Identity Verification Process Research Articles

DESAFIOS DE SEGURANÇA NAS INSTITUIÇÕES FINANCEIRAS DIGITAIS: ATAQUES MALWARE E MECANISMOS DE SEGURANÇA

Financial institutions face increasing challenges related to information security due to the intensification of cyber attacks, particularly threats such as malware and ransomware. This study aims to critically analyze these risks and evaluate the effectiveness of current technological tools and current regulations in preserving the supervision and confidentiality of digital financial systems. To this end, we adopted an exploratory and qualitative approach, based on a comprehensive literature review. Research has revealed that the adoption of artificial intelligence, especially through neural networks and machine learning algorithms, has proven effective in identifying attacks and containing malicious behavior in real time. Intrusion detection and prevention systems (IDS/IPS), combined with multifactor authentication protocols and biometric technologies, have brought benefits to the identity verification and fraud protection process. It is concluded that effectively combating malware attacks requires the adoption of advanced security mechanisms, such as machine learning, multifactor authentication and blockchain, integrated with artificial intelligence. This combination enables rapid detection and response to suspicious behavior, strengthening data protection in digital financial institutions.

Biometric and Geographic Information-based Identity Authentication Method

With the ongoing advancements in computer technology and mobile internet, mobile payment has become an integral component of daily life. Identity authentication technology serves as a critical measure to ensure the security of mobile payments. This paper addresses the issue of low security associated with single-factor identity authentication while highlighting the substantial overhead involved in multi-factor authentication methods. We propose a novel identity authentication method that integrates biometric data and geographic information. In this framework, the identity authentication server first performs system initialization, configures system parameters, and stores them securely. Subsequently, the identity authentication client submits an authentication request to the server and completes its registration process. Finally, the server processes this request from the client, conducts user identity verification, and returns an authentication result. This approach effectively mitigates risks posed by attackers attempting to falsify biometric data or geographic information during the identity verification process and offers robust services for biometric- and geography-based identity authentication.

Integrating Blockchain and AI for Optimized Digital Identity Verification

The rapid evolution of the digital age the use of blockchain and artificial intelligence to improve digital identification methods responds to the challenges of ensuring the security and reliability of personal data. Traditional verification methods are increasingly inadequate, evidenced by the alarming escalation in identity fraud incidents. In response, this research explores an innovative integration of blockchain technology and artificial intelligence to fortify the security, efficiency, and privacy of digital identity verification processes. Blockchain's decentralized and immutable ledger offers a new paradigm for secure and manageable digital identities, while AI's capabilities in automating complex processes and detecting fraud through pattern analysis provide a complementary, synergistic solution. This study addresses the challenges of traditional verification systems, highlights the potential of integrating blockchain and artificial intelligence, and presents a methodology for creating an identity verification information system. Results validated through a series of tests, demonstrating significant improvements in verification accuracy, speed, and resistance to fraudulent activities. Despite promising outcomes, challenges such as scalability, privacy, and integration complexity were identified, with proposed solutions aiming to address these issues for the practical implementation and long-term success of integrated digital identity verification systems.

Applying Biometric Technology in School Attendance and Security Management

In today's digital era, security and efficiency in attendance management in the school environment are very important. Biometric technology has become a new trend in improving security and optimizing the attendance recording process, replacing manual systems prone to errors and fraud. This research aims to analyze the effectiveness of applying biometric technology in school attendance and security management. Specifically, this research focuses on evaluating how this technology can reduce cheating, increase the accuracy of attendance data, and strengthen school security systems. This research uses quantitative methods with a case study approach at five schools implementing a biometric system. Data was collected through surveys of school administrators and direct observation of the registration and identity verification processes at these schools. Data analysis was carried out using descriptive and inferential statistics. This research shows that using biometric technology significantly increases the efficiency and accuracy of the attendance recording system. Student attendance is recorded quickly and accurately, with virtually no cheating. This technology also strengthens the security system by ensuring that only verified individuals can access school facilities. This research concludes that applying biometric technology in school attendance and security management has proven very effective. This technology optimizes attendance and improves security, providing a safe environment for students and staff. The research recommends wider adoption of biometric technology in other schools to improve security and operational efficiency.

Decentralized Identity Verification in Metaverse Auditing Using Blockchain Technology

This research paper presents a comprehensive review of decentralized identity verification in the context of metaverse auditing, leveraging blockchain technology. With the rapid evolution of virtual environments and the emergence of the metaverse, ensuring trust, transparency, and security becomes paramount. Traditional centralized identity verification systems face challenges in such decentralized and dynamic environments. Blockchain technology offers promising solutions by providing a transparent, immutable, and decentralized ledger for identity management and verification. Conceptual Analysis research methodology used in this research paper because theoretical and conceptual clarity are crucial for understanding complex phenomena. The paper begins by exploring the concept of decentralized identity management systems and their significance in virtual environments. It discusses various challenges associated with traditional identity verification methods and highlights the potential of blockchain-based solutions in addressing these challenges. Through a systematic literature review, the paper examines existing research and developments in decentralized identity verification, metaverse auditing, and blockchain technology. Key topics covered include the architecture of decentralized identity systems, consensus mechanisms for identity verification on blockchain networks, privacy-preserving techniques, and interoperability standards for cross-platform identity management. Furthermore, the paper investigates the role of smart contracts and decentralized applications (DApps) in facilitating identity verification processes within the metaverse. The review also discusses the potential implications of decentralized identity verification in enhancing trust and security in virtual economies, enabling seamless user experiences, and fostering innovation in digital asset management. Moreover, it identifies current gaps and challenges in the field and proposes future research directions to address these issues. Overall, this research paper contributes to the understanding of decentralized identity verification in metaverse auditing using blockchain technology. It serves as a valuable resource for researchers, practitioners, and policymakers interested in exploring the intersection of decentralized identity, virtual environments, and blockchain technology. The insights provided aim to stimulate further research and innovation in this rapidly evolving domain.

Analyzing the Role of Blockchain in Identity and Access Management Systems

In today's digital world, protecting identities and securing access to sensitive data are crucial tasks. Traditional centralized Identity and Access Management (IAM) systems are vulnerable to data breaches and unauthorized intrusions. Blockchain technology has emerged as a promising solution to bolster IAM systems by decentralizing control and enhancing security measures. This paper explores the integration of blockchain technology into IAM frameworks to address the shortcomings of centralized systems. It examines the fundamental principles of blockchain, highlighting its immutability, decentralization, and cryptographic security features. By leveraging these attributes, blockchain-based IAM systems offer a robust infrastructure for identity verification, authentication, and authorization processes. Various use cases of blockchain in IAM are discussed, including self-sovereign identity, decentralized authentication protocols, and secure data sharing mechanisms. The paper elucidates how blockchain enhances trust and transparency in identity management, granting individuals greater control over their personal data while ensuring privacy and security. It also scrutinizes the challenges and considerations associated with implementing blockchain-based IAM solutions, such as scalability, interoperability, and regulatory compliance. Additionally, the paper explores potential future developments and trends in this evolving field, emphasizing the importance of collaboration between industry stakeholders and regulatory bodies to foster innovation while addressing security and privacy concerns. In summary, this paper underscores the transformative potential of blockchain technology in fortifying IAM systems, leading to a more secure and resilient digital ecosystem.

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

Immutable Identity Validation Using Soul bound Token Abhishek Sharma,

The "Immutable Identity Validation System: A Blockchain and Soulbound Token Approach" paper introduces an innovative method for verifying digital identities. By combining blockchain technology and Soulbound Tokens (SBTs), it enhances security and privacy in identity verification processes. SBTs, designed as non-transferable and tamper-proof digital assets, play a crucial role in bolstering the security and reliability of the system. Their unique properties ensure that user privacy is prioritized while also facilitating swift verification through blockchain transparency. This approach addresses the growing demand for robust identity verification solutions, particularly in sectors like education and corporations, where fraud prevention and streamlined processes are critical. The methodology involves various steps, including SBT generation, blockchain integration, user control mechanisms, and stringent security measures. Both frontend and backend development, along with the integration of blockchain using tools like Ganache and decentralized data storage through IPFS, contribute to building a secure and user-friendly system. Thorough testing, documentation, and knowledge transfer are essential to ensuring the system's reliability, security, and compliance with legal standards. Ultimately, the goal is to establish a globally accepted framework for identity verification in today's interconnected digital landscape. Keywords:; privacy; security; Blockchain; Cryptography; Decentralization; Soul Bound Token (SBT), distributed ledger.

Representing the adarsh biometric balak or the ideal biometric child: locating poor children’s care work in the Aadhaar welfare state

ABSTRACT This article spotlights the role of children from poor, underprivileged families who are drawn into care work to facilitate inclusion for their families in food security by performing fingerprint authentication. Owing to the youthful suppleness of their bodies, children’s fingerprints are more likely to succeed in fulfilling biometric requirements. They are also more compliant in having their schooling or playtime disrupted by the unpredictable and time-consuming biometric authentication process. Children’s names may also be added on ration cards to facilitate food security for relatives other than their parents who depend on the biometric viability of children’s bodies and time for welfare. Supplementing ethnographic data with the state’s data of the biometric authentications recorded in the Aadhaar database of India, I make visible the poor child as an ‘ideal biometric child’ in enabling the biometric assemblage of the welfare state to care for its citizens.

Towards data storage scheme in blockchain based serverless environment: AES encryption and decryption algorithm approach

In today's digital age, data storage has become an increasingly important issue. The exponential growth of data has led to the need for secure and private storage solutions. Unfortunately, without a standardized protocol, the security and privacy of sensitive data can be a challenge. However, blockchain technology has emerged as a promising solution for secure data storage. Blockchain's decentralized and immutable nature provides a comprehensive solution for the security and privacy of all types of data. In this research, we propose an innovative framework that leverages the benefits of blockchain technology to securely handle and store data in a serverless environment of distributed nodes. To ensure the highest level of security, we evaluated four encryption algorithms - Blowfish, RC4, DES, and AES - for storing data in a permissioned blockchain network. We found that AES encryption and decryption algorithms provide the best solution for creating a decentralized, immutable coordinate system. Our proposed framework is based on a permissioned blockchain network that enables multiple users to join the network through suitable identity verification processes, and each user is assigned certain special and designated permissions to perform actions. This framework provides a secure and efficient solution for the storage of all types of data, ensuring privacy and security. Our proposed framework offers an innovative solution for secure data storage and management in a decentralized environment. This research has practical implications for organizations that need to store sensitive data securely, and it also contributes to the ongoing development of blockchain technology.

Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN

Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection.

Enhancing Consensus Security and Privacy with Multichain Ring Signatures Based on HotStuff

The paper introduces a novel consensus algorithm named MRPBFT, which is derived from the HotStuff consensus protocol and improved upon to address security deficiencies in traditional consensus algorithms within the domain of digital asset transactions. MRPBFT aims to enhance security and privacy protection while pursuing higher consensus efficiency. It employs a multi-primary-node approach and a ring signature mechanism to reinforce security and privacy preservation features in the consensus system. This algorithm primarily focuses on two main improvements: Firstly, it proposes the ed25519LRS signature algorithm and discusses its anonymity for transaction participants and the non-forgeability of signature information in the identity verification and message verification processes within the consensus algorithm. Secondly, the paper introduces MPBFT asynchronous view changes and a multi-primary-node mechanism to enhance consensus efficiency, allowing for view switching in the absence of global consensus. With the introduction of the multi-primary-node mechanism, nodes can be flexibly added or removed, supporting parallel processing of multiple proposals and transactions. Finally, through comparative experiments, the paper demonstrates that the improved algorithm performs significantly better in terms of throughput and network latency.

Unraveling the effects of occupational identity verification, self-esteem and identity salience on managers’ mental health: examining psychological distress and depression in the workplace

Purpose Managers play a crucial role in organizations. They make decisions that directly influence organizational success and significantly impact employees’ mental health, development and performance. They are responsible for ensuring the financial well-being and long-term sustainability of organizations. However, their mental health is often overlooked, which can negatively affect employees and organizations. This study aims to address managers’ mental health at work, by examining specifically the direct and indirect effects of identity verification on their psychological distress and depression through self-esteem at work. The study also aims to examine the moderating as well as moderated mediation effects of identity salience. Design/methodology/approach A sample of 314 Canadian managers working in 56 different companies was studied, using multilevel analyses. Findings The findings showed that the verification of managers’ identity vis-à-vis recognition is positively associated with psychological distress and depression. Self-esteem completely mediates the association between low identity verification vis-à-vis work control and psychological distress, and also the association between low identity verification vis-à-vis work control and superior support and depression, while it partially mediates the association between low identity verification vis-à-vis recognition and depression. Practical implications This study can also help both managers and human resource management practitioners in understanding the role of workplaces in the identity verification process and developing relevant interventions to prevent mental health issues among managers at work. Originality/value This study proposed a relatively unexplored approach to the study of managers’ mental health at work. Its integration of identity theory contributes to expanding research on management and workplace mental health issues.

Spline Interpolation and Deep Neural Networks as Feature Extractors for Signature Verification Purposes

Digital security in modern systems very often uses biometric, and increasingly, new implementations appear. Such applications can be found everywhere, even when picking up the package from courier, we certify its receipt through our signature on the tablet. However, verification of this form is not one of the simplest elements in information processing systems. Given the different sizes, angles, or writing conditions that may affect its stability, new methods to evaluate signatures are constantly needed. In this article, we propose the use of spline interpolation and two types of artificial neural networks to verify the identity of a person based on selected local and global features extracted from the image of a signature. Global features are extracted concerning interpolation and graphic processing methods, while local features are verified using convolutional neural networks. Both sets of features are used in the identity verification process. The article presents the model of the operation together with experiments, taking into account various parameters of the proposed extraction. We have reached an accuracy of 87.7% on the SVC2004 database.

How AI can improve identity verification and access control processes

How AI can improve identity verification and access control processes

Intergenerational Transmission of Entrepreneurial Identity within Entrepreneurial Families

Entrepreneurial families play a critical role in the next generation’s interest in and desire for entrepreneurship. Because entrepreneurship is an identity construction process, the influence of entrepreneurial parents on the construction or nonconstruction of their children’s entrepreneurial identity (EI) needs to be explored. This paper provides a theoretical framework of the children’s EI construction process. It specifically suggests that entrepreneurial parents’ emotional expression resulting from the identity verification process and emotion regulation in family communication can influence the children’s identity construction processes. When parents’ EI is verified and its resulting positive emotion is expressed in a conversation- and conformity-oriented environment, it sends children the message that EI can enhance identity motives. This message leads to the activation of EI assimilation and maintenance for the children. We contribute to existing research by going beyond seeing parents only as role models by highlighting the interplay between parents’ EI enactment and child’s identity construction and explaining the family communication influence.

Family arrangements, identity verification, and happiness

We study how the family structural arrangements individuals inhabit, the cultural meanings that accompany these structures, and individuals' experiences of identity verification within these structures are related to their general happiness. We use data (N = 1304) from the 2014 General Social Survey Identity Module to study the normative and non-normative family arrangements in which individuals reside, their experience of verification of the parent/non-parent identity, and their happiness. We find that parent/non-parent identity verification is positively associated with happiness for normative family arrangements but not non-normative family arrangements. We discuss how social structural arrangements and a culture that supports them may assist the identity verifying outcome of happiness, while structural arrangements and a culture that is not as supportive of certain family forms may undermine the identity verifying outcome of happiness. The findings provide insights into how social structure and culture at the macro level coalesce with the identity verification process at the micro level in affecting the emotional tenor of people's lives.

Non-conventional Bio-Cryptic DOST Features for Private Cloud Secure Access Using Machine Learning Algorithms

Security issues in cloud computing is always challenging task for the researchers and practitioners. Especially in private cloud security is one of the critical issues to grant access to the remote server. Biometric authentication process will be one of the best solutions to grant access for private cloud server. This paper proposes a novel integrated technique for the secure cloud access by considering Bio-cryptic with DOES features, designated as ‘Bio-Cryptic DOST’ (BCDOST) method. The method is implemented in Matlab and trained with 6000 data samples and tested using 5000 biometric data samples that includes, finger, face, iris and palm biometric features. Overall, 98.7% has obtained on a K-fold cross-validation (k=5), and also the results was compared with the present DOST and 4 different customary strategies. supported the results, it's over that, the proposed methodology performed well with relation to accuracy and computation-time.

Cancelable Multi-biometric Template Generation Based on Dual-Tree Complex Wavelet Transform

In this article, we introduce a new cancelable biometric template generation layout depending on selective encryption technology and Dual-Tree Complex Wavelet Transform (DT-CWT) fusion. The input face biometric is entered into the automatic face-segmentation (Viola-Jones) algorithm to detect the object in a short time. Viola-Jones algorithm can detect the left eye, right eye, nose, and mouth of the input biometric image. The encoder can choose the left or right eye to generate a cancelable biometric template. The selected eye image of size M × N is XORed with the created pseudo-random number (PRN) matrix CM × N to provide an initial primary image. Arnold Cat Map (ACM) is used to scramble the PRN matri pixel by pixel for primary image encryption keeping the pixel values themselves unchanged. Finally, the ACM scrambled eye images are encrypted using Advanced Encryption Standard (AES) algorithm for database storage. Moreover, the PRN and Initial Key (IK) of the AES algorithm for the same person are used for the biometric model authentication process for database storage. The IK is generated from the right finger and right eye fusion using DT-CWT technique. To avoid injury of a single eye, both right-eye and left-eye models are developed.

Efficient Joint Key Authentication Model in E-Healthcare

Many patients have begun to use mobile applications to handle different health needs because they can better access high-speed Internet and smartphones. These devices and mobile applications are now increasingly used and integrated through the medical Internet of Things (mIoT). mIoT is an important part of the digital transformation of healthcare, because it can introduce new business models and allow efficiency improvements, cost control and improve patient experience. In the mIoT system, when migrating from traditional medical services to electronic medical services, patient protection and privacy are the priorities of each stakeholder. Therefore, it is recommended to use different user authentication and authorization methods to improve security and privacy. In this paper, our prosed model involves a shared identity verification process with different situations in the e-health system. We aim to reduce the strict and formal specification of the joint key authentication model. We use the AVISPA tool to verify through the well-known HLPSL specification language to develop user authentication and smart card use cases in a user-friendly environment. Our model has economic and strategic advantages for healthcare organizations and healthcare workers. The medical staff can increase their knowledge and ability to analyze medical data more easily. Our model can continuously track health indicators to automatically manage treatments and monitor health data in real time. Further, it can help customers prevent chronic diseases with the enhanced cognitive functions support. The necessity for efficient identity verification in e-health care is even more crucial for cognitive mitigation because we increasingly rely on mIoT systems.