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Spatial de-correlation of generated keys from wireless channels using adversarial deep learning

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Abstract

Physical-layer Key Generation (PKG) is the major candidate for use in secure wireless communications and Internet of Things (IoT) systems. Using Deep Learning (DL) and the Band Feature Mapping (BFM) method leads to reciprocal features, which is an essential requirement for the key generation in Orthogonal Frequency-Division Multiplexing Frequency Division Duplexing systems. Additionally, randomness and spatial de-correlation are two other essential requirements of secure PKG schemes. When the distance of an eavesdropper from a legal user is short, the eavesdropper can experience a correlated fading and generate the secret key.Other works assume that the adversary is far away from legitimate users, whereas the proposed scheme allows the adversary to approach the legitimate users without sacrificing the security Conventional DL-based BFM includes an offline training stage using a pre-collected dataset. To solve the spatial correlation problem, this paper simultaneously uses the concepts of physical layer security and adversarial training. Moreover, a DL-based adversary in the PKG model is considered which has not been studied yet. Simulation results confirm the effectiveness of the proposed Adversarial DL (ADL) key generation scheme in terms of Key Error Rate and Key Generation Rate. Our results show that using the proposed training strategy the illegal user can only generate a random key with an error rate of about 0.5. In the meantime, this method maintains the performance of the generated key by the legal users under a certain level. The mentioned features make ADL key generation scheme an appealing candidate for applications, such as secure cloud-based communications, low-size networks, and resource-constrained IoT.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Data sharing and data citation is encouraged.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran

    Amir Aliabadian, Mohammadreza Zahabi & Majid Mobini

  2. Department of Electronic and Communication Engineering, Shomal University, Amol, Mazandaran, Iran

    Amir Aliabadian

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  1. Amir Aliabadian
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  2. Mohammadreza Zahabi
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  3. Majid Mobini
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We the undersigned declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We understand that the Corresponding Author is the sole contact for the Editorial process. He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs.

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Correspondence to Mohammadreza Zahabi.

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Aliabadian, A., Zahabi, M. & Mobini, M. Spatial de-correlation of generated keys from wireless channels using adversarial deep learning. Int. J. Inf. Secur. (2024). https://doi.org/10.1007/s10207-024-00831-1

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