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Statistical inference attack against PHY-layer key extraction and countermeasures

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Abstract

The establishment of secure secret keys ahead of transmissions is one of the key issues in the field of information security. The security of traditional cryptographic secret key establishment mechanisms is seriously challenged by computing-intensive attacks, with the fast growth of high-performance computing. As an alternative, considerable efforts have been made to develop physical (PHY) layer security measures in recent years, such as link-signature-based (LSB) secret key extraction techniques. Those mechanisms have been believed secure, based on the fundamental assumption that wireless signals received at two locations are uncorrelated when separated by more than half a wavelength. However, this assumption does not hold in some circumstances under latest observations, rendering LSB key extraction mechanisms vulnerable to attacks. To address this problem, the formal theoretical analysis on channel correlations in both real indoor and outdoor environments is provided in this paper. Moreover, this paper proposes empirical statistical inference attacks (SIA) against LSB key extraction, whereby an adversary infers the signature of a target link. Consequently, the secret key extracted from that signature has been recovered by observing the surrounding links. In contrast to prior literature that assumes theoretical link-correlation models for the inference, our study does not make any assumption on link correlation. Instead, we employ machine learning (ML) methods for link inference based on empirically measured link signatures. We further propose a countermeasure against the SIAs, called forward-backward cooperative key extraction protocol with helpers (FBCH). In the FBCH, helpers (other trusted wireless nodes) are introduced to provide more randomness in the key extraction. Our experimental results have shown that the proposed inference methods are still quite effective even without making assumptions on link correlation. Furthermore, the effectiveness of the proposed FBCH protocol is validated by our experiment results.

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Acknowledgements

This research work is partially supported by the National Science Foundation under Grants CNS-1837034, CNS-1745254, CNS-2006998, CNS-1460897 and DGE-1623713. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors would like to thank Mr. James F. Huber for proofreading and editing the language of this paper.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Oakland University, Rochester, MI, 48309, USA

    Rui Zhu & Huirong Fu

  2. Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, 36849, USA

    Tao Shu

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  1. Rui Zhu
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Correspondence to Rui Zhu.

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Zhu, R., Shu, T. & Fu, H. Statistical inference attack against PHY-layer key extraction and countermeasures. Wireless Netw 27, 4853–4873 (2021). https://doi.org/10.1007/s11276-021-02769-x

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