Authentication Process Research Articles

Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios

Machine learning has several essential applications, including classification and recognition. Both people and objects may be identified using the Machine learning technique. It is particularly important in the verification process since it recognizes the characteristics of human eyes, fingerprints, and facial patterns. With the advanced technology developments, nowadays, Facial recognition is used as one of the authentication processes by utilizing machine learning and deep learning algorithms and it has been the subject of several academic studies. These algorithms performed well on faces without masks, but not well on faces with masks. since the masks obscured the preponderance of the facial features. As a result, an improved algorithm for facial identification with and without masks is required. After the Covid-19 breakout, deep learning algorithms were utilized in research to recognize faces wearing masks. Those algorithms, however, were trained on both mask- and mask-free faces. Hence, in this, the cropped region for the faces is only used for facial recognition. Here, the features were extracted using the texture features, and the best-optimized features from the glow worm optimization algorithm are used in this paper. With these features set, the hybrid Dolphin glow worm optimization is used for finding the optimal features and spread function value for the neural network. The regression neural network is trained with the optimized feature set and spread function for the face recognition task. The performance of the suggested method will be compared to that of known approaches such as CNN-GSO and CNN for face recognition with and without masks using accuracy, sensitivity, and specificity will next be examined.

CAPP: Context‐Aware Privacy‐Preserving Continuous Authentication in Smartphones

ABSTRACTThe pervasive use of smart devices in everyday life has increased the risk of theft and cyberattacks, necessitating robust and continuous behavioral authentication techniques during every interactive session. Traditional authentication methods are insufficient to address these evolving threats, as they often fail to maintain security throughout the session. To address this challenge, we propose a continuous authentication scheme that leverages touch and motion sensors, incorporating user context to enhance security. However, building an effective authentication model requires creating a behavioral template by aggregating data from multiple users. Moreover, outsourcing sensitive sensor data to third‐party servers for processing introduces the risk of privacy breaches, such as behavioral profiling or the theft of personal information. To mitigate these risks, we designed CAPP—a context‐aware privacy‐preserving data outsourcing scheme for continuous authentication during live sessions. CAPP employs Format Preserving Encryption (FPE) to encrypt the sensor data, ensuring that the data's format remains intact, which allows the machine learning model to function with minimal loss of accuracy. Our solution enables secure and efficient authentication by creating a model on the server side using data from motion sensors like the accelerometer, gyroscope, and magnetometer. Our experimental results demonstrate the effectiveness of the proposed scheme, achieving an accuracy of 87.57% and an Equal Error Rate (EER) of 12.15%, while requiring only 1.69 ms to authenticate a user. These results show that CAPP preserves user privacy and provides a rapid and reliable authentication process, outperforming existing state‐of‐the‐art methods.

OPTIMIZING AUTHENTICATION SECURITY IN INTELLIGENT SYSTEMS THROUGH VISUAL BIOMETRICS FOR ENHANCED EFFICIENCY

Context. The primary objective of this article is to explore aspects related to ensuring security and enhancing the efficiency of authentication processes in intelligent systems through the application of visual biometrics. The focus is on advancing and refining authentication systems by employing sophisticated biometric identification methods. Objective. A specialized intelligent system has been developed, utilizing a Siamese neural network to establish secure user authentication within the existing system. Beyond incorporating fundamental security measures such as hashing and secure storage of user credentials, the contemporary significance of implementing two-factor authentication is underscored. This approach significantly fortifies user data protection, thwarting most contemporary hacking methods and safeguarding against data breaches. The study acknowledges certain limitations in its approach, possibly affecting the generalizability of the findings. These limitations provide avenues for future research and exploration, contributing to the ongoing evolution of authentication methodologies in intelligent systems. Method. The two-factor authentication system integrates facial recognition technology, employing visual biometrics for heightened security compared to alternative two-factor authentication methods. Various implementations of the Siamese neural network, utilizing Contrastive loss function and Triplet loss function, were evaluated. Subsequently, a neural network employing the Triplet loss function was implemented and trained. Results. The article emphasizes the practical implications of the developed intelligent system, showcasing its effectiveness in minimizing the risk of unauthorized access to user accounts. The integration of contemporary authentication methodologies ensures a secure and robust user authentication process. Conclusions. The implementation of facial recognition technology in authentication processes has broader social implications. It contributes to a more secure digital environment by preventing unauthorized access, ultimately safeguarding user privacy and data. The study’s originality lies in its innovative approach to authentication, utilizing visual biometrics within a Siamese neural network framework. The developed intelligent system represents a valuable contribution to the field, offering an effective and contemporary solution to user authentication challenges.

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.

A Secure and Decentralized Using Blockchain and Ring‐Based Cryptosystem

ABSTRACTThe advancement of Intelligent Transport Systems (ITSs) and the Internet of Vehicles (IoVs) introduces significant security challenges, particularly in ensuring secure communication between vehicles and vehicles (V2V), vehicles and Road Side Units (V2RSU). These vulnerabilities could potentially compromise the integrity of vehicular networks, making robust security measures essential. This paper aims to develop a secure communication framework for IoV environments that addresses these security concerns by enhancing authentication and encryption processes. The proposed approach combines the Ring‐Based Degree Truncated Polynomial Cryptosystem (RNTPC) with a Blockchain framework incorporating an enhanced proof‐of‐authentication (BEPoAuth) consensus algorithm. The approach is initiated from the registration phase and managed by a Trusted Authority (TA), ensuring secure vehicle and RSU registration. Authentication is carried out in two ways: V2V authentication through vehicle signatures and V2RSU authentication via batch verification of cluster members. Group keys are generated using RNTPC for secure communication, and blockchain technology is integrated to secure transactions within the IoV environment. The proposed methodology is evaluated using various performance metrics, demonstrating a 15%–20% improvement in throughput compared to existing techniques such as Practical Byzantine Fault Tolerance (PBFT), Secure and Highly Efficient Blockchain PBFT Consensus Algorithm (SG‐PBFT), credit‐based PBFT consensus algorithm (CPBFT) and Geographic‐PBFT (G‐PBFT). The system also exhibited minimal computational and communication latency while enhancing V2V and V2RSU communication efficiency. The RNTPC‐BEPoAuth framework offers a robust and secure solution for IoV communications, significantly outperforming existing methods in terms of throughput and efficiency. This approach provides a reliable foundation for secure ITSs, addressing critical security concerns in vehicular networks.

Th e concept of human image in the artistic heritage of A.A. Zinoviev

The work analyzes the concepts of human representation in the art trilogy of A.A. Zinoviev. The specific genre features of his novels are described, which dictate a new form of representation of a person completely impersonal and stereotyped. The supra-individual nature of his characters is emphasized, which are generally represented by three varieties: ibans, homos, postmen, each of which has its own extreme poles. Specific techniques have been identified that distinguish the novel heroes of the artistic space A.A. Zinoviev: the heroes of the trilogy are compared with the options for representing the character in a classic novel, on the basis of which specific artistic techniques of the image are distinguished, characteristic only of the sociological novels of A.A. Zinoviev. The conclusions obtained as a result of the analysis of typed heroes of the art trilogy allow us to talk about the creation of a new genre of the novel — sociological, in which the concept of human representation undergoes deep author's processing, as a result, the hero is not interesting as a person, but as the embodiment of a particular idea, and conditional impersonal heroes represent a world without conventions, in all its potential possibilities, this is the phenomenon of A.A. Zinoviev, which turned the conditional form of the novel into a statistically life-like diagnostic social sentence to modern society.

Use of QRCode and Digital Signature Using The DSA Method to Authenticate Student Academic Documents

Verification of digital documents is no longer done conventionally but is also done digitally, such as signatures on documents. A signature is a means of authentic evidence as well as proving a person's identity, is a means of proving the authenticity or validity of an agreement or approval for something in a document issued by two or more parties so that it can be used as a solution to verify the integrity of valid data in a document. Application of signatures Digital is also commonly used at the North Sumatra Islamic University (UINSU) Medan, especially in student study documents. Even though UINSU Medan has implemented digital signatures, this application is still a signature image obtained through scanning or photos and manually inserted into a document so that other people can easily reuse the signature image and it can even be misused. To facilitate the digital signature process, the code generated from the signature formation process is entered into a QR code so that it is easy to use to carry out the electronic document authentication process. Researchers will use QR codes and digital signatures to authenticate student documents using the DSA method. This research will be implemented into an application model with PHP and MySQL programming. The student document authentication application model using the DSA method works well as evidenced by the QRCode which contains document token information which is different for each document so that it cannot be duplicated by non-owners of the document.

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.

CHALLENGES IN IOT SECURITY: UNRESOLVED ISSUES AND KEY REQUIREMENTS FOR SAFEGUARDING INFORMATION

In contemporary society, one of the pressing challenges in the realm of information security is the protection of what is commonly referred to as the Internet of Things (IoT). Recent studies indicate a notable rise in demand for IoT devices over the past three to four years, highlighting an increasing reliance on interconnected technologies in our daily lives. This article explores three fundamental security conditions, emphasizing the unique requirements of IoT systems. The key requirements under examination are confidentiality and authentication processes, which are paramount in safeguarding user data and ensuring secure interactions between devices. Furthermore, the article sheds light on several unresolved issues concerning information security within the IoT landscape, such as vulnerabilities stemming from inadequate device security protocols, the challenge of data privacy in a hyper-connected environment, and the need for comprehensive regulatory frameworks. Addressing these concerns is essential for fostering a secure and trustworthy IoT ecosystem as it continues to evolve and integrate further into various aspects of everyday life. Keywords: internet of Things (IoT), information Security, privacy, authentication, security Conditions

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 article 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).