User Verification Research Articles

Bridging medical expertise in crisis: The development and implementation of a novel mobile application for Ukrainian physicians during wartime.

The full-scale invasion disrupted health care in Ukraine, leading to the displacement of physicians and affecting their access to subspecialist consultations. HealUA, a mobile application, was designed to provide secure and timely remote physician-to-physician consultations. We aimed to assess the implementation of the HealUA mobile application for peer-to-peer physician consultations in Ukraine during the Russian invasion. HealUA was developed in May 2022. Security measures included user verification, privacy policies, and legal disclaimers. The application allowed physicians to submit cases and receive remote consultations from physicians in Ukraine and worldwide. We assessed the implementation of the HealUA application using Proctor's implementation outcomes framework, specifically adoption and feasibility. Adoption was measured by user downloads, characteristics of registered physicians, and case submissions. Feasibility was evaluated through clinical case response times, translation services, and technical issues. From May 2022 to May 2024, 3861 physicians registered. The majority were from Ukraine (95%). Of 474 submitted cases, 97.3% received timely responses from other physicians. The application demonstrated prompt response times (84.6% within the first day), successful translation services, and effective resolution of technical issues. The HealUA application achieved broad adoption across medical specialties, fostering robust clinical information exchange during the ongoing conflict. Security standards were upheld and routine technical issues were satisfactorily addressed. Future efforts will focus on broader dissemination and assessing additional implementation outcomes.

The Evolution of Identity and Access Management: Integrating Biometric and Behavioral Authentication

This article explores the transformative role of biometric and behavioral authentication in the evolving landscape of Identity and Access Management (IAM). As traditional password-based systems increasingly fail to meet the challenges posed by sophisticated cyber threats, these advanced authentication methods offer a promising solution. The article provides a comprehensive overview of physical and behavioral biometrics, examining their integration into multi-factor authentication systems and their capacity for continuous, passive user verification. It delves into the significant security benefits these technologies offer, including enhanced resistance to phishing, social engineering, and insider threats, while also highlighting the improvements in user experience and convenience. The article critically analyzes the challenges and considerations associated with implementing biometric and behavioral authentication, including privacy concerns, data security, accuracy issues, and scalability. Furthermore, it explores future directions and research opportunities in the field, such as advancements in biometric technologies, the application of machine learning and AI in behavioral analysis, and the development of context-aware authentication systems. By synthesizing current research and industry trends, this article provides valuable insights for cybersecurity professionals, IT decision-makers, and researchers interested in the future of secure and user-friendly authentication methods. It underscores the potential of biometric and behavioral authentication to significantly enhance organizational security postures while improving user satisfaction in an increasingly digital world.

Leveraging quantum blockchain for secure multiparty space sharing and authentication on specialized metaverse platform

In the era of digital transformation, securing data on metaverse platforms poses significant challenges. This paper proposes Multiparty Space Sharing and Authentication (MSSA), a novel approach for secure user login and location access control within specialized metaverse platforms. MSSA leverages Quantum Multiparty Secret Computation (QMSC) integrated with a quantum blockchain network. This integration facilitates user verification within the presence of potentially untrusted metaverse authority. The underlying quantum blockchain employs a Delegated Proof-of-Stake (DPoS) consensus mechanism with a Borda voting scheme for authority node selection. By harnessing the principles of quantum cryptography, MSSA offers enhanced security against both classical and anticipated future quantum attacks. This research demonstrates the feasibility and potential of quantum blockchain for securing metaverse platforms, paving the way for secure and decentralized digital ecosystems.

Online safety and social media regulation in Australia: eSafety Commissioner v X Corp

ABSTRACT This article discusses the issue of online safety and the regulation of social media, examining first, some key theoretical perspectives on social media regulation, transparency, legitimacy and corporate regulation; second, Australian legal responses to the issue of online safety, including the Online Safety Act, legislation to curb abhorrent violent material and violent extremist material, and the eSafety Commissioner v X Corp case; and third, evolving technology-based strategies to regulation: self-regulation systems and user verification. The challenges and complexities that arise in the context of social media are emblematic of the much wider challenges facing both technology regulation and society around the world.

Study of Multimodal Identification Algorithms using Modern Methods and Tools of Multivariate Analysis

This article aims to comprehensively investigate the theoretical and practical foundations, as well as the distinctive characteristics, underpinning the study of multimodal identification algorithms. This investigation will be conducted using state-of-the-art methods and tools of multidimensional analysis. The development of a multimodal algorithm using the method of modality fusion at the feature level encompasses the integration of various algorithms rooted in multivariate analysis. These include a combined voice activity detector, a face detector utilizing the MTCNN (multi-task cascade convolutional networks) architecture, fine-frequency cepstral coefficients, facial image features, and a decision-making module. To construct a multimodal identification algorithm, a framework for combining these algorithms based on multivariate analysis is proposed. Analysis of the acquired data indicates that “Test 1”, utilizing facial image data, exhibits the highest performance indicators, approaching nearly 100%. Tests 2 and 3 involving voice signals exhibit a minor error in the pre-processing stage, attributed to the inherent delay experienced by participants during the video conference. The proposed multimodal algorithm, integrated within a biometric identification system, enables successful user verification research through the utilization of a combined multidimensional analysis algorithm. Furthermore, the algorithm showcases superior research outcomes in comparison to other analogous multimodal identification algorithms, as it yields precise results.

Lightweight ring-neighbor-based user authentication and group-key agreement for internet of drones

As mobile internet and Internet of Things technologies continue to advance, the application scenarios of peer-to-peer Internet of Drones (IoD) are becoming increasingly diverse. However, the development of IoD also faces significant challenges, such as security, privacy protection, and limited computing power, which require technological innovation to overcome. For group secure communication, it is necessary to provide two basic services, user authentication and group key agreement. Due to the limited storage of IoD devices, group key negotiation requires lightweight calculations, and conventional schemes cannot satisfy the requirements of group communication in the IoD. To this end, a new lightweight communication scheme based on ring neighbors is presented in this paper for IoD, which not only realizes the identity verification of user and group key negotiation, but also improves computational efficiency on each group member side. A detailed security analysis substantiates that the designed scheme is capable of withstanding attacks from both internal and external adversaries while satisfying all defined security requirements. More importantly, in our proposal, the computational cost on the user side remains unaffected by the variability of the number of members participating in group communication, as members communicate in a non-interactive manner through broadcasting. As a result, the protocol proposed in this article demonstrates lower computational and communication costs in comparison to other cryptographic schemes. Hence, this proposal presents a more appealing approach to lightweight group key agreement protocol with user authentication for application in the IoD.

Zara Tech Trail: Futuristic Autonomous Robocart for Cutting-Edge Multi-Perspective Delivery System

The autonomous delivery system presented in this project utilizes a RoboCart equipped with GPS navigation to seamlessly transport products from source to destination. The system, powered by electric charging, ensures timely and secure delivery to end customers, featuring a specialized hand gripper for careful product handling. Designed for diverse applications such as commercial purposes, personal use, and industries including hotels, this fully autonomous cart incorporates a password enabled security feature to guarantee user verification. Positioned as a cutting-edge technological solution, this project aims to effectively address the last mile delivery challenge, presenting a potential to significantly reduce delivery times and contribute to societal benefits. The success of this initiative holds the promise of substantial positive impacts on the Society.

Importance of FDA-Integrated Continuous Glucose Monitors to Ensure Accuracy of Continuous Glucose Monitoring.

Continuous glucose monitoring (CGM) has been shown to improve glycemic control and self-monitoring, as well as to reduce the risk of hypoglycemia. Integrated CGM (iCGM) FDA-cleared systems with published performance data are established nonadjunctive and accurate CGM tools that can directly inform decision-making in the treatment of diabetes (i.e., insulin dosing). Studies have assessed accuracy and safety data of CGMs that were eventually cleared for iCGM by the FDA and that informed the recommendation for their nonadjunctive use. Subsequent robust clinical trials and real-world studies demonstrated clinical effectiveness with improvements in a range of patient outcomes. In recent years, a number of non-iCGM-approved CGM devices have entered the market outside the United States worldwide. Some of these non-iCGM-approved CGM devices require additional user verification of blood glucose levels to be performed for making treatment decisions, termed adjunctive. Moreover, in many non-iCGM-approved CGM devices, accuracy studies published in peer-reviewed journals are scarce or have many limitations. Consequently, non-iCGM-approved CGM devices cannot be automatically perceived as having the same performance or quality standards than those approved for iCGM by the FDA. As a result, although these devices tend to cost less than iCGMs that carry FDA clearance and could therefore be attractive from the point of view of a health care payer, it must be emphasized that evaluation of costs should not be limited to the device (such as the usability preference that patients have for nonadjunctive sensors compared to adjunctive sensors) but to the wider value of the total benefit that the product provides to the patient.

Eye Based Secure Authentication System

Abstract: Personal identification numbers are commonly utilized for user verification and enhanced security. When users authenticate passwords through PINs, they must be obligatory to physically input the PIN, making them susceptible to password cracking techniques like shoulder surfing or thermal tracking. In contrast, utilizing hands-off gaze-based PIN entry methods eliminates any physical traces, providing a significantly more secure option for password entry. Gaze-based authentication involves determining the eye location across consecutive image frames and continuously tracking the precise eye center.

Improving user security during a call

The recent development of mobile networks has led to the emergence of new threats and methods of implementing existing ones. Phishing attacks, including robocalls, are causing record losses to both individual users and large corporations. At the same time, existing countermeasures cannot provide protection against such attacks because most existing solutions focus on device authentication, whereas user authentication does not occur during a call. Another problem with mobile networks is that there is no point-to-point encryption, i.e., the speech is encrypted only on the segment from the subscriber to the base station. The subject of study in this article is the process of ensuring user security during a call. The purpose of this study is to develop a model of mutual user authentication and end-to-end data encryption in a mobile network during a call. The main objectives are the protection of users from spoofing and vishing and the proposal of a protection method by implementing mutual authentication of users during a call without storing confidential information on the side of a "trusted third party". Method of secure key exchange and end-to-end encryption during a call in the mobile network was proposed. It prevents the interception of calls by the operator for circuit-switched and packet networks. The methods used are mathematical modelling, ontological approach, and multi-criteria optimization models. Because of this research, an algorithm for mutual authentication of users is proposed by introducing biometric authentication methods and modifying the sequence of messages during a call. The proposed approach can be implemented for CS-call and VoLTE/VoWiFi calls. A call cannot be received without user biometric authentication; such as ear pattern or bone conduction methods. Modified SETUP and CONNECT ACK messages are used to inform the other party about the user verification result. This prevents user spoofing, call masquerading, and robocalls. A combination of the proposed asymmetric encryption, a short authentication string, and hashes of previous calls provides a higher level of confidentiality, integrity, and additional resistance to man-in-the-middle attacks. Conclusions. The scientific novelty of the obtained results is the integration of the above methods into the sequence of call flow messages for providing mutual authentication, end-to-end encryption, and counteraction to the number of network attacks. The proposed methods allow one level to increase the provision of services of privacy and observation groups and can be implemented in the software part of user equipment.

Exploring the Privacy Experiences of Closeted Users of Online Dating Services in the US

Online dating services present significant privacy risks, especially for LGBTQ+ people who are "in the closet" and have not shared their LGBTQ+ identity with others. We conducted a survey (n = 114) and nine follow-up interviews with US-based, closeted users of online dating services focused on their privacy experience. We found that participants in the study were strongly concerned about the risk of being seen by social relations and institutional data sharing practices like targeted advertising. Participants experienced a range of privacy and safety harms, including inadvertent outing, unauthorized saving and sharing of photos, extortion, and harassment. To protect their privacy, participants typically limited the amount of information and the photos they included in their profile. In order to improve their privacy experience, participants requested better profile visibility controls, limits on the ability of others to download or screenshot their photos, better user verification, and making premium privacy features available for free.

ATM Security based on Face Verification

Abstract: To provide a reliable security solution, this project focuses on creating an ATM security system based on facial recognition using OpenCV, machine learning, and deep learning. The paper examines how facial recognition technology can improve ATM security, offering a non-intrusive and highly accurate method of identity verification. By analyzing unique facial features, such as facial component sizes and shapes, this technology can authenticate users in real-time reliably. The proposed system integrates facial recognition software with existing ATM infrastructure. Users are prompted to look into a camera for facial authentication when approaching the ATM. Upon a successful match, users gain access to ATM functionalities, ensuring a seamless and secure transaction experience. This ATM security system emphasizes robust user verification before granting financial transaction access. By integrating face recognition and personal question verification, the system provides a multilayered security approach, increasing user confidence and deterring unauthorized access attempts.

¬¬¬Instant Messaging Security: A Comprehensive Review of Behavior Patterns, Methodologies, and Security Protocols

This review presents a comprehensive analysis of contemporary scholarship pertaining to instant messaging (IM) user behavior and security protocols. Through meticulous selection, the authors highlight critical studies that illuminate optimized message consumption strategies and delve into the evolving landscape of IM security models. Focusing on the past four years, the review meticulously dissects cutting-edge advancements in this domain. A significant insight emerges: achieving optimal communication security necessitates the synergistic convergence of three fundamental techniques: end-to-end encryption for data confidentiality, decentralized authentication for independent user verification, and zero-knowledge proof for identity obscurity. The review postulates that the simultaneous integration of these elements within the application architecture is paramount for robust privacy and heightened security in the realm of IM.

Behavioural Biometrics as a User Authentication Mechanism in ISMS

Securing sensitive data and mitigating cyber threats necessitates robust user authentication mechanisms within Information Security Management Systems (ISMS). Conventional authentication approaches, such as passwords and static biometric identifiers, exhibit vulnerabilities, urging the adoption of more sophisticated solutions. Behavioral biometrics, which analyzes unique behavioral patterns like typing rhythm and mouse movements, offers a promising avenue for enhancing authentication security while optimizing user experience. This paper examines the integration of behavioral biometrics into ISMS for user verification, emphasizing its advantages including continuous, multimodal, risk-based, and adaptive authentication. Additionally, it addresses considerations like data privacy, regulatory compliance, user experience enhancement, and monitoring and analysis to ensure successful implementation and efficacy. By leveraging behavioral biometrics as part of a holistic authentication strategy, organizations can fortify their security posture, mitigate threats, and adapt to evolving cybersecurity challenges.

Deep learning-based photoplethysmography biometric authentication for continuous user verification

Biometric authentication methods have gained prominence as secure and convenient alternatives to traditional passwords and PINs. In this paper, we propose a novel approach for biometric authentication using photoplethysmography (PPG) signals and deep learning techniques. PPG is a non-invasive method that measures variations in blood volume within microvascular tissue beds, and it is typically used for monitoring heart rate and oxygen saturation. Our research leverages the unique characteristics of PPG signals to develop a robust and continuous user verification system. The primary goal of our study is to explore the feasibility and effectiveness of PPG-based biometric authentication, enabling a seamless and secure means of confirming the identity of individuals. We use a diverse dataset of PPG signals from various individuals, ensuring that it encompasses differences in skin tone, age, and other variables that can influence PPG signal characteristics. The collected data undergoes careful preprocessing, including noise removal, baseline correction, and heartbeat segmentation. For the core of our authentication system, we design and train a multiscale feature fusion deep learning (MFFD) model. This model, utilizing a Convolutional Neural Network (CNN) architecture, takes as input the relevant features extracted from PPG signals and learns to differentiate between individuals based on their unique PPG patterns. In this study, the input is constructed by gradually incorporating various features, beginning with a single PPG signal. In this study, the CNN model was trained independently, followed by the implementation of score fusion techniques. Our evaluation demonstrates the effectiveness of the PPG-based biometric authentication system, achieving high accuracy while addressing key security concerns. We consider false acceptance rate (FAR) and false rejection rate (FRR) to assess the system's performance. The model achieves the Accuracy of 99.5 % on BIDMC, 98.6 % on MIMIC, 99.2 % on CapnoBase dataset.

From Clicks to Security: Investigating Continuous Authentication via Mouse Dynamics

In the realm of computer security, the importance of efficient and reliable user authentication methods has become increasingly critical. This paper examines the potential of mouse movement dynamics as a consistent metric for continuous authentication. By analysing user mouse movement patterns in two contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond conventional methodologies by employing a range of machine learning models. These models are carefully selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as reflected in their mouse movements. This multifaceted approach allows for a more nuanced and comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine learning models employed in this study demonstrate competent performance in user verification, marking an improvement over previous methods used in this field. This research contributes to the ongoing efforts to enhance computer security and highlights the potential of leveraging user behavior, specifically mouse dynamics, in developing robust authentication systems.

Smart Locking System for Banking Applications

Abstract :The Smart and Innovative Locking System for Banking Applications introduces a cutting-edge approach to thwart unauthorized access, trespassing, and intrusion in environments prone to security threats, such as banks, corporate offices, financial organizations, jewelry shops, and government institutions. Elevating security measures, the system employs a dynamic One-Time Password (OTP) protocol, effectively enhancing security levels and deterring potential unauthorized unlocking attempts. User verification leverages a secure OTP-based methodology, ensuring a dependable and user-friendly method for secure access. This advanced security system provides a resilient and versatile security framework, empowering users to proactively address evolving security challenges. Key Words: The Smart Locking System, Unauthorized Access Prevention, Dynamic OTP Protocol

Enhanced user verification in IoT applications: a fusion-based multimodal cancelable biometric system with ECG and PPG signals

Enhanced user verification in IoT applications: a fusion-based multimodal cancelable biometric system with ECG and PPG signals

PORTABLE BIOMETRIC MODULE SOFTWARE FOR MILITARY AVIATION SUPPORT SYSTEM

The article discusses a software solution that addresses the integration of portable devices into the maintenance system of the Polish Air Force. In order to meet the requirements of this solution, it is essential to have a mobile device that can collaborate with military systems on a civilian network and process classified information. The current regulations and existing solutions suggest the need to establish a procedure for wireless data synchronization through the global Internet. The scope of this solution includes developing a method for exchanging data and ensuring data security. To enhance the project, we have also implemented a streamlined user authentication process. To achieve this, we tested the biometric sensors of the mobile device, which allowed for quick and convenient user verification without compromising the security of the processed information. In the paper, we have presented the design process of this solution, along with the final results. Furthermore, we have provided an example of its implementation as a mobile application designed for the pilot's portable device, which seamlessly integrates with the TURAWA flight safety analysis and assessment system.

Enhancing healthcare data security: a two-step authentication scheme with cloud technology and blockchain

In the modern world, medical data leakage has many external and internal threats. Information systems of medical organizations are constantly subject to various types of cyber-attacks and unauthorized penetration attempts, which leads to the publication of patient medical data online. Existing authentication schemes using blockchain technologies in medical organization systems ensure the integrity of medical data and secure access to patient data. However, one of the serious reasons for unauthorized access to the healthcare system is the human factor, which manifests itself in a negligent attitude towards account security, non-compliance with the rules and policies of information security, and transferring to third parties personal login details to the information system of a medical organization. This paper proposes a solution to this problem through an improved two-step authentication scheme using cloud technology and blockchain. The combined use of cloud technologies and blockchain is a distinctive feature of the proposed authentication scheme since it provides two levels of protection: 1) two-step authentication, the second stage of which includes biometrics through a mobile application. It prevents unauthorized access to the system by third parties; 2) cloud encryption keys for decrypting medical data, which are also accessed through the user's biometrics. The practical part of the paper includes the implementation of biometric login in Python using the OpenCV library. As a result of the practical part, unique fingerprint samples were obtained. The biometric user verification algorithm is designed for a mobile application, which we plan to implement in the future