Authentication Problem Research Articles

ESTIMATION OF FORMANT INFORMATION USING AUTOCORRELATION FUNCTION OF VOICE SIGNAL

Context. The current scientific problem of extracting biometric characteristics of a user of a voice authentication system, which can significantly increase its reliability, is considered. There has been performed estimation of formant information from the voice signal, which is a part of the user template in the voice authentication system and is widely used in the processing of speech signals in other applications, including in the presence of interfering noise components. The work is distinguished by the investigation of a polyharmonic signal. Objective. The purpose of the work is to develop procedures for generating formant information based on the results of calculating the autocorrelation function of the analyzed fragment of the voice signal and their subsequent spectral analysis. Method. The procedures for generating formant information in the process of digital processing of voice signal are proposed. Initially, the autocorrelation function of the analyzed fragment of the voice signal is calculated. Based on the results of the autocorrelation function estimation, the amplitude-frequency spectrum is calculated, from which the formant information is extracted, for example, by means of threshold processing. When the signal-to-noise ratio of the analyzed voice signal fragment is low, it is advisable to iteratively calculate the autocorrelation function. The latter allows increasing the signal-to-noise ratio and the efficiency of formant information extraction. However, each subsequent iteration of the autocorrelation function calculation is associated with an increase in the required computational resource. The latter is conditioned by the doubling of the amount of processed data at each iteration. Results. The developed procedures for generating formant information were investigated both in the processing of model and experimental voice signals. The model signals had a low signal-to-noise ratio. The proposed procedures allow to determine more precisely the width of the spectrum of extracted formant frequencies, significantly increase the number of extracted formants, including cases at low signal-to-noise ratio. Conclusions. The conducted model experiments have confirmed the performance and reliability of the proposed procedures for extracting formant information both in the processing of model and experimental voice signals. The results of the research allow to recommend their use in practice for solving problems of voice authentication, speaker differentiation, speech and gender recognition, intelligence, counterintelligence, forensics and forensic examination, medicine (diseases of the speech tract and hearing). Prospects for further research may include the creation of procedures for evaluating formant information based on phase data of the processed voice signal.

A Secured Keystroke-Based Model for Preventing Social Engineering Attacks using Recurrent Neural Network

Among several authentication problems, preventing social engineering attacks using behavioural biometric approach has not received the required attention especially with focus on keystroke dynamics. This study aims to leverage the power of deep learning for more accurate and robust continuous authentication based on typing patterns. The proposed framework for this study utilized deep learning algorithm for behavioural biometrics authentication using Keystroke dynamics. The deep learning model was developed using Recurrent Neural Network (RNN) algorithm and was optimized was to obtain a better performance with Bayesian optimization which, eventually enhanced the model's accuracy. The dataset was split into training and testing in the model design phase and some hyperparameters such as dense, activation, batch size, sigmoid, filament, input size and epoch were used and optimized for building the deep learning algorithm. The RNN model is used to generate the evaluation metrics such as log loss, accuracy, precision and recall. The result presented the accuracy, precision, recall, and loss function as 100%, 100%, 100%, and 36% respectively for optimized model. The cost metrics yielded 0.0032, 0.0032, and 0.0006 MAE, MSE, and RMSE respectively. The developed KBB shows high level of social engineering attacks mitigation in comparison with the existing solution from the performance measure results.

SLACPSS: Secure Lightweight Authentication for Cyber–Physical–Social Systems

The concept of Cyber–Physical–Social Systems (CPSSs) has emerged as a response to the need to understand the interaction between Cyber–Physical Systems (CPSs) and humans. This shift from CPSs to CPSSs is primarily due to the widespread use of sensor-equipped smart devices that are closely connected to users. CPSSs have been a topic of interest for more than ten years, gaining increasing attention in recent years. The inclusion of human elements in CPS research has presented new challenges, particularly in understanding human dynamics, which adds complexity that has yet to be fully explored. CPSSs are a base class and consist of three basic components (cyberspace, physical space, and social space). We map the components of the metaverse with that of a CPSS, and we show that the metaverse is an implementation of a Cyber–Physical–Social System (CPSS). The metaverse is made up of computer systems with many elements, such as artificial intelligence, computer vision, image processing, mixed reality, augmented reality, and extended reality. It also comprises physical systems, controlled objects, and human interaction. The identification process in CPSSs suffers from weak security, and the authentication problem requires heavy computation. Therefore, we propose a new protocol for secure lightweight authentication in Cyber–Physical–Social Systems (SLACPSSs) to offer secure communication between platform servers and users as well as secure interactions between avatars. We perform a security analysis and compare the proposed protocol to the related previous ones. The analysis shows that the proposed protocol is lightweight and secure.

The Authenticity Problem: Authenticity as a Methodological Trap in People-Centred Research on Working-Class Football Supporting Communities

AbstractThis paper seeks to make a methodological contribution to archaeological praxis of working-class communities, by illuminating how archaeologists engaged in oral history-based research with working-class communities may encounter authenticity as a methodological challenge. Drawing on my PhD research on football as cultural heritage, I will outline the authenticity problem I encountered in the field: the enforcement of hierarchies of authenticity by working-class football supporters in response to their experiences of marginalisation in the sport. In turn, I will not only show how these hierarchies of authenticity present obstacles to researchers looking to build relationships of trust with their subjects, but also indicate some solutions to this authenticity problem. Specifically, I will show how it is often useful to “fall into the trap of authenticity” as a researcher and use the interview setting to discursively construct yourself as authentic on your subject communities’ own terms.

Forms of Expression of Will in Civil Transactions

Introduction: the relevance of the topic is related to the emergence of new electronic forms of expression of will when making transactions, which has necessitated ensuring their security in the digital environment. The purpose of the study is to solve the problems of the form of expression of will of parties to civil transactions, including transactions concluded in electronic form by means of information and communication systems. Methods: in preparing the study, the dialectical, logical, and comparative law methods were used, which made it possible to analyze various approaches to classifying forms of expression of will, including oral, written, and other forms, and their legal significance in the context of current legislation. Results: the paper analyzes various forms of expression of will that are important for the validity and enforceability of transactions. The doctrine and judicial practice related to the digitalization of the expression of will have been studied. The problem of ensuring the protection of the rights of parties to electronic transactions related to the identification and authentication of the parties in the digital environment is analyzed. The ways to improve the regulatory framework to take into account the development of digital technologies and ensure the protection of the rights of parties to transactions are proposed. The scope of the results: the approaches outlined in the study to solving the problems of expression of will in the digital environment can become the basis for improving the current civil law legislation; they can serve as the basis for further scientific research in this area; and they can improve law enforcement practice. Conclusions: the proposals related to solving the problems of identification and authentication when entering into the transaction are substantiated. It is proposed to use blockchain technology to solve the problem of confidential data security on the one hand and transparency on the other. The need to fix in the law the methods of electronic transmission of legally significant messages is justified.

Pricing and blockchain adoption for competitive sellers on an e-commerce platform with different contracts

We examine the impact of a blockchain technology (BCT) adoption strategy on the decisions and profits of branded companies and counterfeiters, and then discuss how BCT helps counterfeiters transform on a platform offering blockchain services and commission contracts. Our results show that BCT can make the branded seller better off when a platform has more brand-conscious consumers, despite the high adoption fee. Due to the free-riding effect, the counterfeiter’s best choice is often not to adopt BCT. As the fraction of brand-unconscious consumers increases, even with low adoption fee, the branded seller will not use BCT, but the counterfeiter will adopt BCT to transform her business and become a legitimate seller by offering innovative products, which increases consumer surplus. As the counterfeit probability and blockchain added value increase, both sellers proactively adopt BCT to disclose product authenticity, which may worsen the platform and social welfare. Importantly, there is a win–win–win outcome for sellers, platforms and consumers when at least one seller adopts BCT, depending on the interaction among the abovementioned factors. Compared with the proportional-fee contract, the fixed-fee contract improves the cost affordability of both sellers. In the context of different consumer types on platforms, our findings offer guidelines for branded companies to solve the authenticity problem of products and for counterfeiters to transform their businesses.

Fraud detection in the fishing sector using hyperspectral imaging

Currently, more and more consumers are interested in the quality, safety, and authenticity of food products. The fishing sector is the second food category with the highest risk of fraud and the greatest presence of authentication problems. There are non-destructive, fast and accurate techniques for real-time authentication, with hyperspectral imaging (HSI) standing out among these. In this context, the main aim of this study is to explore the viability of HSI in the visible and near infrared (VIS-NIR) and near infrared (NIR) ranges for the detection of fraud by origin and by non-declaration of the previous freezing process, in anchovies. The spectral pretreatment methods used were the standard normal variate method, the Savitzky-Golay 1st derivate and the Savitzky-Golay 2nd derivate, always followed by mean centering (MC). In addition, the impact of using a previous step of smoothing prior to pretreatment was also evaluated. Two classification algorithms: soft independent modeling of class analogy, and partial least squares discriminant analysis (PLS-DA) were used to build the classification model. After analysis, it was found that the modelling results using the VIS-NIR region were always better than those using the NIR region, and the best performing model was by PLS-DA with a recall of 0.97 for fresh and 0.98 for frozen-thawed anchovies and 0.98 for Cantabrian anchovies and 0.96 for Mediterranean anchovies. One advantage of the model obtained is the ability to classify the anchovies measuring on the skin side of fish without the need for sample preparation. Overall, the results showed that HSI combined with PLS-DA is a favorable solution for rapid, and non-destructive recognition of adulteration regarding freshness and origin in anchovies.

Faith in Fakes

Social media platforms have a deservedly poor reputation amongst scholars, cultural critics, politicians, and the wider public for their exploitation of users and their complicity in fostering misinformation and disinformation on a variety of topics. And yet they enjoy widespread usage, a duality increasingly understood through the false promise of authenticity they offer. This article considers the photo sharing app BeReal, an upstart contender whose viral popularity with teenagers made it both an emblem and an antidote to social media’s authenticity problem. Drawing on and affirming many of the conclusions in the existing scholarship on authenticity and media, it uses BeReal to demonstrate the necessity of considering photography as an essential element in the authenticity framework. Doing so clears a space for individual agency in the face of the acknowledged dominance of these platforms.

A Cross-Chain Mechanism for Agricultural Engineering Document Management Blockchain in the Context of Big Data

Cross-chain mechanism functions as typical approaches for information interaction between diverse blockchains tackling the problem of information silos in the big data era. Most of the existing cross-chain mechanisms are targeted at virtual currency blockchains in the financial sector. With more and more engineering documents manufactured by the development of modern smart farming, the need for engineering document management and cross-chaining between various blockchains has become increasingly urgent. This paper proposes a novel attainable cross-chain mechanism for agricultural engineering document management blockchains concerning the unique structure and operation principals of the specific domain. The methodology sufficiently integrated the characteristics of the agricultural engineering document management with the notary scheme, constructed by government supervision nodes with high credibility. Meanwhile, the authentication technology and cryptographic algorithms are internally fused, solving the authentication problem of the document cross-chain and protecting the cross-chain information respectively, which ensures the integrity and security of the file attribute information, alongside file ontology data in the cross-chain process. Adequate security proof and experiments illustrate that the developed mechanism can guarantee the feasibility of the mechanism, authenticity of the cross-chain parties, and the integrality and reliability of the document information, thus catering to the requirements of the cross-chain performance of blockchain in the field of agricultural engineering document management.

A rapid method for the determination of stable hydrogen isotope ratios of acetic acid in vinegar.

Vinegar is an everyday condiment made from fermented grains or fruits. It contains acetic acid which is the main organic material produced by fermentation. Vinegar suffers from the authenticity problem of exogenous adulteration due to the indistinguishability of low-cost chemical sources of synthetic acetic acid from acetic acid produced by fermentation. It is necessary to establish a simple and rapid measurement technique. Determination was according to the total acid content of vinegar diluted with acetone to a certain concentration. Online separation and determination of acetic acid δD in vinegar were carried out using gas chromatography-pyrolysis-isotope ratio mass spectrometry. An HP-Plot/U column was selected for online separation of acetic acid and water with molecular sieve characteristics. At the same time, combined with the instrument blowback function to remove water. Dilute solvent acetone was treated with a molecular sieve to remove trace water. The reproducibility of this method is less than 3‰. The long-term stability is within a reasonable error range. The accuracy correlation coefficient is greater than 0.99. The δD values of acetic acid in vinegar (-264.5 ± 20.3‰) and from chemical sources (-30.5 ± 90.8‰) were obtained. A rapid method was developed for identification of different sources of acetic acid. These different sources of acetic acid exhibited significant hydrogen isotope distribution characteristics. Additionally, it was observed that the carboxyl hydrogen of acetic acid exhibited facile exchange with water. In future investigations, we aim to mitigate this interference.

Algorithm of information stability in hydroacoustic control channel for aquaculture

Increasing attention is being paid to the mechanisms of data transfer between ocean research devices and data collection and analysis centers for the subsequent study and theorization of ocean evolution. All transmitted data are vulnerable to interception by unauthorized parties, so one of the main challenges is to protect the transmitted information. The proposed algorithm is based on steganographic method and solves the authentication problem by embedding additional data into the original signal in such a way that it is not empirically detectable. Among the first and best- known methods for hidden data embedding is a technique based on spread spectrum technology and its various adaptations, which incorporate additional parameters into the mathematical equation to improve the efficiency of the method. However, when auxiliary information is encoded into the original audio signal, these parameters affect both the covertness and robustness of the embedded data, potentially compromising the integrity of the steganographic system. Consequently, the development of steganographic techniques is aimed at simultaneously improving stealth and robustness.

Implementation of Http Security Protocol for Internet of Things Based on Digital Envelope

The popularity of the Internet of Things (IoT) has been growing in recent years. Low-end or constrained devices are the best IoT device that provides energy efficiency, small size, and mobility support. Unfortunately, connected devices can create security hazards between devices and servers, so ensuring each sender's and receiver's identity is crucial. This research proposes an authentication scheme using the digital envelope method to overcome authentication problems on constrained devices. Encryption is used to secure the data sent by the digital envelope method. The performance achieved is still within tolerance, considering the authentication scheme has succeeded in providing some security services, namely authentication, data confidentiality, data integrity, and non-repudiation.

Rancang Bangun Alat Pendeteksi Keaslian Batu Bacan Doko Berbasis Mikrokontroler ATMega2560

This research designed and developed a tool to detect the authenticity of Bacan Doko. The development of this tool uses TCS3200 sensor components as a detection of color variations in stones, BH1750 sensors to measure light intensity, and LCD1602 to display information on stone detection results visually. This study aims to overcome the problem of identifying the authenticity of Bacan Doko stones which are often difficult to detect accurately by ordinary people who do not understand the world of gemology, which results in the existence of fake stones on the market. The presence of this tool provides innovation in the world of gemology by utilizing microcontroller and sensor technology to overcome the authentication problem of Bacan Doko stone accurately, and support the integrity of the gemstone market. The development of data processing algorithms is carried out to analyze and process information received from input values on sensors. The detection tool provides good results to recognize and distinguish genuine Bacan Doko stones based on the color characteristics of the sensor identification results TCS3200 taking into account the light intensity measured by the BH1750 sensor and information can be displayed clearly through the LCD screen. The development of this tool successfully proved its effectiveness in identifying the authenticity of Bacan Doko stone. This tool can be a reliable solution for traders and gemstone observers in ensuring the authenticity of Bacan Doko.

Deep transfer learning for visual analysis and attribution of paintings by Raphael

Visual analysis and authentication of artworks are challenging tasks central to art history and criticism. This preliminary study presents a computational tool for scholars examining and authenticating a restricted class of paintings, with a specific focus on the paintings of Raffaello Sanzio da Urbino, more popularly known as Raphael. We applied transfer learning to the ResNet50 deep neural network for feature extraction and used a support vector machine (SVM) binary classifier in support of authentication. Edge detection and analysis algorithms, considered to be crucial for capturing the essence of Raphael’s artistic style, including the brushwork signatures, were also integrated and are used as an authentication tool. The machine learning approach we have developed demonstrates an accuracy of 98% in image-based classification tasks during validation using a test set of well known and authentic paintings by Raphael. Of course, a full authentication protocol relies on provenance, history, material studies, iconography, studies of a work’s condition, and more. Our work, then, contributes to just a portion of a full authentication protocol. Our findings suggest that machine learning methods, properly employed by experts aware of context, may enhance and expand traditional visual analysis for problems in art authentication.

Bias Cuts and Data Dumps

Bias Cuts and Data Dumps

Current State of Milk, Dairy Products, Meat and Meat Products, Eggs, Fish and Fishery Products Authentication and Chemometrics.

Food fraud is a matter of major concern as many foods and beverages do not follow their labelling. Because of economic interests, as well as consumers' health protection, the related topics, food adulteration, counterfeiting, substitution and inaccurate labelling, have become top issues and priorities in food safety and quality. In addition, globalized and complex food supply chains have increased rapidly and contribute to a growing problem affecting local, regional and global food systems. Animal origin food products such as milk, dairy products, meat and meat products, eggs and fish and fishery products are included in the most commonly adulterated food items. In order to prevent unfair competition and protect the rights of consumers, it is vital to detect any kind of adulteration to them. Geographical origin, production methods and farming systems, species identification, processing treatments and the detection of adulterants are among the important authenticity problems for these foods. The existence of accurate and automated analytical techniques in combination with available chemometric tools provides reliable information about adulteration and fraud. Therefore, the purpose of this review is to present the advances made through recent studies in terms of the analytical techniques and chemometric approaches that have been developed to address the authenticity issues in animal origin food products.

FVF-AKA: A Formal Verification Framework of AKA Protocols for Multi-server IoT

As IoT in a multi-server environment increases resources’ utilization, more and more problems of IoT authentication and key agreement are being revealed. The Authentication and Key Agreement (AKA) protocol plays an important role in solving these problems. Many AKA protocols have been proposed, and some of them support their own verifications. However, a unifying verification framework for multi-server IoT is lacking. In this article, we propose a formal verification framework of AKA protocols for multi-server IoT (FVF-AKA). It supports the construction of CSP models for the AKA protocol, the implementation of the CSP models in PAT with C#, and the verification of formal models. With the help of C#, many complex functions in the AKA protocol can be implemented. We also design an algorithm to support automatic conversion from the CSP model to the PAT model. FVF-AKA can verify four fundamental properties (deadlock freedom, entity legitimacy, timeout delay, and session key consistency). It also supports the verification of security properties for the AKA protocol suffering from four different attacks (relay attacks, denial of service attacks, server spoofing attacks, and session key attacks). Our approach can be applied to most AKA protocols for multi-server IoT generally. By applying FVF-AKA to two AKA protocols, we can verify whether they satisfy the fundamental properties and analyze their security properties in vulnerable environments. Our work would help to analyze the AKA protocol for multi-server IoT and provide the foundation for the analysis of enhancing its security and robustness.

CNN combined with data augmentation for face recognition on small dataset

Faces have universal structures yet contain distinct features among individuals. Recognizing individuals based on their faces has always been a popular topic in pattern recognition, and computer vision and many traditional approaches have yielded satisfying results. In recent years, rapid growth in deep learning has encouraged researchers to use deep learning methods to solve authentication problems. Convolutional neural networks are one of the most popular deep neural networks with multiple layers and the ability to reduce parameters by using kernels to capture features from input. It has outstanding performance in pattern recognition due to its ability to extract features and take images as inputs. In machine learning, data augmentation is a technique to seemingly enlarge a dataset to avoid underfitting or overfitting problems caused by insufficient data. This paper uses convolutional neural networks to solve face recognition problems on a small dataset. It compares performance with traditional face recognition methods such as Principal Component Analysis and examines the impact on performance using data augmentation. Overall, data augmentation boosts the accuracy of the network but also results in an unsteady learning curve. The convolutional neural network performs well on pattern recognition and obtains an accuracy of 94% in an augmented dataset with only two convolutional layers.

Security in wireless body area networks via anonymous authentication: Comprehensive literature review, scheme classification, and future challenges

Authentication is a security practice that seeks to validate a user identity to various entities, yet doing so across a wireless network poses inherent risks. Wireless Body Area Networks (WBANs) are a unique form of network that contains e-healthcare patient data, thus WBANs require a high level of privacy and security. There are several anonymous WBAN authentication systems in the literature, but this is the first paper to conduct a complete review of all the various schemes, then tabulate and compare the results. Doing so reveals a number of research avenues that need either greater depth, or systems development. This article presents a thorough taxonomy and survey of the anonymous authentication methods, illustrating the security services and vulnerabilities, and then the properties of an ideal anonymous authentication scheme. The various schemes reviewed are also classified by their main encryption algorithm, such as bilinear parings-based schemes, elliptic curve cryptography-based solutions, lattice-based methods, and XOR-based approaches. The main authentication capabilities, cryptographic features, security advantages, evaluation metrics, and shortcomings are all specified and discussed. A thorough comparison of anonymous authentication methods is presented to demonstrate each scheme's resistance to a variety of security threats. The study concludes that very few schemes have addressed the group authentication problem, which is very important in healthcare systems. Also, in the multi-factor authentication context, which provides a higher level of security, only 11% of the schemes used three factors, and 1% of them utilized four factors. Besides, DDoS attacks, as a growing concern in all computing environments, are handled by a narrow number of schemes. At last, a set of recommendations for further research based on identified literature gaps.

E-eye, flash GC E-nose and HS-GC-MS combined with chemometrics to identify the adulterants and geographical origins of Ziziphi Spinosae Semen.

Ziziphi Spinosae Semen (ZSS), a valuable seed food, has faced increasing authenticity issues. In this study, the adulterants and geographical origins of ZSS were successfully identified by electronic eye, flash gas chromatography electronic nose (Flash GC e-nose) and headspace gas chromatography-mass spectrometry (HS-GC-MS). As a result, there were color differences between ZSS and adulterants, mainly represented by the a* value of ZSS was less than adulterants. In ZSS, 29 and 32 compounds were detected by Flash GC e-nose and HS-GC-MS. Spicy, sweety, fruity and herbal were the main flavor of ZSS. Five compounds were determined to be responsible for flavor differences between different geographical origins. In the HS-GC-MS analysis, the relative content of Hexanoic acid was the highest in ZSS from Hebei and Shandong, while 2,4-Decadien-1-ol was the highest in Shaanxi. Overall, this study provided a meaningful strategy for addressing authenticity problems of ZSS and other seed foods.