Physical Layer Authentication Research Articles

Channel state information based physical layer authentication for Wi‐Fi sensing systems using deep learning in Internet of things networks

Channel state information based physical layer authentication for Wi‐Fi sensing systems using deep learning in Internet of things networks

Exploiting Carrier Frequency Offset and Phase Noise for Physical Layer Authentication in UAV-Aided Communication Systems

Exploiting Carrier Frequency Offset and Phase Noise for Physical Layer Authentication in UAV-Aided Communication Systems

How to Leverage Double-Structured Sparsity of RIS Channels to Boost Physical-Layer Authentication

How to Leverage Double-Structured Sparsity of RIS Channels to Boost Physical-Layer Authentication

Exploiting Cascaded Channel Signature for PHY-Layer Authentication in RIS-Enabled UAV Communication Systems

Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communications face a critical security threat from impersonation attacks, where adversaries impersonate legitimate entities to infiltrate networks to obtain private data or unauthorized access. To combat such security threats, this paper proposes a novel physical layer (PHY-layer) authentication scheme for validating UAV identity in RIS-enabled UAV wireless networks. Considering that most existing works focus on traditional communication systems such as IoT and millimeter wave multiple-input multiple-output (MIMO) systems, there is currently no mature PHY-layer authentication scheme to serve RIS-UAV communication systems. To this end, our scheme leverages the unique characteristics of cascaded channels related to RIS to verify the legitimacy of UAV transmitting signals to the base station (BS). To be more precise, we first use the least squares estimate method and coordinate a descent-based algorithm to extract the cascaded channel feature. Next, we explore a quantizer to quantize the fluctuations of the channel gain that are related to the extracted channel feature. The 1-bit quantizer’s output findings are exploited to generate the authentication decision criteria, which are then tested using a binary hypothesis. The statistical signal processing technique is utilized to obtain the analytical formulations for detection and false alarm probabilities. We also conduct a computational complexity analysis of the proposed scheme. Finally, the numerical results validate the effectiveness of the proposed performance metric models and show that our detection performance can reach over 90% accuracy at a low signal-to-noise ratio (e.g., −8 dB), with a 10% improvement in detection accuracy compared with existing schemes.

Multi-attribute weighted convolutional attention neural network for multiuser physical layer authentication in IIoT

Compared with upper layer authentication, physical layer authentication (PLA) is essential in unmanned Industrial Internet of Things (IIoT) scenarios, owing to its low complexity and lightweight. However, in dynamic environments, as the amount of users expands, the accuracy of single-attribute-based authentication decreases drastically, which becomes an urgent issue for IIoT. Accordingly, this paper proposes a novel multi-attribute-based convolutional attention neural network (CANN) for multiuser PLA. Using characteristics such as amplitude, phase, and delay, the multiple attributes from a real industrial scene are first constructed into three-dimensional matrices fed into CANN. Then, attention blocks are designed to learn the correlation between attributes and extract the attribute parts that are more instrumental in the CANN to improve authentication accuracy. In addition, to avoid confusing multiple users, a center confidence loss is introduced, which adaptively adjusts the weight of the center loss and works together with the softmax loss to train the CANN. The effectiveness of the proposed CANN-based multiuser PLA and center confidence loss is supported by experimental results. Compared with the recently proposed latent perturbed convolutional neural network (LPCNN), the CANN-based scheme improves the authentication accuracy by 8.11%, which is superior to the existing learning-based approaches. As the CANN is further trained with the loss function that combines center confidence loss, the authentication accuracy can be improved by at least 2.22%.

Radio frequency fingerprint authentication based on feature fusion and contrastive learning

Radio frequency fingerprints (RFFs) are inherent attributes of wireless devices caused by differences in electronic components, which can serve as unique device identifiers and effective means of achieving physical layer authentication. However, traditional RFF approaches predominantly depend on manual feature extraction, which struggles to effectively distinguish devices due to its limited generalization capability as the number of devices grows. Additionally, current machine learning-based methods are designed for scenarios with a fixed set of trained devices while they fail to handle unknown devices. In this paper, we propose a novel contrastive learning-based RFF authentication framework to address the above challenges. The proposed framework integrates both manual feature extraction and deep learning-based techniques to enhance the generalization ability of RFFs, thereby enabling efficient differentiation among a vast number of devices. To further address the limitations of traditional machine learning approaches in dealing with unknown devices, we incorporate contrastive learning techniques into RFF authentication. By employing an input construction strategy, we generate positive and negative sample pairs of the RFFs to train an RFF compression network, allowing it to learn pivotal features within the fused RFFs. This process results in highly discriminative representations of the fused RFFs, ensuring effective authentication of open-set devices. The experimental results demonstrate that our method achieves an accuracy of 98% in open-set device classification even in the most complex experimental scenario, significantly outperforming other benchmark methods.

A novel physical layer authentication mechanism for static and mobile 3D underwater acoustic communication networks

In this paper, we present an innovative physical layer authentication approach for underwater acoustic communication networks. Our method leverages the transmitter nodes’ positions to establish a robust authentication mechanism. We consider two scenarios based on the mobility of the nodes. In the first scenario, underwater nodes (both legitimate and malicious) are static, while the second scenario assumes that the underwater nodes are mobile moving at a certain velocity. For both scenarios, we estimate position by analyzing the signals received at reference nodes that are strategically placed within a predetermined underwater area. Once the estimates are available, we propose binary hypothesis testing based on the estimated position to determine the legitimacy of the transmitter node. Furthermore, when the nodes are mobile, we perform velocity estimation at a certain time by taking the difference of the estimated coordinates, for which we also find the uncertainty in the estimation. We use a linear Kalman filter operation to track the legitimate node’s mobility. We provide closed-form expressions of the false alarm rate and missed detection rate resulting from binary hypothesis testing. We validate our proposed mechanism through simulation, demonstrating error behavior against link quality, malicious node location, and receiver operating characteristic (ROC) curves.

Physical Layer Authentication in Backscatter Communication: A Review

Physical Layer Authentication in Backscatter Communication: A Review

MAG-PUFs: Authenticating IoT devices via electromagnetic physical unclonable functions and deep learning

The challenge of authenticating Internet of Things (IoT) devices, particularly in low-cost deployments with constrained nodes that struggle with dynamic re-keying solutions, renders these devices susceptible to various attacks. This paper introduces a robust alternative mitigation strategy based on Physical-Layer Authentication (PLA), which leverages the intrinsic physical layer characteristics of IoT devices. These unique imperfections, stemming from the manufacturing process of IoT electronic integrated circuits (ICs), are difficult to replicate or falsify and vary with each function executed by the IoT device. We propose a novel lightweight authentication scheme, MAG-PUFs, that uses the unintentional Electromagnetic (EM) emissions from IoT devices as Physical Unclonable Functions (PUFs). MAG-PUFs operate by collecting these unintentional EM emissions during the execution of pre-defined reference functions by the IoT devices. The authentication is achieved by matching these emissions with profiles recorded at the time of enrollment, using state-of-the-art Deep Learning (DL) approaches such as Neural Networks (NN) and Autoencoders. Notably, MAG-PUFs offer compelling advantages: (i) it preserves privacy, as it does not require direct access to the IoT devices; and, (ii) it provides unique flexibility, permitting the selection of numerous and varied reference functions. We rigorously evaluated MAG-PUFs using 25 Arduino devices and a diverse set of 325 reference function classes. Employing a DL framework, we achieved a minimum authentication F1-Score of 0.99. Furthermore, the scheme’s efficacy in detecting impostor EM emissions was also affirmed, achieving a minimum F1-Score of 0.99. We also compared our solution to other solutions in the literature, showing its remarkable performance. Finally, we discussed code obfuscation techniques and the impact of Radio Frequency (RF) interference on the IoT authentication process.

Secure authentication in MIMO systems: exploring physical limits

Multiple-input multiple-output (MIMO) technology is employed to improve the reliability and capacity of wireless communication systems. However, the wireless communication environment creates vulnerabilities to spoofing attacks. Furthermore, the authentication challenges posed by the heterogeneous characteristics of wireless applications increase as diverse technologies facilitate the growing number of Internet of Things (IoT) devices. To address these challenges, adaptive physical-layer authentication (PLA) leveraging the inherent antenna diversity in MIMO systems is examined, and an information-theoretic perspective on PLA in MIMO systems is given. The real and imaginary components of the received reference signals are used as attributes with a single-class classification support vector machine (SCC-SVM). It is shown that the authentication performance improves with the number of antennas, and the proposed scheme provides robust authentication.

Variational autoencoder-assisted unsupervised hardware fingerprint authentication in a fiber network.

Physical-layer authentication (PLA) based on hardware fingerprints can safeguard optical networks against large-scale masquerade or active injection attacks. However, traditional schemes rely on massive labeled close-set data. Here, we propose an unsupervised hardware fingerprint authentication based on a variational autoencoder (VAE). Specifically, the triplets are generated through variational inference on unlabeled optical spectra and then applied to train the feature extractor, which has an excellent generalization ability and enables fingerprint feature extraction from previously unknown optical transmitters. The feasibility of the proposed scheme is experimentally verified by the successful classification of eight optical transmitters after a 20 km standard single-mode fiber (SSMF) transmission, to distinguish efficiently the rogue from legal devices. A recognition accuracy of 99% and a miss alarm rate of 0% are achieved even under the interference of multiple rogue devices. Moreover, the proposed scheme is verified to have a comparable performance with the results obtained from supervised learning.

Access-Based Lightweight Physical-Layer Authentication for the Internet of Things Devices

Access-Based Lightweight Physical-Layer Authentication for the Internet of Things Devices

Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency Response

Wireless communication plays a crucial role in the automation process in the industrial environment. However, the open nature of wireless communication renders industrial wireless sensor networks susceptible to malicious attacks that impersonate authorized nodes. The heterogeneity of the wireless transmission channel, coupled with hardware and software limitations, further complicates the issue of secure authentication. This form of communication urgently requires a lightweight authentication technique characterized by low complexity and high security, as inadequately secure communication could jeopardize the evolution of industrial devices. These requirements are met through the introduction of physical layer authentication. This article proposes novel deep learning (DL) models designed to enhance physical layer authentication by autonomously learning from the frequency domain without relying on expert features. Experimental results demonstrate the effectiveness of the proposed models, showcasing a significant enhancement in authentication accuracy. Furthermore, the study explores the efficacy of various DL architecture settings and traditional machine learning approaches through a comprehensive comparative analysis.

A RNN-based approach to physical layer authentication in underwater acoustic networks with mobile devices

Underwater acoustic communications are becoming a popular solution for underwater data communications and telemetry, making the authentication of transmitted data a necessity. In this paper, we propose a physical-layer authentication strategy for underwater acoustic networks (UWANs) with mobile devices. Such a scenario is more challenging than classical authentication scenarios in static networks, because the mobility of the receiver and/or transmitter implies that channel conditions slowly change over time. Thus, we cannot rely on the statistics of channel features to be stationary. In our proposed strategy, we assume that the receiver can rely on a set of sensors. We first extract a set of channel features, to be used to track the channel evolution over time. We then develop a long short-term memory (LSTM)-based approach, where at each step the sensors predict future feature values based on a learned model and on previously observed feature values. Next, each sensor computes the prediction error and passes it on to the actual receiver, which makes a decision on the signal authenticity through a generalized likelihood ratio test (GLRT). We model different classes of attacks and test them using simulation data obtained via the Bellhop ray tracing software. Numerical results show that our authentication mechanism successfully distinguishes between legitimate and impersonating transmitters, even when considering challenging attacking scenarios where the attacker can successfully mimic the channels between the legitimate transmitter and the sensors.

Model-Driven Learning for Physical Layer Authentication in Dynamic Environments

Model-Driven Learning for Physical Layer Authentication in Dynamic Environments

Improving Transferability and Immunity of Physical Layer Authentication by the Channel Time-Varying Pattern

Improving Transferability and Immunity of Physical Layer Authentication by the Channel Time-Varying Pattern

Artificial intelligence empowered physical layer security for 6G: State-of-the-art, challenges, and opportunities

With the commercial deployment of the 5G system, researchers from both academia and industry are moving attention to the blueprint of the 6G system. The space–air–ground–sea integrated 6G system is envisioned to support various new applications with very diverse requirements in terms of quality of service and security. Due to the open nature of the wireless channel, highly dynamic network topology, and mission-critical applications, 6G will face various security threats. The conventional upper-layer cryptography-based security mechanisms face multiple challenges to resist physical layer attacks in the context of large numbers computing, power-limited low-cost Internet of Thing (IoT) devices. The Physical layer security (PLS) mechanisms by exploiting the random nature of the wireless channel and intrinsic hardware imperfection provide complementary approaches to secure the 6G system. Meanwhile, the quick development and successful application of artificial intelligence facilitate the advancement of PLS solutions. In this paper, we make a comprehensive overview of machine learning-empowered PLS techniques toward 6G. We first introduce the vision of the 6G system, typical applications, key enabling radio technologies, security threats, and security requirements to 6G. Then typical machine learning (ML) methods and the driving forces for introducing intelligent PLS in 6G are presented. Afterward, the major academic works on PLS oriented to key 6G radio techniques including cell-free massive MIMO, visible light communication, Terahertz technology, configurable intelligent surfaces, molecular communication, and ambient backscatter communication are discussed. Then, the latest works on ML-enabled PLS solutions including physical layer key generation, secure wireless transmission, physical layer authentication, intelligent anti-jamming, and ML-enabled attack and detection are systematically reviewed. Finally, we identify important open issues and future research opportunities to inspire further studies to realize a more intelligent and secure 6G system.

Multiobservation-Multichannel-Attribute-Based Multiuser Authentication for Industrial Wireless Edge Networks

In order to truly promote the further development of the Industrial Internet of Things (IIoT), terminal authentication of the IIoT is essential. Physical-Layer Authentication (PLA) has recently attracted much attention for its high security and lightweight. Nevertheless, most existing PLA schemes in conjunction only the observation of a single receiver will lead to low-reliability and low-robustness of authentication, especially in hostile time-varying wireless channels. To tackle this issue, we developed a multi-observation-multi-channel-attribute (MOMCA)-based multiuser authentication architecture, which considers both the observations of multi-receivers and multiple channel attributes of each observation to enhance wireless security. Specifically, the proposed architecture can provide additional spatial recognition characteristics for multi-users. To better fit the channel features of multi-observations, we proposed two Gradient Boosting Optimization (GBO)-based schemes. One uses Taylor expansion to approximate objective functions and adds the regularization term to avoid over-fitting issues. The other can obtain higher authentication performance by sampling the signal data with small gradient characteristics. The simulations on real industrial indoor and outdoor datasets verify the superiority of the proposed schemes in authentication accuracy over six baseline authentication schemes

Generative Adversarial Network-Based Data Augmentation for Enhancing Wireless Physical Layer Authentication.

Wireless physical layer authentication has emerged as a promising approach to wireless security. The topic of wireless node classification and recognition has experienced significant advancements due to the rapid development of deep learning techniques. The potential of using deep learning to address wireless security issues should not be overlooked due to its considerable capabilities. Nevertheless, the utilization of this approach in the classification of wireless nodes is impeded by the lack of available datasets. In this study, we provide two models based on a data-driven approach. First, we used generative adversarial networks to design an automated model for data augmentation. Second, we applied a convolutional neural network to classify wireless nodes for a wireless physical layer authentication model. To verify the effectiveness of the proposed model, we assessed our results using an original dataset as a baseline and a generated synthetic dataset. The findings indicate an improvement of approximately 19% in classification accuracy rate.

Physical Layer Authentication and Security Design in the Machine Learning Era

Physical Layer Authentication and Security Design in the Machine Learning Era