Authentication Performance Research Articles

The evolution of performance style in the history of the International Fryderyk Chopin Piano Competition in Warsaw

The International Fryderyk Chopin Piano Competition in Warsaw was inaugurated in 1927, in part out of the desire to promote an authentic performance practice in relation to Chopin’s oeuvre. Among the main concerns were the establishment of, and strict adherence to, the original authoritative text and an authentic performance aesthetic. The debate over those issues has been at the heart of the artistic mission of the Competition ever since. As the Chopin Competition indeed exercised immense influence on the performance style of Chopin’s music during the twentieth century – an influence that has continued into the twenty-first century – this paper examines the evolution of that style through Competition performances. Has the performance style evolved at all? If so, to what extent? Starting with the 5th Competition, in 1955, the year in which the Competition audio archives were started, the author discusses selected performances by most of the winners of the First Prize. Also discussed are performances by some of the pianists who did not win but whose contributions were important in terms of their influence on Chopin performance practice. Besides addressing the question of whether the evolution of Chopin performance style is encouraged, discouraged or even possible at a Competition whose main goals include establishing and preserving an authentic performance practice, the paper questions whether that goal can ever be achieved. Also discussed is the place of the Competition in relation to the changes in Chopin performance style in the piano world outside the Warsaw Competition. In the introduction, the author briefly considers some of the characteristics and challenges of the unique style of Chopin as understood through most recent achievements in musicology, cultural studies, editing, research, performance, piano pedagogy and music criticism. He also describes the main principles of the so-called ‘Polish School’ of Chopin interpretation and its impact on the development of what is currently the principal way of performing Chopin. The article is supplemented by an online playlist comprising audio excerpts of some of the discussed performances.

Trusted Access Authentication Technology for Large-Scale Heterogeneous Terminals in a Vehicle Charging Network System

A vehicle charging network system has to access large-scale heterogeneous terminals to collect charging pile status information, which may also give malicious terminals an opportunity to access. Though some general access authentication solutions aimed at only allowing trusted terminals have been proposed, they are difficult to work with in a vehicle charging network system. First, among various heterogeneous terminals with significant differences in computing capabilities, there are inevitably terminals that cannot support computations required for cryptography-based access authentication schemes. Second, though access authentication schemes based on device fingerprints are independent of terminal computing capabilities, their authentication performance is weak in robustness and high in overhead. Third, the access authentication delay is huge since the system cannot withstand heavy concurrent access requests from large-scale terminals. To address the above problems, we propose a reliable and lightweight trusted access authentication solution for terminals in the vehicle charging network system. By cloud, edge, and local servers cooperating to execute authentication tasks, our Cloud-Edge-End Collaborative architecture effectively alleviates the authentication delay caused by high concurrent requests. Each server in the architecture deploys our well-designed unified trusted access authentication (UATT) model based on device fingerprints. With ingenious data construction and the powerful swin-transformer network, the UATT model can provide robust and low-overhead authentication services for heterogeneous terminals. To minimize authentication latency, we further design an A2C-based authentication task scheduling scheme to decide which server executes the current task. Comprehensive experiments demonstrate our solution can authenticate terminals with an accuracy higher than 98% while reducing the required data packets by two orders of magnitude, and it can effectively reduce authentication latency.

Physical-Layer Authentication for Ambient Backscatter-Aided NOMA Symbiotic Systems

Ambient backscatter communication (AmBC) and non-orthogonal multiple access (NOMA) are two promising technologies for the future wireless communication networks owing to their high energy and spectral efficiencies. The AmBC-aided NOMA symbiotic radio is a promising technology because of possessing advantages of AmBC and NOMA. Nonetheless, when a number of devices with limited power and computation capability access to the AmBC-based NOMA symbiotic networks, communication security becomes a critical issue. In this paper, we investigate physical-layer authentication (PLA) to identify the users and prevent illegal access and malicious activities for AmBC-based NOMA symbiotic networks. Moreover, channel estimation errors are considered when calculating the probability of false alarm (PFA) and probability of detection (PD) of the far user and near user. To enhance the authentication performance, three PLA schemes for the considered networks are designed according to the multiplexing form of the authentication tags: i) PLA with shared authentication tag (PLA-SAT); ii) PLA with space division multiplexing authentication tags; iii) PLA with time-division multiplexing authentication tags. To characterize the proposed PLA schemes, we first derive the PFA and the PD of the considered AmBC-based NOMA symbiotic networks. Then, the covertness is studied in terms of outage probability and asymptotic behavior in the high signal-to-noise ratio regime. Extensive analytical and computer simulated results show that: i) The PLA-SAT scheme has better performance than the other two authentication schemes with the same threshold; ii) The outage performance of systems employing authentication schemes is worse than those without authentication; iii) There exists a trade-off between robustness and covertness.

Palmprint authentication using symbolic aggregate approximation features with improved enhancement techniques

The palmprint enhancement is a pre-processing stage of palmprint authentication. The accuracy of the recognition rate can be improved by incorporating effective palmprint enhancement methods. So far, only a little work has been done in palmprint enhancement. Little attention is given to incorporate the enhancement techniques in palmprint authentication system to achieve performance improvement. This work presents robust enhancement methods which provide better performance and accuracy. Palmprint enhancement using curvelet and Recursive Histogram Equalization (RHE) overcomes the drawbacks of the existing systems and makes palmprint recognition simpler and more accurate with enhanced palm image as input. Palmprint recognition system uses the Symbolic Aggregate Approximation (SAX) features from the enhanced palmprint image. The recognition rate is optimum when both curvelet and RHE methods are used for enhancement of palmprint. Here we compared different types of enhancement methods and we have obtained maximum recognition rate with a combination of recursive histogram equalization and curvelet transform. The performance of the palmprint authentication is measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Total Success Rate (TSR). The comparison of recognition rate with enhancement is done using minimum distance classifier, Support Vector Machine, Random Forest and Bayesian. Random Forest classifier provides better results.

Transfer learning convolutional neural network with modified Lion optimization for multimodal biometric system

Recently, biometric verification systems have been widely used in security applications with multi-level authentication. Existing machine learning approaches were utilized to develop traditional multimodal biometric systems (MBS). However, they fail to guarantee optimal secrecy, security, and accuracy. Thus, this work adopted the transfer learning convolutional neural network (TL-CNN) approach for implementing the eight-class hybrid MBS. The multi-level security biometric verification systems are achieved by considering eight different types of biometric datasets, which are the retina, faces, ears, palm print, fingerprint, voice, gait, and DNA-based biometric data. Initially, the noise from these datasets is eliminated by using Multi-Kernel-Multi-Patch Bilateral Filtering (MK-MP-BF), which also enhances the regions of images. Further, a deep convolutional residual network (DCRN) is used to extract pattern-specific features from the pre-processed data, which also identifies the relationship between various features. Then, a bio-optimization-based modified Lion optimization algorithm (MLOA) is used to select the optimal feature, which also identifies the inter- and intra-dependencies among various biometrics. Finally, a TL-CNN-based GoogleNet classifier was utilized for recognizing the biometrics, which also performed the multi-class classification operation. From the simulations, it is proven that the proposed MLOA optimized TL-CNN resulted in outstanding recognition and authentication performance in comparison with conventional methods.

Efficient secure data storage based on novel blockchain model over IoT-based smart computing systems

In recent years, edge-related smart computing is the way to process and store data via outsourcing environments. Security is the main progressive concept in smart-related Internet of Things (IoT) software denied networks to share and increase the scalability and efficiency in data storage. Because of the rapid growth of different smart services, different security-related problems may appear in resource processing and sharing data in real-time computing systems. To increase the reliability in secure data storage and satisfy the basic requirements related to IoT-related smart computing networks. So propose and implement a Novel Artificial Intelligence based Blockchain Secure Model (NAIBSM) to provide efficient secure data storage in IoT-related smart computing systems. This model flexibly captures each user authentication for the detection of different user-related attacks (i.e. distributed denial-of-service (DDOS)) in the storage of data via applying Artificial Intelligence (AI) calculation method i.e. Leverage Bat algorithm to explore complex features. Build a blockchain at the server side to provide secure communication and reliability of storing data on the terminal of IoT. This approach provides random hash values to each data to ensure blockchain for the integrity of data and uses a weight-based data storage procedure to arrange/store and classify data to each user with secure and unsecured storage in smart computing networks. The experimental results of the proposed model are to explore complex security features and ensure the performance of secure authentication to each user and better security, accuracy, and low communication overhead in IoT-related smart computing data storage and sharing systems.

Analysis of Mechanisms for Resource Management in IoT: Performance of Mutual Authentication in the Context of Fog and EDGE-Fog Computing

The Internet is now experiencing an unprecedented expansion with the development of connected objects. At its inception the IoT, like any revolution, its objectives were quite limited. Its main objective was to reduce the effort of human data entry in order to allow the storage and automatic processing of the latter. Moreover, objects generate a lot of data that they cannot store or process by themselves, hence the need for external support. This is how Cloud Computing comes to answer this problem, making the success of IoT. This technology guarantees the storage, availability, processing, etc. of data, its integration into the latter is known as "Cloud of Things" CoT. The data generated should not only be stored, but rather processed in order to derive knowledge, decision making. The objective of our work is to analyze mutual authentication management models of IoT devices to optimize resources trying to provide solutions in the face of resource availability. To carry out this work, we analyzed the application areas and the challenges of IoT. Then, we studied IoT security threats. Our work ends with a study and an analysis mechanism of mutual authentication management models of IoT based on uncertificated Authentication mechanisms, Elliptic curve cryptography authentication, Mutual Authentication in the context of Fog Computing.

Concept Retention among Senior High School Science, Technology, Engineering, and Mathematics (STEM) Students Exposed to a Strategic Intervention Material (SIM)

Using strategic intervention materials (SIM) in Science class is essential for achieving students' needed competencies, which they could have succeeded in regular classroom instruction. This study examines the retention of concepts among Grade 11 learners in Central Philippines after exposure to teacher-made Strategic Intervention Materials (SIMs). It utilized the Quasi-Experimental Research Design, Post-test Post-Post-Test, Experimental Group only design. To determin0e the significant difference in the level of proficiency right after and a semester after exposure to SIMs intervention Wilcoxon signed-rank test for paired samples was employed. Results showed that the SIMs helped students exceed the core knowledge, skills, and understanding requirements. Learning is best transferred through authentic performance tasks which made retention of Science concepts evident.0

Practical Consideration in using Pre-trained Convolutional Neural Network (CNN) for Finger Vein Biometric

Using a pre-trained Convolutional Neural Network (CNN) model for a practical biometric authentication system requires specific procedures for training and performance evaluation. There are two criteria for a practical biometric system studied in this paper. First, the system’s ability to handle identity theft or impersonation attacks. Second, the ability of the system to generate high authentication performance with minimal enrollment period. We propose the use of the Multiple Clip Contrast Limited Adaptive Histogram Equalization (MC-CLAHE) technique to process finger images before being trained by CNN. A pre-trained CNN model called AlexNet is used to extract features as well as classify the MC-CLAHE images. The authentication performance of the pre-trained AlexNet model has increased by a maximum of 30% when using this technique. To ensure that the pre-trained AlexNet model is evaluated based on its ability to prevent impersonation attacks, a procedure to generate the Receiver Operating Characteristics (ROC) curve is proposed. An offline procedure for training the pre-trained AlexNet model is also proposed in this paper. The purpose is to minimize the user enrollment period without compromising the authentication performance. In this paper, this procedure successfully reduces the enrollment time by up to 95% compared to using on-line training.

A flexible approach for biometric menagerie on user classification of keystroke data

Abstract Biometric systems aim to provide reliable authentication and verification of users. The behaviour of the users may alter the authentication performance when accessing these systems. Therefore, clustering users based on their actions is crucial. A biometric menagerie defines and labels user groups statistically according to their variability. However, determining groups is a fuzzy process and it may lead to inconsistencies. In this work, a novel and flexible approach is introduced based on the classification performance of the users data collected in a database without imposing any other restrictions. According to the performance measures obtained from the confusion matrix of the classification algorithms, users are ranked and then clustered. Additionally, the norm of a confusion matrix is offered augmenting the state-of-the-art performance metrics. The proposed scheme is evaluated using the behavioural biometrics modality on two benchmark keystroke databases. The performance results successfully illustrate the alternative way of grouping and identification of users sharing the same behaviour irrespective of the chosen classifiers or performance metrics.

Physical-Layer Authentication Based on Hierarchical Variational Autoencoder for Industrial Internet of Things

Recently, physical-layer authentication (PLA) has attracted much attention since it takes advantage of the channel randomness nature of transmission media to achieve communication confidentiality and authentication. In the complex environment, such as the Industrial Internet of Things (IIoT), machine learning (ML) is widely employed with PLA to extract and analyze complex channel characteristics for identity authentication. However, most PLA schemes for IIoT require attackers’ prior channel information, leading to severe performance degradation when the source of the received signals is unknown in the training stage. Thus, a channel impulse response (CIR)-based PLA scheme named “hierarchical variational autoencoder (HVAE)” for IIoT is proposed in this article, aiming at achieving high authentication performance without knowing attackers’ prior channel information even when trained on a few data in the complex environment. HVAE consists of an autoencoder (AE) module for CIR characteristics extraction and a variational AE (VAE) module for improving the representation ability of the CIR characteristic and outputting the authentication results. Besides, a new objective function is constructed in which both the single-peak and the double-peak Gaussian distributions are taken into consideration in the VAE module. Moreover, the simulations are conducted under the static and mobile IIoT scenario, which verify the superiority of the proposed HVAE over three comparison PLA schemes even with a few training data.

Intelligent Feature Selection for ECG-Based Personal Authentication Using Deep Reinforcement Learning.

In this study, the optimal features of electrocardiogram (ECG) signals were investigated for the implementation of a personal authentication system using a reinforcement learning (RL) algorithm. ECG signals were recorded from 11 subjects for 6 days. Consecutive 5-day datasets (from the 1st to the 5th day) were trained, and the 6th dataset was tested. To search for the optimal features of ECG for the authentication problem, RL was utilized as an optimizer, and its internal model was designed based on deep learning structures. In addition, the deep learning architecture in RL was automatically constructed based on an optimization approach called Bayesian optimization hyperband. The experimental results demonstrate that the feature selection process is essential to improve the authentication performance with fewer features to implement an efficient system in terms of computation power and energy consumption for a wearable device intended to be used as an authentication system. Support vector machines in conjunction with the optimized RL algorithm yielded accuracy outcomes using fewer features that were approximately 5%, 3.6%, and 2.6% higher than those associated with information gain (IG), ReliefF, and pure reinforcement learning structures, respectively. Additionally, the optimized RL yielded mostly lower equal error rate (EER) values than the other feature selection algorithms, with fewer selected features.

EEG-Based Person Identification and Authentication Using Deep Convolutional Neural Network

Using biometric modalities for person recognition is crucial to guard against impostor attacks. Commonly used biometric modalities, such as fingerprint scanners and facial recognition, are effective but can easily be tampered with and deceived. These drawbacks have recently motivated the use of electroencephalography (EEG) as a biometric modality for developing a recognition system with a high level of security. The majority of existing EEG-based recognition methods leverage EEG signals measured either from many channels or over a long temporal window. Both set limits on their usability as part of real-life security systems. Moreover, nearly all available methods use hand-engineered techniques and do not generalize well to unknown data. The few EEG-based recognition methods based on deep learning suffer from an overfitting problem, and a large number of model parameters must be learned from only a small amount of available EEG data. Leveraging recent developments in deep learning, this study addresses these issues and introduces a lightweight convolutional neural network (CNN) model consisting of a small number of learnable parameters that enable the training and evaluation of the CNN model on a small amount of available EEG data. We present a robust and efficient EEG-based recognition system using this CNN model. The system was validated on a public domain benchmark dataset and achieved a rank-1 identification result of 99% and an equal error rate of authentication performance of 0.187%. The system requires only two EEG channels and a signal measured over a short temporal window of 5 s. Consequently, this method can be used in real-life settings to identify or authenticate biometric security systems.

Ethnomusicology as the Ukrainian-Polish invention and common proceedings in study on ethnic musical cultures

The article discusses Ukrainian-Polish relationships in the realm of that traditional music culture which is deeply rooted both in natural and intangible values. It argues that the term and activities of ethnomusicology, formulated first in Ukraine (1928) then in Poland (1934), served as a means to gain or stabilize independence in ways of thinking and understanding their own cultures. There are presented common, parallel, similar initiatives and achievements in Poland and Ukraine pertaining to ethnic music cultures, their study and documentation since the beginning of 20th century. The text demonstrates a leading role of Ukrainian ethnomusicology in Europe on the assumption that traditional (folk, ethnic) music in the original, authentic performances helps better to understand past and present of culture of neighbouring countries than only spectacular folkloric arrangements for stage performances.

Fuzzy Logic Based Handover Authentication in 5g Telecommunication Heterogeneous Networks

Under various deployment circumstances, fifth-generation (5G) telecommunications delivers improved network compound management with fast communication channels. Due to the introduction of the Internet of Things (IoT) in data management, the majority of the ultra-dense network models in 5G networks frequently have decreased spectral efficiency, weak handover management, and vulnerabilities. The majority of traditional handover authentication models are seriously threatened, making them vulnerable to a variety of security attacks. The authentication of networked devices is the most important issue. Therefore, a model that incorporates the handover mechanism and authentication model must be created. This article uses a fuzzy logic model to create a handover and key management system that focuses on cloud handover management and authentication performance. In order to decrease delays in 5G networks, the fuzzy logic is built with multiple criteria that aim to reduce the number of executed handovers and target cell selection. The simulation is run to evaluate the model’s performance in terms of latency, spatial complexity, and other metrics related to authentication attack validation.

Three-Dimensional Convolutional Approaches for the Verification of Deepfake Videos: The Effect of Image Depth Size on Authentication Performance

Three-Dimensional Convolutional Approaches for the Verification of Deepfake Videos: The Effect of Image Depth Size on Authentication Performance

A Novel Length-Flexible Lightweight Cancelable Fingerprint Template for Privacy-Preserving Authentication Systems in Resource-Constrained IoT Applications

Fingerprint authentication techniques have been employed in various Internet of Things (IoT) applications for access control to protect private data, but raw fingerprint template leakage in unprotected IoT applications may render the authentication system insecure. Cancelable fingerprint templates can effectively prevent privacy breaches and provide strong protection to the original templates. However, to suit resource-constrained IoT devices, oversimplified templates would compromise authentication performance significantly. In addition, the length of existing cancelable fingerprint templates is usually fixed, making them difficult to be deployed in various memory-limited IoT devices. To address these issues, we propose a novel length-flexible lightweight cancelable fingerprint template for privacy-preserving authentication systems in various resource-constrained IoT applications. The proposed cancelable template design primarily consists of two components: 1) length-flexible partial-cancelable feature generation based on the designed reindexing scheme and 2) lightweight cancelable feature generation based on the designed encoding nested difference XOR scheme. Comprehensive experimental results on public databases FVC2002 DB1–DB4 and FVC2004 DB1–DB4 demonstrate that the proposed cancelable fingerprint template achieves equivalent authentication performance to state-of-the-art methods in IoT environments, but our design substantially reduces template storage space and computational cost. More importantly, the proposed length-flexible lightweight cancelable template is suitable for a variety of commercial smart cards (e.g., C5-M.O.S.T. Card Contact Microprocessor Smart Cards CLXSU064KC5). To the best of our knowledge, the proposed method is the first length-flexible lightweight, high-performing cancelable fingerprint template design for resource-constrained IoT applications.

Multiuser Physical-Layer Authentication Based on Latent Perturbed Neural Networks for Industrial Internet of Things

Recently, learning (DL)-based physical-layer authentication (PLA) has attracted much attention since artificial neural networks (ANNs) can be built to extract useful features from complex wireless environments, thus achieving high authentication performance and lightweight deployment in mobile edge computing (MEC)-Industrial Internet of Things (IIoT) scenario. However, the low latency characteristic of MEC makes it impossible to have much time to obtain sufficient signals for training the authentication system, which will cause over-fitting issues and deteriorate the authentication performance. Data augmentation is an effective method to address this problem. However, existing PLA with data augmentation can not generate representative and high-quality samples, consequently lacking generality in the actual identity authentication. To tackle this problem, a novel channel impulse response (CIR)-based multiuser authentication named latent perturbed neural networks (LPNNs) is proposed in this article, aiming at achieving high authentication performance even when trained a few data. Instead of relying on the generation of synthetic samples, the proposed LPNN adds Gaussian noise in the smooth latent space to avoid underdetermined and poor generalization, which has better interpretability. Specifically, to obtain a better understanding than a black box that connects input CIRs to authentication results, we defined Fingerprint Library and provided post-hoc explanations to answer the following question: which library examples explain the authentication results issued for a given CIR sample? Moreover, the simulations under the static and dynamic IIoT scenarios verify the superiority in authentication accuracy of the proposed LPNN over vanilla deep neural network (DNN) and convolutional neural network (CNN).

Physical Layer Authentication in the Internet of Vehicles based on Signal Propagation Attribute Prediction

Physical layer authentication (PLA) has emerged as a promising alternative to complex cryptographic-based authentication schemes, especially for the Internet of Vehicles (IoV) scenarios with resource-limited onboard units (OBUs). However, the existing PLA schemes securing the IoV against GPS location spoofing/falsification attacks consider only insider attackers. Moreover, they cannot be used by mobile vehicles to validate GPS locations. To address these issues, this paper proposes a PLA scheme based on the Gaussian process (GP) path loss prediction, where channel state information (CSI) is used to track the variation of the channel characteristics and predict the next legitimate path loss (PL) of the signal from a transmitter for authentication. The key ideas in the proposed scheme are to first establish a mapping between the historical CSI attributes and PL features of the transmitter’s signal and use this mapping to predict the next PL, which is then used to cross-verify the transmitter’s reported location information. Extensive simulation experiments are conducted using generated radio channel characteristics from the quasideterministic radio channel generator (QuaDRiGa) to demonstrate the effectiveness of the proposed approach. The results of the experiments show that our system efficiently addressed the limitations of the existing works and improves the authentication performance in IoV environments.

Hybrid Physical-Layer Authentication

Physical-Layer Authentication (PLA) attracts a lot of research interests because of its significant advantages over upper-layer authentication mechanisms: high security and low complexity. The PLA schemes can be categorized into passive and active schemes. In this paper, we extensively leverage the advantages of both the active and passive schemes as a reference scheme, named as the Direct Hybrid (DH) scheme. Although the DH scheme improves the authentication performance of the prior PLA schemes, it has limitations, e.g., high communication overhead. Then, we further propose two hybrid PLA schemes to overcome the limitations of the DH scheme. The first proposed scheme further uses the advantage of the Challenge-Response Authentication Mechanism (CRAM) scheme, named as the CR-based Hybrid (CRH) scheme. Although both DH and CRH schemes significantly improve the authentication performance of the prior PLA schemes, they do not address one significant limitation of the active scheme, i.e., to set the power allocation of a tag empirically. Thus, based on the CRH scheme, we further propose the Adaptive CR-Based Hybrid (ACRH) scheme to adaptively set the parameter instead of the empirical setting. Moreover, we provide the theoretical analysis of the proposed schemes over wireless fading channels and derive their closed-form expressions in terms of the Probability of Detection (PD), Probability of False Alarm (PFA), and optimal threshold, respectively. At last, we discuss the advantages and disadvantages of the proposed schemes and give some useful suggestions for seeking a better tradeoff. Our experimental results show that, in comparison with the active scheme, the DH scheme has better robustness, and the CRH scheme has better both robustness and compatibility but it sacrifices the security. The ARCH scheme achieves a better tradeoff than the remaining schemes.