Authentication Process Research Articles

Quantifying pigment features of Thangka Five Buddhas using hyperspectral imaging

Thangka, a masterpiece of Tibetan painting, is renowned for its adept use of natural mineral pigments such as gold, turquoise, and cinnabar, which imbue it with profound artistic and historical significance. Presently, chemical analysis methods relying on microscopic perspectives are prevalent in researching the pigment components of cultural artifacts. However, these methods suffer from quantization gaps and carry the risk of damaging the relics. Hence, in this study, we focus on Thangka Five Buddha as our experimental sample and propose a novel approach utilizing Linear Spectral Mixed Analysis (LSMA) based on Hyperspectral Imaging (HSI) technology to perform quantitative analysis of pigment components at a sub-pixel level. The results indicate the following. 1) A database of spectral curves for 25 representative Thangka pigments was established, covering 196 bands from 393 to 800 nm VIR-NIR range. 2) The LSMA model successfully separated the 13 pigment components of the Thangka at the sub-pixel level, achieving a Root Mean Square Error (RMSE) of 0.0186, which indicates high classification accuracy. 3) The quantitative analysis reveals that 33.07 % of the area is painted using a single pigment, while 56.01 % is painted using a combination of two pigments. Verdigris (18.56 %), malachite (17.52 %), and cinnabar (10.91 %) are the pigment types with the highest proportions among them. Out of the 521 pigment combinations, verdigris and turquoise (4.55 %), malachite and calcite (4.02 %), minium and cinnabar (2.87 %), and turquoise and malachite (2.82 %) are more commonly used. 4) The application of quantitative analysis methods demonstrates significant potential in painting techniques, authentication processes, and establishing historical dating, among other areas of study.

Security analysis and provision of authentication protocol, based on peer-to-peer structure in IOT platform

The Internet of Things technology allows you to transfer data to any asset through communication networks such as the Internet or intranet. One of the most important problems is security, which includes confidentiality, authenticity validation, and completeness. Among security problems, the most important ones are ensuring authenticity and protecting privacy. The elliptic curve encryption algorithm is famous protocols for maintaining privacy and verifying authenticity. Despite the capabilities they have and provide good security against network attacks, they face challenges such as response time, delay in the authentication and authentication process, as well as the amount of memory consumed. Two-factor authentication (2FA) is a security process where users provide two different authentication factors to authenticate themselves. It also provides a higher level of security than authentication methods that rely on single-factor authentication (SFA). This article presents two novel methods for 2FA that use encryption techniques, scramble architecture, and chord architecture. These methods allow for the authentication process to be initiated by both parties involved in the communication. Three scenarios and three metrics of reaction time, memory usage, and connection latency were used to assess the efficacy of the suggested approach. The average reaction time is improved and is 0.012 s when the lengths of the finger table in the chord structure are increased to 10, 15, and 20. The findings show that the suggested approach reduces computing complexity, reaction time, and communication latency. It has an average reaction time of 0.016 s, an average memory use of 30,360 kb, and an average connection latency of 0.042 s.

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

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

Advancing IoMT security: A two-factor authentication model employing PUF and Fuzzy logic techniques

The rapid integration of Internet of Things technologies in healthcare has catalyzed the development of the Internet of Medical Things, markedly enhanced patient care while posing significant security risks. This paper introduces a comprehensive computational framework to safeguard Internet of Medical Things devices and healthcare providers through a sophisticated registration and authentication process. Our model incorporates cryptographic technologies such as Physical Unclonable Functions, fuzzy extractors, and hash functions to bolster the security during the registration and authentication processes for Internet of Medical Things devices and healthcare providers. The Physical Unclonable Function module enhances device security by producing unique, non-replicable responses for device authentication, significantly reinforcing the system's defense against physical and cloning attacks. Furthermore, the model leverages fuzzy logic for the real-time classification of patient health states, enhancing the decision-making accuracy. A comparative analysis confirms that our model exceeds existing models in communication cost, computational efficiency and security. The proposed scheme has been rigorously tested against various attacks using the Scyther tool. By employing a unique identifier generation method through Physical Unclonable Function and utilizing fuzzy logic for secure data transmission and patient classification, our framework addresses vulnerabilities such as man-in-the-middle, denial of service, impersonation, identity guessing, password guessing and replay attacks, which are prevalent in current Internet of Medical Things frameworks.

The Open Data Newsroom: A Game Approach for Developing Open Data Competencies in Elementary School

Open Data refers to digital data that is made available to anyone with the legal and technical conditions to be freely used, reused, and redistributed. Although it has emerged as a new common with the potential to increase citizen participation and transparency, current literature suggests the lack of skills for managing data and participating in Open Data processes, as one of the main barriers to achieve these benefits. Integrating Open Data in school education has been recognized as key to fostering a larger community able to participate in Open Data ecosystems. This study showcases the design of a role-playing game grounded in authentic learning principles for the development of Open Data competencies in elementary school. The Open Data Newsroom is a game approach that immerses students in solving a mystery with data. In the game, students play Open Data Journalists engaging in a three-phase authentic process to get, understand and deliver data. The learning goal in the game is that students build open data competencies: data literacy and real-world problem solving. A design-based research methodological approach is applied to develop theory based and practically grounded educational designs. Two interventions in Danish schools have been conducted, each one with the participation of seventeen students in 7th to 9th grade and three teachers. Interventions are aimed at iteratively designing and testing the game. The discussion section elaborates on opportunities for redesigning and systematically developing the game as a learning design for Open Data competencies.

Bildung and the Moral Topography of the Self

Abstract The ideal of authenticity that developed in the Romantic era emphasized that both individuals and places find their own authentic process for cultivating their identity and humanity, a notion that has become controversial in modern philosophy. This paper will explore the concepts of authenticity and place and discuss their relevance for Bildung theory today. Based on the works of Charles Taylor, we will argue that Bildung can be seen as constituted by and constitutive of language and moral spaces that go beyond the self and its individuality. In this view, Bildung can be understood as a ‘moral topography of the self’ with both substantial and procedural aspects. However, language and moral spaces (i.e. communities) can both enable and constrict our Bildung processes. Drawing on Rahel Jaeggi, we thus argue that alienation is an equally important concept to understand the complex and dialectical relationship between individuals and communities in our current moment.

Trustworthy fog: A reputation-based consensus method for IoT with blockchain and fog computing

This paper proposes Trustworthy Fog, a novel reputation-based consensus method for Internet of Things (IoT) systems that leverages blockchain and fog computing technologies. By integrating fog computing’s near-end data processing capabilities with blockchain’s immutability and transparency, the proposed method addresses challenges related to latency, device load, and the adaptability of traditional consensus algorithms to resource-constrained environments. A reputation management module evaluates device and node behaviors, facilitating rapid authentication and consensus processes. Distinct reputation calculation schemes for physical devices and fog nodes aim to prevent reputation centralization through periodic resets of reputation values. Based on these values, a lightweight consensus algorithm balances computational capacity and reputation to select leader nodes. Simulations demonstrate the method’s effectiveness in dynamically reflecting device trustworthiness and ensuring fair consensus participation. This research advances IoT blockchain technology, offering a robust solution for the scalability and security challenges inherent in IoT networks.

Physical unclonable functions and QKD-based authentication scheme for IoT devices using blockchain

As the number of Internet of Things (IoT) devices is increasing exponentially, strong security measures are needed to guard against different types of cyberattacks. This research offers a novel IoT device authentication technique to mitigate these challenges by integrating three cutting-edge technologies namely blockchain technology, Quantum Key Distribution (QKD), and Physically Unclonable Functions (PUFs). By utilizing the distinctive qualities of PUFs for device identification and the unrivaled security of QKD for key exchange, the proposed approach seeks to address the significant security issues present in IoT environments. Adopting blockchain technology ensures transparency and verifiability of the authentication process across distributed IoT networks by adding an unchangeable, decentralized layer of trust. An examination of the computing and communication costs reveals that the proposed protocol is effective, necessitating low computational resources that are critical for IoT devices with limited resources. The protocol’s resistance against a variety of attacks is demonstrated by formal proofs based on the Real-Or-Random (ROR) model and security evaluations using the Scyther tool, ensuring the integrity and secrecy of communications. Various threats are analyzed, and the protocol is proven to be secure and efficient from all forms of attacks.

THREAT MODEL FOR PAYMENT SYSTEMS

Payment systems are the backbone of modern economies, and their security is a critical aspect of maintaining user trust and safeguarding financial data. In this context, the development of threat models plays a central role in ensuring the protection of these systems from attacks. One of the most effective methodologies for threat modeling is STRIDE, which helps to structure and systematize the analysis of risks and threats. By utilizing the STRIDE model and incorporating a customized threat model for payment systems, the security of authentication processes and transaction handling can be significantly enhanced.

Serial Multimodal Biometrics Authentication and Liveness Detection Using Speech Recognition with Normalized Longest Word Subsequence Method

Biometric authentication aims to verify whether an entity matches the claimed identity based on biometric data. Despite its advantages, vulnerabilities, particularly those related to spoofing, still exist. Efforts to mitigate these vulnerabilities include multimodal approaches and liveness detection. However, these strategies may potentially increase resource requirements in the authentication process. This paper proposes a multimodal authentication process incorporating voice and facial recognition, with liveness detection applied to voice data using speech recognition. This paper introduces Normalized Longest Word Subsequence (NLWS), a combination of Intersection Over Union (IOU) and the longest common subsequence, to compare the prompted system sentence with the user's spoken sentence at speech recognition. Unlike the Word Error Rate (WER), NLWS has a measurable range between 1 and 0. Furthermore, the paper introduces decision-level fusion in the multimodal approach, employing two threshold levels in voice authentication. This approach aims to reduce resource requirements while enhancing the overall security of the authentication process. This paper uses cosine similarity, Euclidean distance, random forest, and extreme gradient boosting (XGBoost) to measure distance or similarity. The results show that the proposed method has better accuracy compared to unimodal approaches, achieving accuracies of 98.44%, 98.83%, 97.46%, and 99.22% using cosine similarity, Euclidean distance, random forest, and XGBoost calculations. The proposed method also demonstrates resource savings, reducing from 5.19 MB to 0.792 MB, from 7.3294 MB to 1.9437 MB, from 6.6512 MB to 1.3284 MB, and from 7.8632 MB to 2.1517 MB in different distance or similarity measurements

A Secure Key Exchange and Authentication Scheme for Securing Communications in the Internet of Things Environment

In today’s advanced network and digital age, the Internet of Things network is experiencing a significant growing trend and, due to its wide range of services and network coverage, has been able to take a special place in today’s technology era. Among the applications that can be mentioned for this network are the field of electronic health, smart residential complexes, and a wide level of connections that have connected the inner-city infrastructure in a complex way to make it smart. The notable and critical issue that exists in this network is the extent of the elements that make up the network and, due to this, the strong and massive data exchanges at the network level. With the increasing deployment of the Internet of Things, a wide range of challenges arise, especially in the discussion of establishing network security. Regarding security concerns, ensuring the confidentiality of the data being exchanged in the network, maintaining the privacy of the network nodes, protecting the identity of the network nodes, and finally implementing the security policies required to deal with a wide range of network cyber threats are of great importance. A fundamental element in the security of IoT networks is the authentication process, wherein nodes are required to validate each other’s identities to ensure the establishment of secure communication channels. Through the enforcement of security prerequisites, in this study, we suggested a security protocol focused on reinforcing security characteristics and safeguarding IoT nodes. By utilizing the security features provided by Elliptic Curve Cryptography (ECC) and employing the Elliptic Curve Diffie–Hellman (ECDH) key-exchange mechanism, we designed a protocol for authenticating nodes and establishing encryption keys for every communication session within the Internet of Things. To substantiate the effectiveness and resilience of our proposed protocol in withstanding attacks and network vulnerabilities, we conducted evaluations utilizing both formal and informal means. Furthermore, our results demonstrate that the protocol is characterized by low computational and communication demands, which makes it especially well-suited for IoT nodes operating under resource constraints.

Vehicle And Driver Recognition for Access Control

The following paper proposes an advanced vehicle and driver recognition system to enhance security at vehicular entry-exit points in response to the growing needs of more efficient and secure access control systems at these checkpoints. The proposed system will integrate license plate recognition and facial recognition technologies using state of-the-art machine-learning models from Ultralytics: YOLOv8 and EasyOCR, respectively. It works in three stages: license plate detection, character alpha-numeric identification, and then driver identity with facial recognition. Hence, the two-tier authentication process based on license plate detection and facial identification ensures further prevention from unauthorized entry. Preliminary evaluation of the system also produced very high values for precision, recall, and the mean average precision, indicating that the VDR system offers substantial advances in effectiveness and security when compared to conventional methodologies for access control.

Enhancing Recycled Concrete Performance by Using Chemical Activators

The need for sustainable and eco-friendly construction materials and processes is rising significantly. In these circumstances, recycling of concrete can be a lifesaver. Although, various recycling techniques have already been introduced. But none of them can fulfill the term recycle refers to. They mostly focus on merely reusing coarse aggregates (CA) or fine aggregates (FA). To come out of this, this paper is focused not only on the reuse of both coarse and fine materials (RCA, RFA) but also on introducing a new process in which those materials can be used in their old form, as recycling refers to. In this experiment, we use all kinds of materials recollected from recycled concrete (fine, coarse, recycled cement particles). Those materials are vetted into two parts. One is for recycling coarse aggregates and one is for rebuilding concrete using recovered fine, coarse, and cement particles. First, we discuss the collection and treatment system procedure of recycled demolished concrete. The second part will consist of various standardized tests for reuse and evaluation. The third part will describe the treatment needed for the materials for chemical (Using Na2SiO3, NaOH, and Na2CO3) reactivation. Finally, we will test the recycled material’s performance as concrete and establish an authentic process of recycling and reusing concrete materials. With that, we obtain the most optimum usage of recycled concrete aggregate mixing ratio Cement: CA (coarse aggregate): RCA (recycled coarse aggregate): FA (fine aggregate): RFA (recycled fine aggregate) is 1: 2: 2: 0.8: 1.2: 0.8 (With molar solution of Na2SiO3 and NaOH + KOH) and other ration of mixing with their respected properties (Compressive Strength).

A designated private set based trapdoor authentication scheme for privacy preserving trust management in decentralized systems

Authentication is crucial for network system security, relying on methods such as passwords, ID cards, biometrics, and behavioral characteristics. The conventional centralized authentication may lead to potential performance bottlenecks and privacy risks such as key exposure, single point of failure. Decentralized authentication systems using cryptographic techniques aim to address these issues but often tradeoff between flexibility and communication efficiency. In this paper we propose a new cryptographic concept called designated private set-based trapdoor authentication (DPSBTA) for flexible and efficient trust management in decentralized systems. DPSBTA eliminates the need for a trusted authority, with users’ access privileges defined by their private sets. During the authentication process, each server can designate an element set and only if a user holds adequate elements which are contained in the designated set can he obtains a credential from the server. The key features of DPSBTA include: decentralized trapdoor authentication management, without a trusted authority, conducted in a double threshold manner; privacy preservation, as servers do not know users’ element holdings or credential generation; round-optimal communication, with only two rounds of interaction between users and servers. We present the generic construction, security models, and concrete algorithms with correctness proof. The theoretical proof and the performance evaluations demonstrate the tangible security and high efficacy of the proposed DPSBTA.

Enhancing biometric system selection: A hybrid AHP-neutrosophic fuzzy TOPSIS approach

A biometric system is essential in improving security and authentication processes across a variety of fields. Due to multiple criteria and alternatives, selecting the most suitable biometric system is a complex decision. We employ a hybrid approach in this study, combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Analytic Hierarchical Process (AHP). Biometric technologies are ranked using the TOPSIS method according to the relative weights that AHP determines. By applying the neutrosophic set theory, this approach effectively handles the ambiguity and vagueness inherent in decision-making. Fingerprint, face, Iris, Voice, Hand Veins, Hand geometry and signature are the seven biometric technologies that are incorporated in the framework. Seven essential characteristics are accuracy, security, acceptability, speed and efficiency, ease of collection, universality, distinctiveness used to evaluate these technologies. The model seeks to determine which biometric technology is best suited for a particular application or situation by taking these factors into account. This technique may be applied in other domains in the future.

DESIGN OF E-MODULE TEACHING MATERIALS BASED ON LOCAL WISDOM FOR BIPA LEARNING

This research aims to design teaching materials in e-modules that integrate local wisdom to increase the effectiveness of learning Indonesian for Foreign Speakers (BIPA). In the era of globalization, understanding local culture is important to support a more contextual and authentic language learning process. The e-module developed in this research focuses on introducing and understanding local Indonesian culture, such as customs, traditional arts, and social values. This development model uses the Borg & Gall development model, namely preliminary study, research planning, design development, preliminary field test, revision of limited field test results, main field test, revision of wider field test results, feasibility test, final revision of feasibility test results, and dissemination and implementation of the final product. Data collection at the pre-development stage was carried out using a questionnaire validation instrument. The type of data in this research is qualitative data. Qualitative data was obtained from expert test questionnaire validation in the form of data resulting from comments, criticism, and suggestions in the form of nonverbal data at the pre-development stage.

Highly efficient Dy3+ activated Sr9Al6O18 nanophosphors for W-LEDs, optical thermometry and deep learning-based intelligent system for personal identification applications

A novel single-phase Dy3+ activated Sr9Al6O18 nanophosphors (NPs) was synthesized using a solution combustion method with urea as the fuel. When phosphors are excited at 350 nm, their photoluminescence (PL) spectra exhibit strong emissions at 484 and 574 nm, which are associated with the 4F9/2→6H15/2 and 4F9/2→6H13/2 transitions. These emissions are attributed to magnetic and electric dipole allowed transitions, correspondingly. It is found that dipole–dipole interactions are the main source of concentration quenching and energy transfer among the activator ions. The prepared NPs show thermal stability up to 480 K having an activation energy (Ea) of 0.2437 eV, as determined by temperature-dependent photoluminescence (TDPL). With a remarkable internal quantum efficiency (IQE) of 66.14 %, the optimized phosphor achieves notable relative sensitivity (Sr) and absolute sensitivity (Sa), estimated to be 2.65 % K−1 and 1.38×10−3 K−1 at 300 K, respectively. Thus, Sr9Al6O18:Dy3+ is a promising material for non-contact optical thermometry applications. Latent fingerprints (LFPs) and latent lip prints (LLPs) can be efficiently produced on a variety of surfaces by using the optimized NPs with the simple powder dusting technique. The prints are made visible under UV light with a 365 nm wavelength. The You Only Look Once version 8 (YOLOv8x) approach analyzes the overall morphological correlations of different minutiae to identify essential minutiae that indicate the identities of people from FPs. Splitting and matching multi-scale fingerprints (FPs) features improves the validity of the authenticating findings. The results show that the recommended method, which improves the entire FP authentication process, offers exceptional accuracy, resilience and speed. The findings provide strong support for the implementation of the optimized NPs in photonic devices, optical thermometers, and advanced forensic applications.

Soulbound Tokens: Enabler for Privacy-Aware and Decentralized Authentication Mechanism in Medical Data Storage

Context: The digitalization of the healthcare sector faces significant challenges due to the diverse representation of data and their distribution across various hospitals. Moreover, security is a key concern as healthcare-related data are subject to the legal obligations of GDPR and similar data protection legislations. Standardization efforts like HL7 have been implemented to enhance data interoperability. However, authentication still remains a critical issue, even significant challenges. Aim: This research aims to improve and strengthen the authentication process by introducing a novel architecture for decentralized authentication. Additionally, it proposes a new approach to decentralized data management, which is crucial for handling sensitive medical data efficiently. Methodology: The proposed architecture adopts a user-centric approach, utilizing Self-Sovereign Identity (SSI). It introduced a new non-fungible token (NFT) type called Soulbound token (SBT) in the medical context, which will facilitate user authentication across different hospitals, effectively creating a federation of interconnected institutions. Results: The implementation of the proposed architecture demonstrated a significant reduction in authentication time across multiple hospitals. The use of SBT ensured secure and seamless user authentication, enhancing overall system interoperability and data security. The decentralized approach also mitigated the risks associated with centralized authentication servers. Conclusion: The study successfully presents a novel decentralized authentication architecture for the healthcare domain, leveraging SSI and SBTs. This approach not only accelerates the authentication process but also enhances data security and interoperability among hospitals. Future research should explore the scalability of this architecture and its application in other sectors requiring stringent data security measures.

What Makes News (Seem) Authentic on Social Media? Indicators from a Qualitative Study of Young Adults

This study investigates how young adults in Denmark authenticate news they come across on social media. Based on data from 300 entries of news diaries and interviews with 20 participants, we found that this group uses reputation, endorsement, and consistency as the leading shortcuts in the context of news exposure. Though different conceptually, authenticity is linked to trust, and this group perceives Danish national media as the most credible source of information. Moreover, they are self-confident about distinguishing news from the noise caused by misinformation and rarely complete a two-step authentication process to (double) check content. Our theoretical standpoint departs from the normative perspective that journalism is essential for shaping informed citizenship. Thus, while brand reputation is paramount for legacy Danish media to connect with their audience, this centric news consumption might push people away from learning about other countries’ issues.

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.