Physical-layer key generation (PKG) enables wireless devices to establish shared keys from reciprocal channel observations without directly exchanging the key. This capability is attractive for edge networks, where distributed and resource-constrained devices may require lightweight key establishment with limited access to centralized infrastructure. We introduce a channel-informed neural network for PKG that derives binary key features directly from received IQ measurements while explicitly grounding the learned representation in the underlying multipath channel. The proposed multi-task recurrent neural network jointly learns reciprocity-preserving binary features and an auxiliary channel estimate using a training objective that combines deep metric learning with channel-informed supervision. Structured channel sounding enables channel estimation from over-the-air measurements, while Sionna-RT ray tracing is used to augment training with additional propagation conditions. We evaluate the framework using indoor and outdoor software-defined-radio measurements collected on the POWDER radio testbed. Across all evaluated scenarios, the proposed model produces lower bit disagreement for reciprocal Alice-Bob observations than for Eve-related observations. Ray-traced data augmentation substantially improves key diversity, increasing the unique-key rate to 0.94, 0.99, and 0.99 across the indoor and two outdoor scenarios, respectively. Successfully reconciled channel-informed keys pass the selected NIST randomness tests prior to SHA-3 privacy amplification. The results demonstrate the potential of channel-informed representation learning for decentralized wireless key establishment while highlighting an important tradeoff between key diversity and reconciliation reliability.
Physical-layer key generation (PKG) enables wireless devices to establish shared keys from reciprocal channel observations without directly exchanging the key. This capability is attractive for edge networks, where distributed and resource-constrained devices may require lightweight key establishment with limited access to centralized infrastructure. We introduce a channel-informed neural network for PKG that derives binary key features directly from received IQ measurements while explicitly grounding the learned representation in the underlying multipath channel. The proposed multi-task recurrent neural network jointly learns reciprocity-preserving binary features and an auxiliary channel estimate using a training objective that combines deep metric learning with channel-informed supervision. Structured channel sounding enables channel estimation from over-the-air measurements, while Sionna-RT ray tracing is used to augment training with additional propagation conditions. We evaluate the framework using indoor and outdoor software-defined-radio measurements collected on the POWDER radio testbed. Across all evaluated scenarios, the proposed model produces lower bit disagreement for reciprocal Alice-Bob observations than for Eve-related observations. Ray-traced data augmentation substantially improves key diversity, increasing the unique-key rate to 0.94, 0.99, and 0.99 across the indoor and two outdoor scenarios, respectively. Successfully reconciled channel-informed keys pass the selected NIST randomness tests prior to SHA-3 privacy amplification. The results demonstrate the potential of channel-informed representation learning for decentralized wireless key establishment while highlighting an important tradeoff between key diversity and reconciliation reliability.
Physical layer security (PLS) can be used to pro-vide keyless and innately secure communications by exploiting the propagation characteristics of the wireless channel at the physical layer (PHY) layer to co-generate cipher keys for symmetric encryption. While existing methods use channel state information as input for secret key generation, we consider a novel approach that derives features from frequency-time domain representation of in-phase and quadrature (IQ) samples to derive initial keys with high agreement rate. A recurrent neural network (RNN) with a quadruplet loss function is trained based on spectrograms collected from bi-directional time-division duplex channel probing between the two parties and on spectrograms collected from a passive eavesdropper. Information reconciliation is achieved using Reed Solomon codes. At the last step of the protocol, a 512-bit cryptographic key is generated by feeding the reconciled bit sequence to the Secure Hash Algorithm 3. Our experiments with software-defined radios in both indoor and outdoor environments evaluate the proposed key generation system under different channel propagation environments and hardware inaccuracies. We demonstrate that the proposed RNN-assisted channel feature extractor trained on indoor channel probe data operates successfully in two outdoor setups and produces up to 70 amplified keys out of 100 probes exchanged between two trusted parties i.e., Alice and Bob, while the passive eavesdropper fails to directly predict any of the generated keys.
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.
Physical-layer key generation (PKG) establishes cryptographic keys from highly correlated measurements of wireless channels, which relies on reciprocal channel characteristics between uplink and downlink, is a promising wireless security technique for Internet of Things (IoT). However, it is challenging to extract common features in frequency-division duplexing (FDD) systems as uplink and downlink transmissions operate at different frequency bands whose channel frequency responses are not reciprocal anymore. Existing PKG methods for FDD systems have many limitations, i.e., high overhead and security problems. This article proposes a novel PKG scheme that uses the feature mapping function between different frequency bands obtained by deep learning to make two users generate highly similar channel features in FDD systems. In particular, this is the first time to apply deep learning for PKG in FDD systems. We first prove the existence of the band feature mapping function for a given environment and a feedforward network with a single hidden layer can approximate the mapping function. Then, a key generation neural network (KGNet) is proposed for reciprocal channel feature construction, and a key generation scheme based on the KGNet is also proposed. Numerical results verify the excellent performance of the KGNet-based key generation scheme in terms of randomness, key generation ratio, and key error rate. Besides, the overhead analysis shows that the method proposed in this article can be used for resource-constrained IoT devices in FDD systems.
Reliable communication over Wireless Body Area Networks (WBANs) is an essential aspect of Wireless Body Area Networks (WBANs) because the physiological data flows between wearable sensor nodes is sensitive data. The convectional cryptography methods usually apply a lot of computational complexity as well as energy overhead besides which they are not going to fit in the ultra-low-power WBANs. In the current paper a light-weight, energy-efficient Physical Layer Key Generation (PLKG) scheme the possibilities of which based on the native randomness and reciprocity of the wireless channel is used to generate secure symmetric keys is presented. With WBANs, there is a lot of temporal and spatial dynamics in the human body and its surrounding environment, which causes great variability in channel characteristics, in terms of Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). Such physical layer parameters can be used as perfect entropy sources in generation of unique, unpredictable secret keys with whose knowledge, communication between nodes is possible and mutually exclusive only. The channel probing in the proposed framework is also robust, multi-bit quantization is performed adaptively, information reconciliation is performed through error correction codes, and privacy amplification is made through cryptographic hashing. We have developed a resilient system against bit mismatch and efficiently against passively eavesdropping, we guarantee the secrecy of the generated keys under adversary situation. With realistic WBAN models simulation and experiment validation shows that the proposed approach is capable of generating keystokes with very high key generation rate (50 bps) and a bit mismatch rate of lower than 3 percent as compared to existing schemes that solely relied on RSSI, making this protocol more secure and reliable to use. In addition, the framework meets the statistical randomness requirements specified by NIST and it does not leak mutual information to eavesdroppers. The scheme allows real-time use on resource-constrained devices, e.g., wearable medical devices and health-monitoring sensor, as it has low computational and memory overhead. The findings establish the feasibility of employing physical layer properties to achieve secure WBANs communication without any pre-shared keys or a centralized trust paradigm, thus, opening the way to scalable and privacy-preserving healthcare systems.
Physical-layer secret key generation (PLKG) is a new research area that has emerged in recent years. It is aimed at scenarios where legitimate IoT devices communicate directly, interacting with confidential information for lower overhead and higher security by using wireless channel. When applying it to wireless feature extraction, noise removal is not taken into account in current deep learning networks. To address these problems, the PLKG scheme based on bidirectional convergence feature learning convolutional network (BCFL-based scheme) is proposed, which consists of neural network called BCFL and a new quantization method to achieve better secret key generation. Unlike existing PLKG schemes that enabling both parties to communicate for obtaining higher channel feature similarities, when training it, channel state information (CSI) obtained by channel estimation for two legitimate devices during coherent time is used as inputs; and mean square error (MSE) between two outputs is used as a result of loss function for iterative training. Thus, it can obtain better denoising ability with guaranteed low computational resource consumption, and two legitimate devices can obtain highly correlated channel features. Multiple quantization method is also proposed to address low secret key generation rate (KGR) and low-secret key randomness (KR). The results show that the proposed BCFL-based scheme has a lower MSE than other schemes in different scenarios, indicating that it has better capability to learn channel reciprocity; and secret key error rate (KER) and time consumption are only about 50% of other schemes, which is a significant performance improvement.
Secret keys can be generated from reciprocal channels to be used for shared secret key encryption. However, challenges arise in practical scenarios from non-reciprocal measurements of reciprocal channels due to changing channel conditions, hardware inaccuracies and estimation errors resulting in low key generation rate (KGR) and high key disagreement rates (KDR). To combat these practical issues, we propose KNEW Key Generation using NEural Networks from Wireless Channels, which extracts the implicit features of channel in a compressed form to derive keys with high agreement rate. Two Neural Networks (NNs) are trained simultaneously to map each other's channel estimates to a different domain, the latent space, which remains inaccessible to adversaries. The model also minimizes the distance between the latent spaces generated by two trusted pair of nodes, thus improving the KDR. Our simulated results demonstrate that the latent vectors of the legitimate parties are highly correlated yielding high KGR (≈ 64 bits per measurement) and low KDR (<0.05 in most cases). Our experiments with over-the-air signals show that the model can adapt to realistic channels and hardware inaccuracies, yielding over 32 bits of key per channel estimation without any mismatch.