhou, Fatemeh Nejati, Rongxing Lu & Ali Ghorbani

  2. Mastercard Vancouver Tech Hub, Vancouver, British Columbia, Canada

    Mansur Mirani, Gunjan Piya & Igor Opushnyev

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  1. Mahdi Rabbani
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  2. Jinkun Gui
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Contributions

A and C contributed to writing the original draft. B and D were responsible for the methodology and feature extraction processes. E, F, and G handled review and editing. H and I provided supervision and guidance. All authors have read and approved the final version of the manuscript.

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Correspondence to Mahdi Rabbani.

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This article is part of the Topical Collection: 2 - Track on Security and Privacy

Guest Editors: Rongxing Lu

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Rabbani, M., Gui, J., Zhou, Z. et al. A lightweight IoT device identification using enhanced behavioral-based features. Peer-to-Peer Netw. Appl. 18, 2 (2025). https://doi.org/10.1007/s12083-024-01891-9

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