User Authentication Mechanism Research Articles

Tech Quest Language Learning

Tech Quest Language Learning is an innovative web based platform designed to facilitate language learning through interactive quizzes tailored for engineering students and professionals. The platform offers a diverse range of quizzes covering various engineering subjects, providing users with an engaging and effective way to test their knowledge and skills. Through a user-friendly interface, participants can navigate seamlessly between quizzes, receive instant feedback on their performance, and track their scores over time. Additionally, the platform incorporates user authentication mechanisms, ensuring secure access to personalized learning experiences. With its emphasis on interactivity, accessibility, and user engagement, "Tech Quest Language Learning" aims to enhance language proficiency and academic success in the engineering domain.

Integrating NLP Chatbot into Career Guidance Web Application

Artificial Intelligence (AI) is the development of computer systems that can perform tasks that typically require human intelligence. The development of an advanced web-based chatbot system involves the integration of various technologies and methodologies to create a seamless conversational interface. Leveraging HTML, CSS, and JavaScript for frontend development, alongside Python Flask framework for backend functionality, the project aims to deliver a robust and intuitive user experience. Key to the system's operation is the incorporation of PyTorch for machine learning, allowing for the implementation of sophisticated natural language processing (NLP) techniques. Through tokenization and stemming using NLTK (Natural Language Toolkit), the system enhances its understanding of user queries, enabling it to provide relevant and contextually appropriate responses. At the core of the system lies a supervised learning model trained on a custom dataset compiled from user interactions. This model utilizes bag-of-words representations and neural network architectures to predict responses based on input text. By continuously refining its predictions through feedback mechanisms, the system improves its accuracy and responsiveness over time, ensuring a more tailored and effective user experience. Voice input functionality, facilitated by speech recognition technology, further enhances user interaction by allowing users to communicate with the chatbot using spoken commands. This feature expands the accessibility and convenience of the system, catering to a broader range of users with varying preferences and abilities. In addition to its conversational capabilities, the system prioritizes security and privacy, implementing robust user authentication mechanisms and secure data storage practices. By adhering to industry best practices and standards, the project aims to instill trust and confidence in users regarding the confidentiality and integrity of their data.User interface design and usability are also key considerations in the development process, with careful attention given to creating an intuitive and visually appealing interface. Through thoughtful design choices and user feedback, the system seeks to optimize the user experience and streamline the interaction flow, ensuring that users can easily navigate and utilize its features. Overall, the project endeavors to create an intelligent and user-friendly chatbot system that enhances communication and interaction between users and technology. By leveraging advanced technologies and methodologies, the system aims to provide personalized assistance and support to users, ultimately improving productivity, efficiency, and satisfaction in various domains and applications

A robust and effective 3-factor authentication protocol for smart factory in IIoT

Smart factory, as an intelligent application of industrial internet of things (IIoT), significantly enhances the efficiency of industrial processes while also reducing resource waste. In a system where security is crucial, the user authentication mechanism is essentially designed to prevent a range of security issues, including production failures caused by illegal intrusions from hackers. To verify the user’s identity and ensure data be accessed with a secure session key, a large number of authentication protocols have been provided for IIoT. However, existing newly alternatives show deficiency either in terms of superior performance or robust security. To get a better balance of security and efficiency, this paper designs a robust and effective 3-factor user authentication protocol. Then this paper shows the detailed security analyses and automated verification by the Proverif tool, where the ProVerif tool used in this paper can detect whether the eCK adversary with stronger attack ability can break the protocol, while the general ProVerif can only detect threat of the Dolev–Yao adversary with weaker attack ability to the protocol security. Subsequently, this paper presents performance comparison which indicates that the proposed protocol can be superior to those newly alternatives. Especially, the comparison results show that the computation cost consumed in the proposed protocol can be reduced by 62.2% than the average cost of all six compared alternatives. Lastly, the evaluation results on the energy consumption and the network delay indicate the practicability of proposed protocol for smart factory.

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.

Revolutionizing Cloud Storage Security: Searchable Public-Key Encryption

The Secure Cloud Storage App, developed using React.js for the frontend and MongoDB for thebackend, addresses the growing need for a reliable and privacy-focused solution for storing and managing digital assetsin the cloud. In response to the escalating demand for a dependable and privacy-centric solution to handle digital assetsin the cloud, the Secure Cloud Storage App has emerged, meticulously crafted with React.js powering its frontend andMongoDB driving its backend architecture. This innovative application stands as a beacon for users seeking a securehaven for their data. At its core, security takes precedence, with the implementation of cutting-edge encryptionprotocols meticulously woven into every aspect of data transmission and storage. This fortification ensures the utmostconfidentiality and integrity of user information throughout its journey within the cloud infrastructure. The userinterface, expertly fashioned using React.js, stands as a testament to seamless design and intuitive interaction. Users areempowered with effortless capabilities to upload, organize, and access their files, enhancing productivity andconvenience in managing digital assets. MongoDB, a stalwart in backend database solutions, offers a robust foundationcharacterized by scalability and flexibility, adeptly catering to diverse storage requirements. Moreover, the SecureCloud Storage App encompasses comprehensive user authentication and authorization mechanisms, bolstering accesscontrol and privacy measures. Through the amalgamation of secure authentication protocols and end-to-end encryption,paramount importance is placed on shielding user data from prying eyes, fostering an environment of trust andconfidence in cloud-based file management. The synergy between React.js and MongoDB transcends meretechnological integration; it epitomizes a harmonious marriage of functionality and security. Not only does this pairingensure a responsive and dynamic user interface, but it also lays down a resilient and secure infrastructure for the storageand retrieval of sensitive information within the cloud ecosystem. In essence, the Secure Cloud Storage App stands as abeacon of innovation, ushering in a new era of secure and efficient cloud-based file management solutions.

Lightweight privacy-preserving authentication mechanism in 5G-enabled industrial cyber physical systems

With the deep integration of informatization and industrialization, cyber-physical system (CPS), which integrates computing, communication and control technologies, comes into being and has been widely used in industrial applications. A large number of information physical system devices and control systems are based on open internet connections, and security and privacy protection issues are gradually emerging, especially in the process of dynamic and difficult to control information physical system stream data transmission and storage, which will face new security threats. To alleviate these issues, user authentication mechanisms can prevent unauthorized access by adversaries in the CPS environment. For this purpose, we put forth a lightweight privacy-preserving authentication scheme for 5G-enabled ecosystems. This method utilizes edge devices to save online transaction data in network and securely transmits IoT devices data from the sensors to the edge gateway. Then, the edge gateway delivers and stores these data in the cloud. A detailed comparative study based on experimental results indicates that our proposal achieves a better balance between security and functionality features, communication and capitulation costs compared to other existing competitive solutions.

Enhancing Security in IoT Networks: Advancements in Key Exchange, User Authentication, and Data Integrity Mechanisms

Enhancing Security in IoT Networks: Advancements in Key Exchange, User Authentication, and Data Integrity Mechanisms

A Rivest–Shamir–Adleman-Based Robust and Effective Three-Factor User Authentication Protocol for Healthcare Use in Wireless Body Area Networks

In healthcare, wireless body area networks (WBANs) can be used to constantly collect patient body data and assist in real-time medical services for patients from physicians. In such security- and privacy-critical systems, the user authentication mechanism can be fundamentally expected to prevent illegal access and privacy leakage occurrences issued by hacker intrusion. Currently, a significant quantity of new WBAN-oriented authentication protocols have been designed to verify user identity and ensure that body data are accessed only with a session key. However, those newly published protocols still unavoidably affect session key security and user privacy due to the lack of forward secrecy, mutual authentication, user anonymity, etc. To solve this problem, this paper designs a robust user authentication protocol. By checking the integrity of the message sent by the other party, the communication entity verifies the other party’s identity validity. Compared with existing protocols, the presented protocol enhances security and privacy while maintaining the efficiency of computation.

User Authentication Mechanisms Based on Immersive Technologies: A Systematic Review

User Authentication Mechanisms Based on Immersive Technologies: A Systematic Review

Design and Testbed Experiments of User Authentication and Key Establishment Mechanism for Smart Healthcare Cyber Physical Systems

Smart healthcare technologies transform the traditional healthcare system in all possible ways. Smart healthcare has enormous number of benefits over the traditional system. However, at the same time, it also suffers from some healthcare data security and privacy related issues as the potential Internet attackers may get access to the sensitive healthcare through the deployment of various kinds of attacks. To mitigate these issues, in this paper, a new lightweight remote user authentication and key establishment scheme (in short, UAKM-SH) has been proposed by making the use of lightweight cryptographic operations. The security analysis of UAKM-SH is conducted to prove its robustness against various possible attacks. A detailed comparative study among the proposed UAKM-SH and other existing schemes shows that UAKM-SH performs better in terms of computation cost, communication cost, and the offered security and functionality features. Finally, the testbed implementation of UAKM-SH is given to observe its behavior in the real time scenario.

Data-Augmentation-Enabled Continuous User Authentication via Passive Vibration Response

Continuous identity authentication is critical for privacy protection throughout an entire user login session. In this paper, we propose a continuous user authentication mechanism namely, which employs the vibration responses from hand biometrics and is passively activated by natural user-device interaction. Hand vibration responses are embedded in the mechanical vibration of a force-bearing body consisting of one mobile device and one user hand. A built-in accelerometer of the device can capture hand-dependent vibration signals. Considering the concealment of vibration generation and the non-replicability of hand structure, it’s difficult for attackers to counterfeit user identity. Moreover, for ensuring the robustness of authentication performance to tapping behavior interference, we construct a data augmentation module jointly leveraging a signal processing and learning-based pipeline. It can generate enough vibration responses representing hand structure biometrics under various behaviors, thereby making comprehensively understand vibration response variation. We prototype on smartphones, and extensive experiments demonstrate that can achieve satisfactory authentication accuracy.

Secure two-factor mutual authentication scheme using shared image in medical healthcare environment

The cloud healthcare system has become the essential online service during the COVID-19 pandemic. In this type of system, the authorized user may login to a distant server to acquire the service and resources they demand, we need full security procedures that cover criteria such as authentication, privacy, integrity, and availability. The journey of security for any healthcare system starts with the authentication of users based on their privileges. Traditional user authentication mechanisms, such as password and personal identification number (PIN) typing, are vulnerable to malicious attacks like on/offline, insider, replay, guessing, and shoulder surfing. To address these issues, we proposed a secure authentication scheme that uses the authenticated delegating mechanism based on two factors: a one-time password and generating a secure variable vector from a legible user's digital image to enable the permission of a user through the back-end database of a cloud server. The proposed mutual authentication can protect the information against well-known attacks, ensure the user's privacy, and key management. Moreover, comparisons with existing schemes show that the proposed scheme supplies more privacy, security metrics, and resistance to attacks than the others while being more efficient in computation and communication costs.

Secure two-factor mutual authentication scheme using shared image in medical healthcare environment

The cloud healthcare system has become the essential online service during the COVID-19 pandemic. In this type of system, the authorized user may login to a distant server to acquire the service and resources they demand, we need full security procedures that cover criteria such as authentication, privacy, integrity, and availability. The journey of security for any healthcare system starts with the authentication of users based on their privileges. Traditional user authentication mechanisms, such as password and personal identification number (PIN) typing, are vulnerable to malicious attacks like on/offline, insider, replay, guessing, and shoulder surfing. To address these issues, we proposed a secure authentication scheme that uses the authenticated delegating mechanism based on two factors: a one-time password and generating a secure variable vector from a legible user's digital image to enable the permission of a user through the back-end database of a cloud server. The proposed mutual authentication can protect the information against well-known attacks, ensure the user's privacy, and key management. Moreover, comparisons with existing schemes show that the proposed scheme supplies more privacy, security metrics, and resistance to attacks than the others while being more efficient in computation and communication costs.

Dynamic User Routing, Paid and Free Users, Web applications, Content Delivery Network (CDN)

Abstract: This research paper introduces a novel approach for efficient routing between paid and free users in web applications by harnessing the capabilities of a Content Delivery Network (CDN). The primary objective of this study is to optimize user experience and resource allocation based on user type, aiming to deliver personalized content while maximizing resource utilization. The proposed routing mechanism dynamically directs users to the appropriate pages or content based on their subscription status, leveraging the integration of a CDN into the web application architecture. The paper thoroughly explores the implementation details, emphasizing the seamless interaction between the CDN and user authentication mechanisms, and investigates the routing algorithms employed to intelligently determine the most suitable content delivery path for each user type. Through comprehensive experimentation and evaluation, the research demonstrates the efficiency and scalability of the proposed approach. Various user scenarios are considered, and factors such as response time, resource utilization, and content availability are evaluated. The experimental results showcase the significant enhancement of the overall user experience achieved through the dynamic user routing mechanism, providing tailored content delivery while effectively managing resource allocation. The findings of this research contribute to the field of web application development, particularly in managing different user types and delivering personalized content. By effectively leveraging CDNs and implementing dynamic routing strategies, web applications can optimize user satisfaction, ensure efficient resource utilization, and accommodate varying user subscription models. This research offers valuable insights and practical implications for designing and implementing usercentric web applications that deliver customized experiences and efficiently allocate resources based on user types. The paper delves into the implementation details, highlighting the key components and functionalities of the proposed solution. It explores the integration of the CDN into the web application architecture, emphasizing the seamless interaction between the CDN and user authentication mechanisms. Additionally, it investigates the routing algorithms employed to intelligently determine the appropriate content delivery path for each user type.

End-to-End Post-Quantum Cryptography Encryption Protocol for Video Conferencing System Based on Government Public Key Infrastructure

Owing to the expansion of non-face-to-face activities, security issues in video conferencing systems are becoming more critical. In this paper, we focus on the end-to-end encryption (E2EE) function among the security services of video conferencing systems. First, the E2EE-related protocols of Zoom and Secure Frame (SFrame), which are representative video conferencing systems, are thoroughly investigated, and the two systems are compared and analyzed from the overall viewpoint. Next, the E2EE protocol in a Government Public Key Infrastructure (GPKI)-based video conferencing system, in which the user authentication mechanism is fundamentally different from those used in commercial sector systems such as Zoom and SFrame, is considered. In particular, among E2EE-related protocols, we propose a detailed mechanism in which the post-quantum cryptography (PQC) key encapsulation mechanism (KEM) is applied to the user key exchange process. Since the session key is not disclosed to the central server, even in futuristic quantum computers, the proposed mechanism, which includes the PQC KEM, still satisfies the E2EE security requirements in the quantum environment. Moreover, our GPKI-based mechanism induces the effect of enhancing the security level of the next-generation video conferencing systems up to a quantum-safe level.

SearchAuth: Neural Architecture Search-based Continuous Authentication Using Auto Augmentation Search

Mobile devices have been playing significant roles in our daily lives, which has made device security and privacy protection extremely important. These mobile devices storing user sensitive and private information, therefore, need rigorous user authentication mechanisms. In this article, we present SearchAuth, a novel continuous authentication system on smartphones exploiting a neural architecture search (NAS) to find an optimal network architecture and an auto augmentation search (AAS) to more effectively train the optimal network along with the best data augmentation policies, by leveraging the accelerometer, gyroscope, and magnetometer on smartphones to capture users’ behavioral patterns. Specifically, SearchAuth consists of three stages, i.e., the offline stage, registration stage, and authentication stage. In the offline stage, we utilize the NAS on sensor data of the accelerometer, gyroscope, and magnetometer to find an optimal network architecture based on the designed search space. With the optimal network architecture, namely, NAS-based model, the AAS automatically optimizes the augmentation of the input data for more effectively training the model that is for feature extraction. In the registration stage, we use the trained NAS-based model to learn and extract deep features from the legitimate user’s data, and train the LOF classifier with 55 features selected by the PCA. In the authentication stage, with the well-trained NAS-based model and LOF classifier, SearchAuth identifies the current user as a legitimate user or an impostor when the user starts operating a smartphone. Based on our dataset, we evaluate the performance of the proposed SearchAuth, and the experimental results demonstrate that SearchAuth surpasses the representative authentication schemes by achieving the best accuracy of 93.95%, F1-score of 94.30%, and EER of 5.30% on the LOF classifier with dataset size of 100.

A multi‐device user authentication mechanism for Internet of Things

AbstractThe advent of the Internet of Things (IoT) enables different customized services to ease the day‐to‐day life activities of users by utilizing information attained through the internet connectivity of low‐powered sensing devices. Due to device diversity and resource constraints of participating devices, IoT is vulnerable to security attacks. Consequently, authentication is the fundamental measure for using IoT services in the context of network security. IoT devices’ resource captivity makes designing robust and secure authentication mechanisms challenging. Besides, existing user authentication mechanisms are designed assuming a user always accesses an IoT environment using a particular device. However, nowadays, most users employ multiple devices to access the internet; subsequently, it needs an authentication mechanism to handle this diversity. This paper addresses this limitation and proposes a new One‐Time Password (OTP)‐based user authentication scheme supporting user access from multiple devices in an IoT environment. We verify the proposed scheme using widely used BAN logic, AVISPA tool, and informal security analysis, guaranteeing that our scheme preserves the necessary security features. Comparative performance analysis shows that our scheme achieves comparable computation, storage, and communication costs concerning existing works. Moreover, simulation results demonstrate that the proposed method also sustains satisfactory network performance.

Continuous User Authentication on Multiple Smart Devices

Recent developments in the mobile and intelligence industry have led to an explosion in the use of multiple smart devices such as smartphones, tablets, smart bracelets, etc. To achieve lasting security after initial authentication, many studies have been conducted to apply user authentication through behavioral biometrics. However, few of them consider continuous user authentication on multiple smart devices. In this paper, we investigate user authentication from a new perspective—continuous authentication on multi-devices, that is, continuously authenticating users after both initial access to one device and transfer to other devices. In contrast to previous studies, we propose a continuous user authentication method that exploits behavioral biometric identification on multiple smart devices. In this study, we consider the sensor data captured by accelerometer and gyroscope sensors on both smartphones and tablets. Furthermore, multi-device behavioral biometric data are utilized as the input of our optimized neural network model, which combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network. In particular, we construct two-dimensional domain images to characterize the underlying features of sensor signals between different devices and then input them into our network for classification. In order to strengthen the effectiveness and efficiency of authentication on multiple devices, we introduce an adaptive confidence-based strategy by taking historical user authentication results into account. This paper evaluates the performance of our multi-device continuous user authentication mechanism under different scenarios, and extensive empirical results demonstrate its feasibility and efficiency. Using the mechanism, we achieved mean accuracies of 99.8% and 99.2% for smartphones and tablets, respectively, in approximately 2.3 s, which shows that it authenticates users accurately and quickly.

Trust-Based Secure Multi-Cloud Collaboration Framework in Cloud-Fog-Assisted IoT

Cloud-Fog-Assisted Internet-of-Things (IoT) is a convincing paradigm to provide users with on-demand and low-latency services through Fog nodes in the edge of multiple clouds (Multi-Cloud). Multi-Cloud is a scalable multi-domain service-oriented net-centric system and can respond to complicated user requirements leveraging Multi-Cloud Service Composition (MCSC). However, in MCSC, user security can be easily compromised by untrusted and curious cloud service providers that may collect and violate the privacy and other essential assets of cloud users. Although many trust-based MCSC solutions have been proposed to seek a trustworthy composite service with highest trust level, most of them are vulnerable to malicious users intending to break through the clouds and inflict serious data leakage or asset damage. Considering these security concerns on both malicious users and untrusted service providers, in this paper, we present a trust-based secure multi-cloud collaboration framework for Cloud-Fog-Assisted IoT systems. Specifically, to guarantee the security of users, we develop a role-based trust evaluation method to enhance the trustworthiness of MCSC. To preserve the security of services, we design an efficient user authentication scheme and a secure collaboration scheme to provide collaborative user authentication and access control mechanism for MCSC. We evaluate its performane with extensive experiments

A Survey on Quantitative Risk Estimation Approaches for Secure and Usable User Authentication on Smartphones

Mobile user authentication acts as the first line of defense, establishing confidence in the claimed identity of a mobile user, which it typically does as a precondition to allowing access to resources in a mobile device. NIST states that password schemes and/or biometrics comprise the most conventional user authentication mechanisms for mobile devices. Nevertheless, recent studies point out that nowadays password-based user authentication is imposing several limitations in terms of security and usability; thus, it is no longer considered secure and convenient for the mobile users. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Alternatively, biometric-based user authentication has gained attention as a promising solution for enhancing mobile security without sacrificing usability. This category encompasses methods that utilize human physical traits (physiological biometrics) or unconscious behaviors (behavioral biometrics). In particular, risk-based continuous user authentication, relying on behavioral biometrics, appears to have the potential to increase the reliability of authentication without sacrificing usability. In this context, we firstly present fundamentals on risk-based continuous user authentication, relying on behavioral biometrics on mobile devices. Additionally, we present an extensive overview of existing quantitative risk estimation approaches (QREA) found in the literature. We do so not only for risk-based user authentication on mobile devices, but also for other security applications such as user authentication in web/cloud services, intrusion detection systems, etc., that could be possibly adopted in risk-based continuous user authentication solutions for smartphones. The target of this study is to provide a foundation for organizing research efforts toward the design and development of proper quantitative risk estimation approaches for the development of risk-based continuous user authentication solutions for smartphones. The reviewed quantitative risk estimation approaches have been divided into the following five main categories: (i) probabilistic approaches, (ii) machine learning-based approaches, (iii) fuzzy logic models, (iv) non-graph-based models, and (v) Monte Carlo simulation models. Our main findings are summarized in the table in the end of the manuscript.