Layer Authentication Research Articles

Secure authentication and encryption via diffraction imaging-based encoding and vector decomposition

Abstract Traditional optical encryption systems have security risks due to their linearity and usually encounter problems such as the heavy burden of key transmission and storage. This paper proposes a novel security-enhanced optical image authentication and encryption framework that combines diffractive imaging-based encryption with the vector decomposition algorithm (VDA). Chaotic random phase masks (CRPMs) are used to encrypt data for authentication via VDA, and a pair of complementary binary matrix keys are utilized to extract information from the encrypted data to generate ciphertext. During the authentication and decryption processes, a sparse reference image is reconstructed from the ciphertext for verification. If the authentication is successful, image decryption can be executed using a key-assisted phase retrieval algorithm. The employment of nonlinear VDA, an additional layer of authentication, and the use of CRPMs and binary matrix keys enhance security and address key burden concerns. Simulation results demonstrate the feasibility, effectiveness, and security of the scheme.

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

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.

Beyond passwords: A multi‐factor authentication approach for robust digital security

AbstractMulti‐Factor Authentication (MFA) strengthens digital security by necessitating users to verify their identity. It uses various authentication methods like adding an extra layer of protection beyond conventional passwords. Proposed method introduces a novel MFA system that integrates multiple authentication layers, starting with two phase Graphical password with the traditional email‐password and progressing to facial recognition using Convolutional Neural Networks (CNN) and Quick response (QR) code authentication. To prove the robustness of our method, we are considering some test cases and few performance metrics like delay, accuracy, etc. The results are derived for False positive rates, complexity. The success rate is observed to be more than 93% for the proposed model.

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

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.

Session password Blum–Goldwasser cryptography based user three layer authentication for secured financial transaction

Cloud computing is an eminent and evolving technology that offers various services such as data transaction, authentication, distribution and so on. In this article, session password Blum–Goldwasser Cryptography based user authentication (SPBGCUA) method is proposed to enhance the communication security of cloud services with minimum overhead. Moreover, SPBGCUA is a cryptographic technique that provides secured authentication mechanism for financial transaction with higher confidentiality rate. In order to fill the security lack of recent authentication techniques, this method develops dynamic security structure with key generation, encryption, authentication and decryption techniques. Finally, analysis is performed using SPBGCUA Method on factors such as authentication accuracy, data confidentiality rate and data integrity for number of financial data and cloud users.

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.

BEC Defender: QR Code-Based Methodology for Prevention of Business Email Compromise (BEC) Attacks.

In an era of ever-evolving and increasingly sophisticated cyber threats, protecting sensitive information from cyberattacks such as business email compromise (BEC) attacks has become a top priority for individuals and enterprises. Existing methods used to counteract the risks linked to BEC attacks frequently prove ineffective because of the continuous development and evolution of these malicious schemes. This research introduces a novel methodology for safeguarding against BEC attacks called the BEC Defender. The methodology implemented in this paper augments the authentication mechanisms within business emails by employing a multi-layered validation process, which includes a MAC address as an identity token, QR code generation, and the integration of timestamps as unique identifiers. The BEC-Defender algorithm was implemented and evaluated in a laboratory environment, exhibiting promising results against BEC attacks by adding an extra layer of authentication.

Multifactor Authentication System

The multifactor authentication system is a state- of-the-art solution created to improve security protocols across numerous sectors and shield confidential data from unauthorized access. Several layers of authentication are provided by this system, which makes use of cutting-edge technologies, ensuring that only people with the proper authorization can access particular resources or systems. It integrates biometric authentication, including fingerprint, facial, or iris scanning, which ups security because these biological characteristics are personal to each person. This system also uses multi-factor authentication, which combines two or more authentication factors, such as something the user is (a smart card), something they have (a password), or something they know (a password) (biometrics). This combination lessens the possibility of a single point of failure, hence enhancing security. Real-time monitoring, risk-based authentication, and behavioral biometrics are further aspects of the advanced authentication system that help to identify and stop fraudulent actions. Organizations can dramatically reduce security risks, safeguard sensitive data, and adhere to strict regulatory requirements by deploying this solution. The advancedauthentication system, in its whole, is a potent tool in the modern digital environment, protecting important assets with its strong and complex security measures. Keywords: advanced authentication system, security, biometric authentication, multi-factor authentication, real-time monitoring, risk- based authentication,

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.

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.

A Survey on Cross-Layer Authentication in Wireless Communication Networks

In current wireless networks, Physical Layer Authentication (PLA) and cryptography-based authentication have been recognized as the two dominant methods that can be trusted for user authentication and device identification. PLA methods are intensively studied in the field of network security as a strong complement to upper layer authentication. However, using only two existing authentication mechanisms cannot meet the security requirements and service demands of future wireless network development. Cross-layer authentication was created in response to the future explosion of Internet of Things (IoT) devices for secure authentication and broader service requirements. This paper presents a survey on current research status of cross-layer authentication schemes in wireless networks. We divide current cross-layer authentication approaches into two categories: cross-layer device authentication and cross-layer user-device authentication, introduce the state-of-the-art works in each category and finally discuss the challenges and future research directions.

Distributed Physical Layer Authentication: Overview and Opportunities

Distributed physical layer authentication (DPLA) is a novel authentication framework, which not only exploits the collaborative computing of multiple devices to enhance overall efficiency, but also alleviates the degradation of processing performance caused by resource-constrained terminals. It is considered a promising architecture for solving access security issues in future communications. Considering DPLA's potential, in this article, we review existing DPLA schemes to provide a comprehensive summary of the strategies and technical approaches adopted during each implementation stage. Our simulation results show that the voting-assisted DPLA scheme has better authentication performance than the centralized PLA. In addition, we also present some open research issues on DPLA, addressing new opportunities ahead and potential research directions.