Physical Layer Secret Key Generation (PSKG) exploits wireless channel reciprocity to establish cryptographic keys between legitimate users, providing a promising solution for securing IoT networks. However, reciprocity in time-division duplex (TDD) systems is often compromised by hardware imperfections and channel noise, posing significant challenges for reliable key generation. To address these issues, this paper proposes an autoencoder-based noise augmentation (AE-NA) model that operates in an unsupervised manner. Specifically, the proposed model integrates dynamic Gaussian noise augmentation directly into the autoencoder architecture. This approach enhances feature extraction capabilities and improves noise robustness, enabling the network to learn noise-invariant representations effectively from noisy channel estimates. Furthermore, we present a comprehensive PSKG framework that incorporates the proposed AE-NA model. Reported results demonstrate that the AE-NA-based PSKG significantly reduces the key disagreement ratio (KDR) while enhancing key randomness, thereby providing a scalable and effective solution for secure key generation in dynamic IoT environments.
Physical Layer Secret Key Generation (PSKG) exploits wireless channel reciprocity to establish cryptographic keys between legitimate users, providing a promising solution for securing IoT networks. However, reciprocity in time-division duplex (TDD) systems is often compromised by hardware imperfections and channel noise, posing significant challenges for reliable key generation. To address these issues, this paper proposes an autoencoder-based noise augmentation (AE-NA) model that operates in an unsupervised manner. Specifically, the proposed model integrates dynamic Gaussian noise augmentation directly into the autoencoder architecture. This approach enhances feature extraction capabilities and improves noise robustness, enabling the network to learn noise-invariant representations effectively from noisy channel estimates. Furthermore, we present a comprehensive PSKG framework that incorporates the proposed AE-NA model. Reported results demonstrate that the AE-NA-based PSKG significantly reduces the key disagreement ratio (KDR) while enhancing key randomness, thereby providing a scalable and effective solution for secure key generation in dynamic IoT environments.
Physical-layer secret key generation (PSKG) is a well-known and effective method for boosting wireless security in the Internet of Things (IoT). This technique creates cryptographic keys from correlated wireless channel measurements by taking advantage of the reciprocal nature of uplink and downlink channels. However, in time-division duplex (TDD) systems, issues such as hardware imperfection and noise can disrupt channel reciprocity, making the key generation process more difficult. This paper presents a new PSKG approach that tackles these issues by using deep learning to enhance channel reciprocity, even with imperfect channel state information (CSI). Specifically, a denoising sparse autoencoder (DSAE) is introduced to efficiently learn and extract channel reciprocity features in TDD OFDM systems. Furthermore, this study presents a low-complexity DSAE-based PSKG scheme that utilizes the phase of channel frequency responses (CFR) for key generation. The reported results indicate that the proposed DSAE-based PSKG method notably decreases the key disagreement ratio (KDR) while improving randomness, offering a robust and practical solution for securing wireless communications in IoT environments.
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.
This article presents a novel low-complexity deep learning model for physical-layer secret key generation (PSKG), specifically designed to enhance wireless security in the Internet of Things (IoT). PSKG typically generates cryptographic keys by exploiting the reciprocal nature of wireless channels; however, this reciprocity is frequently compromised in time-division duplex (TDD) systems due to hardware imperfections and noise, which significantly complicate the key generation process. To effectively address these challenges, the proposed deep neural network (DNN) is developed to efficiently learn and enhance reciprocity features, even in the presence of imperfect channel state information (CSI). Furthermore, the study introduces a DNN-based PSKG method that strategically leverages the phase of channel frequency responses for key extraction. The results conclusively demonstrate that the proposed model substantially reduces the key disagreement ratio (KDR) and enhances randomness, thus providing a robust and practical solution for securing wireless communications in IoT environments.
Physical layer secret key generation (PSKG) lever-ages the reciprocal properties of wireless channels to establish cryptographic keys between legitimate users. However, in time-division duplex (TDD) systems, this reciprocity can be compromised by hardware imperfections and channel noise, complicating the key generation process. To address these challenges, we propose a shared cross-prediction neural network (SCP-NN) model that operates in an unsupervised manner, eliminating the need for perfect channel state information (CSI). Specifically, the proposed SCP-NN model employs a cross-prediction strategy that minimizes the difference between the predicted channels of legitimate users, ensuring that the outputs converge toward each other. Furthermore, we present a comprehensive PSKG procedure that leverages the SCP-NN architecture to extract key information from the phase characteristics of the channel frequency responses. Experimental results demonstrate that the proposed SCP-NN significantly reduces the key disagreement ratio (KDR), providing a robust and effective solution for secure key generation in dynamic IoT environments.
Physical layer encryption is a promising solution to provide transmission security for Internet-of-Things (IoT). To realize physical layer encryption, channel state information (CSI) as a random source to generate secret keys should be exploited. The reciprocity of wireless channel between legitimate communication nodes which is typically assumed for time division duplex (TDD) systems, is of critical importance to ensure the consistency in generated keys at two nodes. However, in practical TDD systems, due to the asynchronous measurement of CSI, the receiver noise and the the difference in hardware, etc., the reciprocity of channel measurements between legitimate parties is hard to be guaranteed, which makes physical layer key generation approach infeasible. To address this issue, we propose a wireless key generation scheme based on deep learning techniques. Firstly, a feature extraction network, which consists of two auto-encoders trained jointly, is developed to extract the common feature from the different yet correlated channel observations at two legitimate nodes, which will be used as the source to generate keys. Then a quantizer with an adaptive guard band width is devised to output a binary secret-key sequence from the feature vector. Numerical results show that the proposed scheme achieves remarkable improvement in terms of key disagreement rate (KDR) and key generation rate (KGR) compared existing counterparts in the literature.
In this paper, we propose denoising autoencoder (DAE)-assisted secret key generation (SKG), where channel noise reciprocity imperfections induced due to wireless channel measurements are suppressed, hence significantly enhancing the reliability and efficiency. More specifically, the DAE is capable of capturing the intrinsic structure of input distributions, reconstructing the original data structure, and removing additive noise while preserving the essential structure of signals. In our analysis, it is demonstrated that the proposed SKG scheme exhibits higher performance than the conventional schemes in terms of key disagreement rate (KDR), secret key capacity (SKC), and randomness of the generated keys.