Authentication Technology Research Articles

Blockchain‐Powered Framework for Trust Enhancement in FinTech: A Comprehensive Trust Evaluation Approach

ABSTRACTThe rapid advancement of financial technology (FinTech) has led to the integration of advanced technologies like data science, blockchain, cloud computing, and artificial intelligence. However, trust evaluation remains a critical challenge in dynamic landscape. Existing trust evaluation methods often neglect key aspects of timeliness, reliability, and non‐invasiveness, leading to imprecise trust assessments and insufficient detection of malicious user behavior. This paper introduces a robust four‐layer architectural framework with the blockchain layer, edge computing service layer, cloud computing service layer, and terminal user application layer leveraging blockchain technology for authentication and trust evaluation. Blockchain technology transforms FinTech data into linked data, ensuring data security and decentralization during information transfers. A novel hybrid consensus protocol combining Proof of Elapsed Time (PoET) and Proof of Stake (PoS) is introduced to enhance the efficiency and security of the blockchain. Extensive simulation experiments have demonstrated significant improvements in data security, reliability, and accuracy of trust assessments compared to existing methods. This paper presents a comprehensive solution for enhancing trust evaluation in FinTech, emphasizing timeliness, reliability, and non‐invasiveness of assessments.

Real‐Time Communication System Using Canonical Cortical Graph Neural Network With High‐Level Target Navigation Pigeon‐Inspired Optimization

ABSTRACTRadio‐frequency (RF) fingerprinting is an emerging technology for advanced device authentication. This work addresses the issue by improving both the algorithm and hardware to target real‐time AI processing in communication systems. Here, efficient real‐time communication system using canonical cortical graph neural network with high‐level target navigation pigeon‐inspired optimization (RCS‐CCGNN‐HTNPIO) is proposed. Initially, radio frequencies from multiple devices such as modem, TV, and mobile phone, these devices transmit RF signals, which are captured and processed via RF fingerprinting. Then, RF fingerprinting system is designed to distinguish between known signals and unknown signals. Afterwards, the canonical cortical graph neural network (CCGNN) for analyzing and identifying the specific RF fingerprint of each signal. The CCGNN evaluates the signals and categorizes them as known signal or unknown signal based on the RF fingerprint characteristics. Hence, the high‐level target navigation pigeon‐inspired optimization (HTNPIO) is used to optimize the CCGNN. The performance metrics provide a complete assessment of the system's ability to manage the demands of real‐time processing in communication networks. The RCS‐CCGNN‐HTNPIO approach attains19.21%, 26.12%, and 30.15% higher accuracy compared with existing techniques likes deep learning based multidimensional radio‐frequency fingerprinting enhancement approach for IoT device identification (DL‐RFF‐DI), embedding‐assisted attentional deep learning for real‐world RF fingerprinting of Bluetooth (ADL‐RW‐RFF), and multichannel attentive feature fusion for radio‐frequency fingerprinting (MCA‐RFF). The simulation results prove that the RCS‐CCGNN‐HTNPIO can provide a robust and adaptive solution for achieving high accuracy in next‐generation radio access networks.

Civis ID with True Face: A Biometric Voting System for Secure and Transparent Elections

The "Civis ID with True Face" project presents an innovative approach to modernizing the electoral process by integrating biometric facial recognition technology for voter authentication. This system addresses critical challenges in traditional voting methods, such as voter fraud, impersonation, and inefficiencies caused by manual verification processes. By leveraging machine learning algorithms and advanced AI models, it ensures that only eligible voters can cast their votes, thus enhancing the security and integrity of elections. The project also prioritizes user accessibility, offering a seamless and contactless voting experience that reduces wait times and improves inclusivity for voters with disabilities or limited mobility. Key features of the system include real-time facial recognition for authentication, secure data encryption for voter privacy, and automated result tabulation for efficiency and accuracy. Designed to comply with international data protection standards, "Civis ID with True Face" not only eliminates traditional vulnerabilities in voting systems but also ensures transparency through secure and auditable digital records. This project represents a significant step toward embracing technology to enhance the trustworthiness, efficiency, and inclusivity of democratic processes worldwide.

Wearable Sensor-Based Behavioral User Authentication Using a Hybrid Deep Learning Approach with Squeeze-and-Excitation Mechanism

Behavior-based user authentication has arisen as a viable method for strengthening cybersecurity in an age of pervasive wearable and mobile technologies. This research introduces an innovative approach for ongoing user authentication via behavioral biometrics obtained from wearable sensors. We present a hybrid deep learning network called SE-DeepConvNet, which integrates a squeeze-and-excitation (SE) method to proficiently simulate and authenticate user behavior characteristics. Our methodology utilizes data collected by wearable sensors, such as accelerometers, gyroscopes, and magnetometers, to obtain a thorough behavioral appearance. The suggested network design integrates convolutional neural networks for spatial feature extraction, while the SE blocks improve feature identification by flexibly recalibrating channel-wise feature responses. Experiments performed on two datasets, HMOG and USC-HAD, indicate the efficacy of our technique across different tasks. In the HMOG dataset, SE-DeepConvNet attains a minimal equal error rate (EER) of 0.38% and a maximum accuracy of 99.78% for the Read_Walk activity. Our model presents outstanding authentication (0% EER, 100% accuracy) for various walking activities in the USC-HAD dataset, encompassing intricate situations such as ascending and descending stairs. These findings markedly exceed existing deep learning techniques, demonstrating the promise of our technology for secure and inconspicuous continuous authentication in wearable devices. The suggested approach demonstrates the potential for use in individual device security, access management, and ongoing uniqueness verification in sensitive settings.

A Novel Technique for Handwritten Signature Recognition

Handwritten signature recognition (HSR) is a critical component of biometric systems, widely used for securing financial transactions and identity verification. However, the variability of handwritten signatures, influenced by individual writing styles, inconsistencies, and environmental factors, presents significant challenges for recognition systems. Despite these obstacles, signatures remain a reliable and popular biometric trait. This paper introduces a novel deep learning approach utilizing a convolutional neural network (CNN) architecture specifically designed for HSR. The proposed method was validated using two prominent datasets, MCYT-75 and GPDS-300, with detailed descriptions of the CNN structure. Experiments, conducted on a personal computer equipped with an NVIDIA Quadro M1200 GPU, an Intel i7 processor, and 32 GB of RAM, demonstrated the model’s exceptional performance, achieving validation accuracies of 99.60% on the MCYT-75 dataset and 99.80% on the GPDS-300 dataset. These results reflect the model’s robustness and minimized error rates, outperforming existing techniques and underscoring the effectiveness of deep learning for signature recognition. This study highlights the proposed model's potential for real-world applications and paves the way for further advancements in biometric authentication technologies.

Intelligent Fingerprint Recognition System Using Machine Learning

Secure authentication relies heavily on fingerprint recognition systems; however, conventional techniques are hindered by differences in image quality and possible security breaches. This study suggests a novel method for improving fingerprint recognition accuracy and robustness that makes use of machine learning techniques. By utilizing convolutional neural networks' (CNNs') capacity to deduce intricate patterns and representations from fingerprint photos, we investigate the use of CNNs for feature extraction and matching. Comparing our experimental results against conventional approaches, we find considerable improvements in accuracy and false acceptance rates on benchmark datasets. By advancing biometric authentication technologies, this research helps to improve security and dependability for a range of applications in law enforcement, access control, and personal identification.

A novel method for generating public keys involving matrix operations.

With the rise of the Internet of things, the limitations of traditional PKI certificate authentication technology have progressively emerged. Hence, identity-based public key algorithms have been proposed. In this paper, we propose a new approach to generate an identity key, named identity public key (IPK). IPK identity key generation protocol is based on SM2 elliptic curve cryptography and random matrix theory. It builds upon the concept of combined public key (CPK) and resolves the linear collusion issue of CPK as well as the authenticity verification problem of the declared public key of the simplified TF-CPK by enhancing the identity mapping approach. Another main aim of this paper is to prove the security of the IPK identity key generation protocol. Roughly speaking, we verify the security of each part of the private key and the security of the composite private key. We also increase the function and performance comparison with other schemes, such as IBC(SM9) and TF-CPK. Our scheme has the lowest computation cost, which demonstrates its rationality.

Application of Photo-Induced Chirality in Covert Authentication

A new technology to write and read covert information in authentication labels is described. This technology uses the phenomenon of photo-induced chirality in Ge2Sb2Te5 thin films to encode the left- or right-circular or linear polarization of the laser beam used to write the label. The written polarization can be revealed by a simple reading device, which is demonstrated to provide the same qualitative information as reading based on cyclotron circular dichroism spectroscopy and imaging. The suggested method, while based on existing manufacturing approaches, offers a balance between technological complexity for writing and simplicity for reading, and may be advantageous as a new authentication technology.

Biometric and Geographic Information-based Identity Authentication Method

With the ongoing advancements in computer technology and mobile internet, mobile payment has become an integral component of daily life. Identity authentication technology serves as a critical measure to ensure the security of mobile payments. This paper addresses the issue of low security associated with single-factor identity authentication while highlighting the substantial overhead involved in multi-factor authentication methods. We propose a novel identity authentication method that integrates biometric data and geographic information. In this framework, the identity authentication server first performs system initialization, configures system parameters, and stores them securely. Subsequently, the identity authentication client submits an authentication request to the server and completes its registration process. Finally, the server processes this request from the client, conducts user identity verification, and returns an authentication result. This approach effectively mitigates risks posed by attackers attempting to falsify biometric data or geographic information during the identity verification process and offers robust services for biometric- and geography-based identity authentication.

AffectiVR: A Database for Periocular Identification and Valence and Arousal Evaluation in Virtual Reality

This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This study proposes a method for user authentication by utilizing periocular images captured by a camera attached to a VR headset. Existing datasets have lacked periocular images acquired in VR environments, limiting their practical application. To address this, periocular images were collected from 100 participants using the HTC Vive Pro and Pupil Labs infrared cameras in a VR environment. Participants also watched seven emotion-inducing videos, and emotional evaluations for each video were conducted. The final dataset comprises 1988 monocular videos and corresponding self-assessment manikin (SAM) evaluations for each experimental video. This study also presents a baseline study to evaluate the performance of biometric authentication using the collected dataset. A deep learning model was used to analyze the performance of biometric authentication based on periocular data collected in a VR environment, confirming the potential for implicit and continuous authentication. The high-resolution periocular images collected in this study provide valuable data not only for user authentication but also for emotion evaluation research. The dataset developed in this study can be used to enhance user immersion in VR environments and as a foundational resource for advancing emotion recognition and authentication technologies in fields such as education, therapy, and entertainment. This dataset offers new research opportunities for non-invasive continuous authentication and emotion recognition in VR environments, and it is expected to significantly contribute to the future development of related technologies.

Application of stealth nanobeacon for traceability assurance in pharmaceutical tablets via surface-enhanced Raman scattering during the tablet coating process

Microtaggant technologies for on-dose authentication have garnered significant interest for use in the anti-counterfeit activities and traceability of pharmaceutical dosage forms. Previously, we proposed a stealth nanobeacon (NB) comprising self-assembled colloidal gold nanoparticles with reporter molecules that demonstrated characteristic surface-enhanced Raman scattering (SERS) activity. However, the integration of such microtaggants into standard production lines remains underexplored. In this study, we demonstrate the incorporation of NB into tablet coatings using a simple mixing method with conventional coating solutions. Rapid and discernible SERS responses from the NB-coated tablets were observed in response to laser excitation at 785 nm for 0.1s, implying that it is an advanced and efficient method for counterfeit detection. In addition, the SERS intensity of NB increased with coating time, suggesting that NB can be used as a tracer for the real-time monitoring of coating thickness. Furthermore, NB-coated tablets were indistinguishable from NB-free tablets, even during colorimetric analysis. These results suggest that the NB possesses stealth properties and can be easily incorporated into counterfeit detection products.

HDN-Net: A Hybrid Deep Neural Network to Improve Iris Recognition in Unconstrained Environments with Eyeglasses

Recently, the use of iris recognition technology for biometric authentication has gained widespread acceptance due to the rich texture of the iris region, which provides a reliable standard for recognising individuals, as well as the non-intrusive nature of this method. However, the presence of eyeglasses poses a significant challenge to the accuracy of such systems. In unrestricted environments, current iris recognition techniques cannot effectively extract distinguishing features of the iris. Eyeglasses introduce scratches, specular reflections, dirt, blurriness, and other noise factors over the image of the iris, resulting in low recognition accuracy. To tackle this challenge, researchers have proposed the HDN-Net architecture. This architecture employs a multi-CNN model to combine the features of both the right and left iris images, extracting more distinguishing features to improve the accuracy of the classification task in the presence of challenges caused by eyeglasses. Experiment results show that the proposed iris recognition system achieves more promising performance compared to previous methods used in this field. The overall performance of our suggested HDN-Net method on the UBIRIS.V2 and CASIA-Iris.V4-1000 databases achieved 97.89% and 98.79% accuracy, respectively. Thus, the proposed HDN-Net method consistently outperforms other traditional and deep learning approaches and has the possibility to improve the accuracy of iris recognition systems (IRS) in real-world scenarios.

Design and Implementation of USB Key System Based on Dual-Factor Identity Authentication Protocol

With the increasing demand for information security, traditional single-factor authentication technology can no longer meet security requirements. To this end, this paper proposes a Universal Serial Bus (USB) Key hardware and software system based on a two-factor authentication protocol, aiming to improve the security and reliability of authentication. This paper first analyzes the current status and technical principles of USB Key-related research domestically and internationally and designs a two-factor authentication protocol that combines impact/response authentication and static password authentication. The system consists of a host computer and a USB Key device. The host computer interacts with the USB Key through a graphical user interface. The Secure Hash Algorithm 1 (SHA-1) and MySQL database are used to implement the authentication function. Experimental results show that the designed two-factor authentication protocol can effectively prevent replay attacks and information tampering, and improve the security of authentication. If the corresponding USB Key is not inserted, the system will prompt that the device is not found. Once the USB Key is inserted, user identity is confirmed through two-factor verification, which includes impact/response authentication and static password authentication.

The Situation of Counterfeited and Mislabeled Commercialized Edible Mushrooms in China and the Development of Possible Controls

Edible mushroom products, encompassing both cultivated and wild varieties, are highly favored by consumers due to their rich nutritional profiles, including significant levels of proteins and amino acids. These mushrooms have extensive applications across the food, pharmaceutical, and cosmetic industries, making the edible mushroom industry a vital component of global poverty alleviation efforts. Taking China as an example, the country produces over 45 million tons of edible mushrooms annually, accounting for 94.01% of the world’s total production, thereby establishing itself as the leading global producer of edible mushrooms. However, alongside the rapid expansion of this industry, concerns have emerged regarding counterfeit products and incidents of poisoning resulting from the consumption of toxic wild mushrooms. As follows, to advance the development and integrity of the mushroom production and processing industry: (1) This study presents the situation of counterfeit edible mushrooms and elucidates the factors contributing to the production of fraudulent products from both subjective and non-subjective perspectives. (2) We provide a detailed introduction to 22 varieties of freshly cultivated edible mushrooms and commonly encountered wild edible mushrooms in the Chinese consumer market, proposing the application of DNA barcoding, environmental DNA analysis, and other technologies for the future authentication of counterfeit mushroom products. (3) Concurrently, we present an overview of mushroom poisoning incidents in China from 2010 to 2023, emphasizing the challenges in mitigating the risks associated with wild mushroom consumption and preventing food poisoning, thereby necessitating heightened consumer caution. (4) Finally, we offer four recommendations aimed at ensuring the healthy, stable, and sustainable growth of the edible mushroom industry.

Enhancing Facial Identification Systems with YOLOv8: A Cutting-Edge Approach

Face recognition and identification have recently become the most widely employed biometric authentication technologies, especially for access to persons and other security purposes. It represents one of the most significant pattern recognition technologies that uses characteristics included in facial images or videos to detect the identity of individuals. However, most of the traditional facial algorithms have faced limitations in identification and verification accuracy. As a result, this paper presents a sophisticated system for face identification adopting a novel algorithm of deep learning, namely, You Only Look Once version 8 (YOLOv8). This system can detect the face identity of different individuals with different positions with high accuracy. The YOLOv8 model has been trained for several target face images classified as training and validation images of 1190 and 255, respectively. The experimental results show a significant improvement in face identification accuracy of 99% of mean average precision, which outperforms many state-of-the-art face identification techniques.

Gas Sensor Physically Unclonable Function-Based Lightweight Bidirectional Authentication Protocol for Gas Sensor Networks

In gas sensor networks, users can access the data collected by the sensor nodes, but there is a risk of data leakage during transmission. This paper proposes a lightweight bidirectional authentication protocol based on gas sensor physically unclonable functions (GS-PUFs) with authentication technology to guarantee the reliability of data from sensor nodes. A sensor PUF array is constructed by preparing gas sensors to enhance the data security of the physical layer and reduce hardware resource consumption. The authentication part of the protocol mainly uses lightweight encryption methods, consisting of PUF data, one-way cryptographic hash functions, and iso-or functions, to reduce the computational overhead of the authentication process. The protocol security is enhanced by encrypting the GS-PUF response as an irreversible hash value and verifying the hash value by the user, server, and sensor node to complete bidirectional authentication. The test results demonstrate that the protocol, verified through the ProVerif formal tool, can resist impersonation, replay, node tampering, and cloning attacks. Among the compared schemes, this protocol offers the highest security and the least resource overhead, making it effectively applicable in the Internet of Things and other fields.

Smartphone video imaging: A versatile, low-cost technology for food authentication

This study presents a low-cost smartphone-based imaging technique called smartphone video imaging (SVI) to capture short videos of samples that are illuminated by a colour-changing screen. Assisted by artificial intelligence, the study develops new capabilities to make SVI a versatile imaging technique such as the hyperspectral imaging (HSI). SVI enables classification of samples with heterogeneous contents, spatial representation of analyte contents and reconstruction of hyperspectral images from videos. When integrated with a residual neural network, SVI outperforms traditional computer vision methods for ginseng classification. Moreover, the technique effectively maps the spatial distribution of saffron purity in powder mixtures with predictive performance that is comparable to that of HSI. In addition, SVI combined with the U-Net deep learning module can produce high-quality images that closely resemble the target images acquired by HSI. These results suggest that SVI can serve as a consumer-oriented solution for food authentication.

Emerging Authentication Technologies for Zero Trust on the Internet of Things

The large and interconnected nature of the Internet of Things (IoT) presents unique security challenges, even as it revolutionizes various sectors. With numerous devices, often limited in resources, traditional perimeter-based security methods struggle to keep pace. The “never trust, always verify” principle of zero trust security offers a viable solution. Zero trust security is a concept that has become increasingly popular, using key exchange techniques to ensure secure and authenticated communication within the network, especially in managing risks in critical infrastructure. Authentication is a process to identify an entity, a prerequisite for authorization, and essential for granting access control. It fundamentally relies on trust management and various methods to generate and manage cryptographic keys for authentication. The aim of this study is to enhance zero trust security in the context of the Internet of Things by investigating authentication methods and discussing several potential solutions for successful implementation. This study also presents the performance evaluation criteria for authentication in IoT and introduces advanced approaches for different scenarios, including lightweight cryptography, mutual authentication, and blockchain technology. Finally, we address challenges related to implementation and future directions for research.

A blockchain-assisted lightweight UAV network authentication mechanism via covert communication

The increasing importance of terminal privacy in the Unmanned Aerial Vehicle (UAV) network has led to a growing recognition of the crucial role of authentication technology in UAV network security. However, traditional authentication approaches are vulnerable due to the transmission of identity information between UAVs and cryptographic paradigm management centers over a public channel. These vulnerabilities include brute-force attacks, single point of failure, and information leakage. Blockchain, as a decentralized distributed ledger with blockchain storage, tamper-proof, secure, and trustworthy features, can solve problems such as single-point-of-failure and trust issues, while the hidden communication in the physical layer can effectively resist information leakage and violent attacks. In this paper, we propose a lightweight UAV network authentication mechanism that leverages blockchain and covert communication, where the identity information is transmitted as covert tags carried by normal modulated signals. In addition, a weight-based Practical Byzantine Fault-Tolerant (wPBFT) consensus protocol is devised, where the weights are determined by the channel states of UAVs and the outcomes of past authentication scenarios. Simulation results demonstrate that the proposed mechanism outperforms traditional benchmarks in terms of security and robustness, particularly under conditions of low Signal-to-Noise Ratio (SNR) and short tag length.

Analysis of problems and countermeasures in computer network engineering security

This paper analyzes the existing security issues in computer network engineering and proposes a series of countermeasures. To address the problems of hacker attacks and information leakage, it is recommended to strengthen network monitoring and the application of encryption technology. For the issues of malware and virus dissemination, it is advised to regularly update antivirus software and enhance employee security awareness training. Regarding network data leakage and privacy breaches, the use of secure transmission protocols and multi-factor authentication technology is advocated. For network security management and maintenance issues, establishing a sound security management system and emergency response plans is recommended to ensure the stable operation of network systems. Through the implementation of the above measures, the security level of computer network engineering can be effectively improved, ensuring the normal operation of network systems and the security of information.