Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond
Physical-layer secret key generation (PSKG) has emerged as a promising technique for enhancing wireless security in Internet of Things (IoT) networks by exploiting the reciprocity of uplink and downlink channels. However, in time-division duplex (TDD) systems, hardware impairments and channel noise...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11086605/ |
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| author | Anas Alashqar Ehsan Olyaei Torshizi Raed Mesleh Werner Henkel |
| author_facet | Anas Alashqar Ehsan Olyaei Torshizi Raed Mesleh Werner Henkel |
| author_sort | Anas Alashqar |
| collection | DOAJ |
| description | Physical-layer secret key generation (PSKG) has emerged as a promising technique for enhancing wireless security in Internet of Things (IoT) networks by exploiting the reciprocity of uplink and downlink channels. However, in time-division duplex (TDD) systems, hardware impairments and channel noise disrupt channel reciprocity, degrading key generation performance. To overcome these challenges, this paper introduces a deep learning–enhanced PSKG framework that effectively mitigates channel discrepancies and improves key generation reliability under imperfect channel state information (CSI). Specifically, a two-dimensional convolutional neural network–based autoencoder (2D CNN–AE) with a spatial self-attention (SSA) mechanism is developed to efficiently extract and learn channel reciprocity features in time-division duplex (TDD)-based fifth-generation (5G) networks. Additionally, a quantile-based quantization scheme is proposed to enhance key randomness and entropy, thereby strengthening security and resilience against potential threats. To facilitate comprehensive performance evaluation, a wiretap channel dataset is generated in accordance with 5G standards, encompassing diverse propagation conditions and including both legitimate users and an eavesdropper. Extensive simulation results demonstrate that the proposed 2D CNN–AE–SSA-based PSKG framework significantly reduces the key disagreement ratio (KDR), enhances key randomness, and maintains low computational complexity. These findings establish the proposed method as a robust and practical solution for securing wireless communications in resource-constrained IoT environments. |
| format | Article |
| id | doaj-art-0b76fcf31d6f473b829a5031cea1e7e5 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-0b76fcf31d6f473b829a5031cea1e7e52025-08-20T02:46:13ZengIEEEIEEE Access2169-35362025-01-011313174413175610.1109/ACCESS.2025.359092611086605Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and BeyondAnas Alashqar0https://orcid.org/0000-0001-8107-5132Ehsan Olyaei Torshizi1https://orcid.org/0000-0003-0724-0907Raed Mesleh2https://orcid.org/0000-0001-6838-2816Werner Henkel3https://orcid.org/0000-0003-4959-8560School of Computer Science and Engineering, Constructor University, Bremen, GermanySchool of Computer Science and Engineering, Constructor University, Bremen, GermanySchool of Electrical Engineering and Information Technology, German Jordanian University, Amman, JordanSchool of Computer Science and Engineering, Constructor University, Bremen, GermanyPhysical-layer secret key generation (PSKG) has emerged as a promising technique for enhancing wireless security in Internet of Things (IoT) networks by exploiting the reciprocity of uplink and downlink channels. However, in time-division duplex (TDD) systems, hardware impairments and channel noise disrupt channel reciprocity, degrading key generation performance. To overcome these challenges, this paper introduces a deep learning–enhanced PSKG framework that effectively mitigates channel discrepancies and improves key generation reliability under imperfect channel state information (CSI). Specifically, a two-dimensional convolutional neural network–based autoencoder (2D CNN–AE) with a spatial self-attention (SSA) mechanism is developed to efficiently extract and learn channel reciprocity features in time-division duplex (TDD)-based fifth-generation (5G) networks. Additionally, a quantile-based quantization scheme is proposed to enhance key randomness and entropy, thereby strengthening security and resilience against potential threats. To facilitate comprehensive performance evaluation, a wiretap channel dataset is generated in accordance with 5G standards, encompassing diverse propagation conditions and including both legitimate users and an eavesdropper. Extensive simulation results demonstrate that the proposed 2D CNN–AE–SSA-based PSKG framework significantly reduces the key disagreement ratio (KDR), enhances key randomness, and maintains low computational complexity. These findings establish the proposed method as a robust and practical solution for securing wireless communications in resource-constrained IoT environments.https://ieeexplore.ieee.org/document/11086605/Autoencoderself-attentionCNNdeep learningIoT securitychannel reciprocity |
| spellingShingle | Anas Alashqar Ehsan Olyaei Torshizi Raed Mesleh Werner Henkel Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond IEEE Access Autoencoder self-attention CNN deep learning IoT security channel reciprocity |
| title | Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond |
| title_full | Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond |
| title_fullStr | Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond |
| title_full_unstemmed | Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond |
| title_short | Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond |
| title_sort | secret key generation driven by attention based convolutional autoencoder and quantile quantization for iot security in 5g and beyond |
| topic | Autoencoder self-attention CNN deep learning IoT security channel reciprocity |
| url | https://ieeexplore.ieee.org/document/11086605/ |
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