Authentication Techniques Research Articles

Multi-factor Authentication Framework for Secured Data Access in Cloud Computing

Aims and Background: Accessing data services on the cloud raises serious concerns about security and privacy. To verify a client's stated identity, the existing authentication approach uses just one authentication factor. However, traditional techniques of one-factor authentication are inefficient since they demand an excessive amount of user information and expose their private data to eavesdropping. Methods: Cloud-based data storage is susceptible to both external and internal threats. Information security relies heavily on authentication, which is a method for verifying user identities before granting them access to protected data. When it comes to protecting data in a cloud computing environment from the most cutting-edge methods of attack, traditional password authentication simply isn't enough. For the purpose of protecting user identities in the cloud, this study presents interactive multi-layer authentication systems. Various access control policies are used when validating users in the cloud. Registration, permissions, and authentication factors for users are all parts of the cloud app's security process. Results: An intrusion detection system is built into the security mechanism to help identify people who could be malevolent. Various techniques are available for user identity verification, including intrusion detection, access control, and multi-factor authentication. The last step is to encrypt the data so that no one else can read it. Conclusion: To guarantee the effectiveness and accuracy of the suggested frameworks and methodology, they are experimentally evaluated. There were few false positives and a high detection rate.

Fuzzy logic and biometric-based lightweight cryptographic authentication for metaverse security

As Metaverse advances, robust authentication systems become more critical. Traditional security measures are not efficient and adaptable for metaverse scenarios. In this context, this study proposed a two-phase security framework that combines fuzzy logic and convolutional neural network (CNN) techniques for biometric authentication and a lightweight Cryptographic protocol for securing the Metaverse. In the first stage, the CNN model is used to extract accurate biometric signatures from hand tremor data. This innovative method takes into account the inherent unpredictability of biometrics. During the second phase, a cryptographic scheme is used to provide safe mutual authentication between the user and the Metaverse infrastructure. Our findings, which have been verified by BAN Logic analysis, show that the proposed framework achieves a high level of accuracy in user authentication and has robust defensive capabilities against typical cyber-attacks.

Enhancing Iris Authentication for Managing Multiple Cancellations: Leveraging Quotient Filters

Biometric authentication methods have become increasingly popular for their ability to offer secure and convenient access control. However, concerns about the privacy and security of biometric data have arisen. In this study, we present a novel approach to address these concerns by proposing a cancellable biometric encryption technique for secure and format-preserving iris authentication. Our method leverages the Quotient Filter data structure to transform encrypted iris templates into cancellable templates while preserving their original format. We carefully select an appropriate format-preserving encryption algorithm for iris templates and design a mapping scheme to achieve cancellability. To assess the effectiveness and performance of our approach, extensive experiments are conducted. The quantitative results indicate the efficiency and efficacy of our cancellable biometric encryption technique using the Quotient Filter. Our innovation, the Iris Authentication for Multiple Cancelled Instances Using a Quotient Filter (IAMCIQF), demonstrates competitive performance across several key metrics. IAMCIQF achieves a high level of security strength and strikes a balance between security and efficiency in terms of key size, encryption and decryption speeds and storage efficiency when compared to other existing techniques. The quantitative outcomes underscore IAMCIQF’s potential as a promising solution for attaining secure and format-preserving iris authentication, addressing critical concerns about biometric data security.

Role of nanomaterials in food authentication techniques: A mini-review

Role of nanomaterials in food authentication techniques: A mini-review

WITHDRAWN: A Secure and Efficient Blockchain-Powered Scheme for e-Invoicing Application

E-invoicing has become an increasingly popular form of electronic documents in B2B and B2G commerce transactions, and facilitates an effective exchange of invoice-related information containing tax invoice requirements. It enables businesses and governments to make transactions data be stored, retrieved and handled more efficiently among various aspects of business operations through an integrated systems solution such as ERP systems or e-commerce platforms. Although e-invoicing has numerous benefits, the integrated processes involve security risks associated with the ability to exchange and access critical transaction records and sensitive financial information over public networks such as the Internet. To secure e-invoicing services and maintain trust and privacy among their engaged stakeholders, many e-invoicing platforms use various authentication methods, such as factor-based mechanisms and digital signature techniques, to prevent potential threats and unauthorized access through cyberattacks. However, such authentication techniques cannot fully protect sensitive information contained in e-invoices from security breaches in the context of sharing transactional data. This proposed scheme introduces a secure authentication mechanism that integrates the self-certified methodology to enhance the trustworthiness of trading parties. In addition, the computational efforts of the proposed model show that high performance can be achieved in executing e-invoicing processes while maintaining data privacy. By leveraging the capabilities of blockchain technology and the adoption of decentralized applications and smart contracts, the proposed solution enables e-invoicing services to provide a highly secure, efficient, immutable, and transparent platform for businesses and tax authorities.

Main Primitive and Cryptography Tools for Authentication in VANET Environment: Literature Review

Vehicular ad hoc networks (VANETs) provide the potential to improve transportation efficiency by facilitating the sharing of traffic information among vehicles. Acceptance of VANET depends on communication speed and accuracy as well as privacy protection guaranteeing an individual's safety. Vehicle authentication is necessary to ensure message correctness. This necessitates the implementation of an effective privacy-preserving authentication scheme, as well as the need for both secrecy and timebound delivery of messages. Various privacy-preserving authentication schemes have been suggested to guarantee the integrity of messages in communications. However, most of the schemes are not able to solve issues related to computing costs, communication, security, privacy, threats, and vulnerabilities. In this review, we focus on cryptographic strategies that are suggested to accomplish privacy and authentication, such as identity-based, public key cryptography-based, pseudonym-based, and blockchain-based schemes. We provide a thorough analysis of schemes, including their categorizations, advantages, and drawbacks. The study demonstrates that the majority of current authentication techniques necessitate trusted authorities that lack transparency in their operations. Additionally, authentication process incurs substantial computational and communication overhead, leading to a considerable impact on the timely delivery of messages. More efforts are required to enhance the development of efficient authentication schemes in VANETs.

Secure data sharing scheme with privacy-preserving and certificateless integrity auditing in cloud storage

With cloud storage services becoming wildspread and low-cost, individuals and organizations select outsourcing large amounts of document to cloud. Cloud storage applications provide data sharing services with varying functionality. In some cloud storage systems that provide data sharing, such as e-health systems, hiding sensitive information fields is a necessity. Meanwhile, in sharing process, unauthorized entities may have access to these privacy fields. The resource-constrained computation capability for data owner (DO) is universal. To tackle above problems, we propose a sanitizer-based secure data sharing scheme where sanitizer performs most computations of encryption and signature after receiving the partially blinded data from DO. It also checks the legitimacy of visitors before CSP returns stored data file. Besides, adopting an attribute-based access policy guarantees further privacy and fine-grained access authorization. After verification, cloud will return the required data to the legitimate visitors. Moreover, integrity auditing to shared data file is based on certificateless authentication technique, which removes certificates issue of traditional cryptographic technique, avoids key-escrow attack risk caused by identity-based cryptography. Finally, the performance analysis and experimental results show that our proposed scheme is competitive in practical applications since it reduces the authenticator generation overhead by up to 21.6% and proof generation overhead by up to 12% compared with related schemes.

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.

Privacy Preserving Authentication of IoMT in Cloud Computing

INTRODUCTION: The Internet of Medical Things (IoMT) blends the healthcare industry with the IoT ecosystem and enables the creation, collection, transmission, and analysis of medical data through IoT networking. IoT networks consist of various healthcare IT systems, healthcare sensors, and healthcare management software. OBJECTIVES: The IoMT breathes new life into the healthcare system by building a network that is intelligent, accessible, integrated, and effective. Privacy-preserving authentication in IoMT is difficult due to the distributed communication environment of heterogeneous IoMT devices. Although there has been numerous research on potential IoMT device authentication methods, there is still more to be done in terms of user authentication to deliver long-term IoMT solutions. However, password handling is one of the big challenges of IoMT. METHODS: In this paper, we present an IoMT-related online password-less authentication technique that is quick, effective, and safe. In order to offer cross-platform functionality, the article includes a simulation of FIDO2/WebAuthn, one of the most recent standards for a password-less authentication mechanism. RESULTS: This makes it easier to secure user credentials and improve them while preserving anonymity. The IoMT device authentication process and registration process delays are also assessed. CONCLUSION: Results and simulations show that the efficacy of the proposed mechanism with quick authentication on cloud servers may be accomplished with the fewest registration and authentication procedures, regardless of device setup.

The Concept of Electronic Pensioner in Some Countries Versus Romania

We aim to introduce the concept of e-pensioner (EP) with the intention of having this term used as a document within the European Union’s strategy on Artificial Intelligence (AI) for the period 2023-2027 and after implemented into the legislation of member countries. The relationship between older generations and emerging forms of digitalization and artificial intelligence is the focal point, aiming to provide an easily approachable dimension for pensioners. This article presents an analytical framework and approaches for integrating pensioners into the digital era. Following the exposition of theoretical foundations, the article delineates the use of pensioner cards in select countries worldwide. The intersection of pensioners with the digital era proves to be a fertile entry point for examining regulatory ordinances in the digital realm. A novel digital form, the AI-based digital portal, involves a technique of facial authentication for pensioners to send digital life certificates. Two key factors are named across all examined countries. The primary driver of digital transformation is propelled by the desire to enhance the efficiency and efficacy of public administration, which becomes particularly crucial as populations age, leading to increased demand for social services and competition for limited resources in the developed public sector. The innovative application of technology presents opportunities for streamlining processes, enhancing productivity, and reducing costs while concurrently improving service quality. The use of artificial intelligence (AI) in the management and administration of public pension funds stands for a natural progression of technology in the public domain. In an environment characterized by complexity and volatility, AI can provide a level of analysis and prediction that exceeds human ability. Presently, there exists a wide array of AI-powered tools for object detection, natural language processing, understanding and inference, optical character recognition, predictive analytics, anomaly detection, noise elimination, model forecasting, recommendation systems, and monitoring and observability.

A Privacy Focused Chat Application Build Using Flutter

Abstract: In contemporary virtual age, privateness issues have grow to be more and more frequent, mainly in communique platforms where touchy statistics is exchanged. This research paper presents the development of a privacy-focused chat application built using Flutter and Firebase. The utility prioritizes person privateness by means of implementing give up-to-give up encryption, making sure that handiest the supposed recipients can get entry to the messages exchanged. Additionally, the platform consists of robust authentication mechanisms to protect consumer debts and prevent unauthorized access. Through the usage of Firebase as the backend infrastructure, the chat software guarantees scalability, reliability, and actual-time synchronization of data across gadgets. The paper discusses the technical implementation details, such as the encryption algorithms employed, authentication techniques utilized, and the mixing of Firebase offerings. Furthermore, it evaluates the effectiveness of the privacy measures carried out and explores capability regions for similarly enhancement. This studies contributes to the developing body of understanding surrounding privacy-centric software development and gives insights into growing stable verbal exchange platforms in the virtual technology.

An Efficient, Fast and Accurate Online Signature Verification Using Blended Feature Vector and Deep Learning

Online signature verification is one of the biometric authentication techniques. Online signature verification reduces the human error that may occur due to physical signature verification. Many researchers have provided a method of online signature verification still there is a scope for improvement. In this paper, a novel blended feature vector (BFV) is formed by combining two feature vectors. The first of these feature vectors is formed using a raw online signature database and the other feature vector is formed of the features extracted from the grayscale images obtained from the signature database. The algorithm makes use of the probability distribution of the features to provide a feature vector that results in an enhanced verification system. Two online signature databases, namely SVC2004 and ATVS-SSig are used. BFV is used to train the proposed mixed sequence deep neural network (MS-DNN). The use of a blended feature vector in the training of MS-DNN, instead of a whole online signature database, improves the training time of deep neural network to a great extent. This reduction in training time might be useful for new user registration. The performance parameters considered are validation efficiency and equal error rate. Results are compared with the existing state of technology and the comparison shows that the use of a blended feature vector as a classification vector improves the validation efficiency (99.5%) and also there is an improvement in the verification success rate in terms of equal error rate (EER 1.5%) as compared to the existing research.

A Certificateless Linearly Homomorphic Signature Scheme Based on Lattice for Network Coding

Abstract Homomorphic signature is an extremely important public key authentication technique for network coding to defend against pollution attacks. However, there are many problems with previous homomorphic signature schemes which require key escrow, cannot resist malicious key generation center (KGC), and are insecure in the post-quantum era. Therefore, we propose a lattice-based certificateless linearly homomorphic signature scheme. In our scheme, certificateless structure can avoid key escrow and malicious KGC. The lattice structure ensures that our scheme is secure in the post-quantum era. The bimodal Gaussian distribution is used to improve the security and the efficiency. Compared with the previous schemes, our scheme has smaller storage space (no key escrow), can avoid malicious KGC, is more secure in the post-quantum era, and has higher signature efficiency. At the same time, our scheme is more suitable for network coding. Finally, under random oracle model, we proved that our scheme is weakly context hiding and existentially unforgeable against adaptive chosen message attacks against external attackers and the internal KGC.

Internet of Things (IoT) Data Communication Challenges, and Solutions

IoT has become an integral part of our lives, and data communication is one of the most important aspects of IoT. IoT devices can exchange data with each other via secure communication channels. This study aimed to review the IoT data communication challenges and solutions This paper attempts to address the key challenges and solutions for effective IoT data communication, starting by exploring the current challenges of IoT data communication related to scalability, reliability, and security, potential solutions to these challenges, such as using a mesh network, centralized servers, and edge computing. In addition, the paper discusses the importance of proper authentication and authorization techniques to ensure secure communication between IoT devices. The potential of IoT data communication is vast and has the power to fundamentally transform our society. It is an exciting time to be involved in this field, and the future looks promising for the advancement of IoT data communication.

Authentic Materials And Their Use In The Process Of Forming The Type Of Searching Reading

This article is devoted to the development of search reading skills through the use of authentic material. The article provides examples of authentic materials and techniques for working with them.

Authentic Assessment Techniques in IPAS Learning in Elementary School : A Case Study

This research aims to describe the authentic assessment techniques coveyring cognitive, affective, and psychomotor aspects in IPAS learning in elementary schools. This research used a qualitative approach with a descriptive method. The design used in this research is a case study, in which researchers reveal and analyze holistically to get specific results. The subjects contained in this study are educators as homeroom teachers who are also IPAS teachers, students, and school principals. The data collection carried out in this study is using primary sources which include observation and interviews. Secondary sources included journals, books, and scientific articles. Data analysis conducted in this research is descriptive analytic. The results obtained in this study are based on the cognitive aspects of educators using written test assessment techniques, oral tests, and assignments. Affective aspects educators use observation, self-assessment, peer assessment, and journal assessment techniques. As well as psychomotor aspects, educators use project and portfolio assessment techniques. The achievement of learner competence can be seen based on the three aspects and the level of learning success is an integral part of the educational process.

A Lightweight Symmetric Cryptography based User Authentication Protocol for IoT based Applications

The utilization of IoT is expanding across various domains, including telecare, intelligent home systems, and transportation networks. In these environments, IoT devices generate data gathered on remote servers, requiring external users to authenticate themselves to access the data. However, existing authentication protocols for IoT must meet the crucial requirements of speed, security against multiple attacks, and ensuring user anonymity and un-traceability. The main objective of this work is to find lightweight symmetric cryptography-based user authentication protocol tailored for IoT-based applications, focusing on MIM (Man-in-the-Middle) attack prevention, enhanced anonymity, and secure communication between IoT nodes and remote servers via IoT gateways. Existing protocols often lack sufficient defenses against MIM attacks and do not adequately address the need for enhanced user anonymity and secure communication channels within the IoT framework. Our research has identified that authentication techniques based on pairing are susceptible to attacks targeting temporary session-specific data, impersonation, privileged insiders, and offline password guessing. Furthermore, using bilinear pairing in these techniques requires significant computational and communication resources to address the security as mentioned above concerns. A new authentication mechanism must be proposed and designed explicitly for IoT scenarios. The proposed approach exclusively utilizes hash and exclusive-or operations to ensure suitability within the IoT context; thoroughly evaluated the recommended protocol against existing authentication protocols, employing both informal and formal analytical routines like BAN logic, ROR model, and AVISPA simulation. Our findings suggest protocol not only enhances performance but also enhances security. The proposed approach is a tried-and-true strategy for improving security rules in practical Internet of Things (IoT) settings addressing the inherent challenges posed by authentication requirements in IoT environments. The accuracy 98.93%, and Node detection rate 46.57% were improved which is a better outcome.

Medical Data in Wireless Body Area Networks: Device Authentication Techniques and Threat Mitigation Strategies Based on a Token-Based Communication Approach

Wireless Body Area Networks (WBANs), low power, and short-range wireless communication in a near-body area provide advantages, particularly in the medical and healthcare sector: (i) they enable continuous monitoring of patients and (ii) the recording and correlation of physical and biological information. Along with the utilization and integration of these (sensitive) private and personal data, there are substantial requirements concerning security and privacy, as well as protection during processing and transmission. Contrary to the star topology frequently used in various standards, the overall concept of a novel low-data rate token-based WBAN framework is proposed. This work further comprises the evaluation of strategies for handling medical data with WBANs and emphasizes the importance and necessity of encryption and security strategies in the context of sensitive information. Furthermore, this work considers the recent advancements in Artificial Intelligence (AI), which are opening up opportunities for enhancing cyber resilience, but on the other hand, also new attack vectors. Moreover, the implications of targeted regulatory measures, such as the European AI Act, are considered. In contrast to, for instance, the proposed star network topologies of the IEEE 802.15.6 WBAN standard or the Technical Committee (TC) SmartBAN of the European Telecommunication Standards Institute (ETSI), the concept of a ring topology is proposed which concatenates information in the form of a ‘data train’ and thus results in faster and more efficient communication. Beyond that, the conductivity of human skin is included in the approach presented to incorporate a supplementary channel. This direct contact requirement not only fortifies the security of the system but also facilitates a reliable means of secure communication, pivotal in maintaining the integrity of sensitive health data. The work identifies different threat models associated with the WBAN system and evaluates potential data vulnerabilities and risks to maximize security. It highlights the crucial balance between security and efficiency in WBANs, using the token-based approach as a case study. Further, it sets a foundation for future healthcare technology advancements, aiming to ensure the secure and efficient integration of patient data.

Reliable human authentication using AI-based multibiometric image sensor fusion: Assessment of performance in information security

Reliable human authentication is essential for assuring the availability, confidentiality, and integrity of sensitive data and resources in the field of information security. Due to the vulnerabilities of conventional authentication techniques like passwords and PINs, interest in biometric-based authentication systems has developed. To get over this, we suggest an efficient voice and iris recognition-based multimodal biometric verification strategy for human authenticating tools. We first collect the voice and iris datasets from 150 people. Then, we used a median filter and high pass filter to preprocess the voice and iris data. The features of voice identification are extracted using the shifted delta cepstral coefficient (SDCC) and the Mel frequency discrete wavelet coefficient (MFCC), and these two coefficients are evaluated. The outcomes of the extraction of iris identification features using Local binary pattern (LBP) and Speeded robust features (SURF) are evaluated. The classifier Fine Tuned Cuckoo Search Optimized Convolutional Neural Network (FCSO-CNN) performs voice and iris recognition modalities. Voice and iris biometric systems may be combined into a single multimodal biometric system by fusing their respective feature sets and scoring algorithms. The results of the computer simulation demonstrate that for speech recognition, using the SDC and MFC coefficients produces better results, while for iris recognition, using the LBP and FCSO-CNN experiment produces superior outcomes. Additionally, the scores fusion works superior to other scenarios in the proposed multimodal biometrics system. The suggested system provides a dependable and strong solution for human identification, assisting in the improvement of security measures across a variety of industries, including financial institutions, governmental agencies, and the defense of key infrastructure.

Analysis of Biometric Authentication Techniques: A Review

This report delves into diverse biometric authentication techniques and offers an overview of their evolution, applications, limitations, advancements, and comparative attributes. It also highlights biometric authentication's pivotal role in elevating security and convenience across sectors.