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Improving security of the Internet of Things via RF fingerprinting based device identification system

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Abstract

Security is one of the primary concerns when designing wireless networks. Along detecting user identity, it is also important to detect the devices at the hardware level. The trivial identity create-and-discard process at higher layers of the protocol stack alone is not sufficient to effectively counter security threats, such as masquerading and Sybil attacks. To counter these attacks, various radio frequency fingerprinting-based solutions are proposed for the identification of the devices. However, these approaches use expansive devices for signal capturing and rely on high sampling rates and large feature sets for analysis. In this paper, we propose a radio frequency fingerprinting-based device identification technique. The proposed technique is tested on 4G-LTE network for combined intra and inter-manufacturer device detection. It uses low-cost software defined radio to capture smartphone emissions at a lower sampling rate, using our proposed preamble threshold-based detection algorithm. The results show that our proposed technique provides classification accuracy of 95.6% at different SNR levels.

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Acknowledgements

This research work is supported by University of Sharjah, UAE, under Grant No. 1702040389-P and 1702141143-P.

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Authors and Affiliations

  1. Department of Computer Science, University of Sharjah, Sharjah, UAE

    Sohail Abbas, Manar Abu Talib & Omnia Abu Waraga

  2. Department of Electrical and Computer Engineering, University of Sharjah, Sharjah, UAE

    Qassim Nasir, Douae Nouichi & Mohamed Abdelsalam

  3. College of Engineering and Information Technology, Ajman University, Ajman, UAE

    Atta ur Rehman Khan

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  1. Sohail Abbas
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  2. Qassim Nasir
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  3. Douae Nouichi
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  4. Mohamed Abdelsalam
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  5. Manar Abu Talib
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  6. Omnia Abu Waraga
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  7. Atta ur Rehman Khan
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Correspondence to Sohail Abbas.

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Abbas, S., Nasir, Q., Nouichi, D. et al. Improving security of the Internet of Things via RF fingerprinting based device identification system. Neural Comput & Applic 33, 14753–14769 (2021). https://doi.org/10.1007/s00521-021-06115-2

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  • DOI: https://doi.org/10.1007/s00521-021-06115-2

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