Authentication Techniques Research Articles

Secure and Lightweight Cluster-Based User Authentication Protocol for IoMT Deployment

Authentication is considered one of the most critical technologies for the next generation of the Internet of Medical Things (IoMT) due to its ability to significantly improve the security of sensors. However, higher frequency cyber-attacks and more intrusion methods significantly increase the security risks of IoMT sensor devices, resulting in more and more patients’ privacy being threatened. Different from traditional IoT devices, sensors are generally considered to be based on low-cost hardware designs with limited storage resources; thus, authentication techniques for IoMT scenarios might not be applicable anymore. In this paper, we propose an efficient three-factor cluster-based user authentication protocol (3ECAP). Specifically, we establish the security association between the user and the sensor cluster through fine-grained access control based on Merkle, which perfectly achieves the segmentation of permission. We then demonstrate that 3ECAP can address the privilege escalation attack caused by permission segmentation. Moreover, we further analyze the security performance and communication cost using formal and non-formal security analysis, Proverif, and NS3. Simulation results demonstrated the robustness of 3ECAP against various cyber-attacks and its applicability in an IoMT environment with limited storage resources.

Combing chemical composition profiling with machine learning for geographical origins identification of Nardostachys jatamansi DC.

Nardostachys jatamansi DC., a herb endemic to the Himalayas, has a long history of use in ayurvedic and other traditional medicine systems. The quality of N. jatamansi materials is closely related to their geographical origins. This study aims to develop a method for quickly and accurately identifying the geographical origin of N. jatamansi samples. A total of 118 samples of N. jatamansi samples were collected from five production areas in China. High performance liquid chromatography (HPLC), inductively coupled plasma mass spectrometry (ICP-MS) and gas chromatography-mass spectrometry (GC–MS) were used to characterize the active compounds, mineral elements, and volatile components in N. jatamansi, and four algorithms were introduced to explore the potential of various machine learning modeling techniques for geographical origin authentication. Three active compounds, 8 mineral elements and 24 volatile metabolites in N. jatamansi were identified to be the key information for origin traceability and authentication. The fused data based on the 3 active compounds and the other feature variables led to the highest accuracies for origin classification, with the accuracies of linear discriminant analysis (LDA), K-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) are 94.44 %, 100 %, 100 %, and 100 %, respectively. This study demonstrates the effectiveness of a multi-source data fusion strategy for geographical origin traceability of N. jatamansi.

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.

Analysis and Design of Efficient Authentication Techniques for Password Entry with the Qwerty Keyboard for VR Environments.

Authentication in digital security relies heavily on text-based passwords, even with other available methods like biometrics and graphical passwords. While virtual reality (VR) keyboards are typically invisible to onlookers, the presence of inconspicuous sensors, including accelerometers, gyroscopes, and barometers, poses a potential risk of unauthorized observation and recording. Traditional defense shoulder-surfing attack methods typically involve breaking apart the Qwerty layout, which destroys the user's inherent familiarity with the layout. This research addresses the need for secure password entry in VR environments while retaining the Qwerty layout. We explore three keyboard-related position alteration strategies to ensure security while mitigating the decline in user experience. These strategies involve moving the entire keyboard, cursor, and keys. Our theoretical study assesses the effectiveness of these strategies against shoulder-surfing attacks. Two user studies, employing ray-based and position-based text entry methods, respectively, evaluate the practical effectiveness of the three strategies in resisting shoulder-surfing attacks, as well as their impact on typing performance and user experience. Our findings demonstrate that the three strategies achieve shoulder-surfing attack resistance comparable to a random layout keyboard. Moreover, compared to a random layout, the two strategies involving the movement of the entire keyboard and the repositioning of keys support faster entry rates and enhanced user experience.

IMPORTANCE OF INTERNAL CONTROL SYSTEMS ON DETECTION AND PREVENTION OF FRAUD IN COMMERCIAL BANKS OF NEPAL

This research aims to study the importance of internal control systems to detection and prevention of fraud in commercial banks in Nepal. Advanced authentication techniques, vigilant monitoring for questionable activity, frequent staff education and awareness campaigns, and robust encryption for confidential information are all part of the process of identifying and preventing fraud. Effective internal control mechanisms are essential to the identification and aberrance of fraud in financial organizations. This study used descriptive research design to interpret the data. Using simple random sampling Ten commercial banks were selected as sample out of total Twenty commercial banks. Three level staff officer, manager, and executive were chosen on the study. Structured questionnaire was distributed to Fifty respondents but only 48 were collected and used in the study. Chi square test was applied to analyze the data. Findings of this study found that fraud in the organization is done by concealed hand and inefficient allocation of duties and authorities can not detect and prevent the fraud in commercial banks in Nepal. To detect and prevent the fraud the commercial banks need to apply effective internal control systems, allocate the duties and authorities in systematic way.

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.

Enhancing smartphone security with human centric bimodal fallback authentication leveraging sensors.

Smartphones store valuable personal information, necessitating robust authentication methods to protect user data. This research proposes a lightweight bi-model fallback authentication technique that combines dynamic security questions and finger pattern recognition using inertial measurement units. The dynamic security questions are generated based on the smartphone's usage behavior, while the owner's finger movements are captured using four different inertial sensors: accelerometer, gyroscope, gravity sensor, and magnetometer. By combining the answers to the questions and the owner's finger movements, the user can be authenticated even if the primary authentication method fails. In this study, data was collected from 24 participants, including 12 primary phone users and 12 close adversaries, over a span of 28 days. The dynamic security questions, derived from call, SMS, battery charging events, application usage, location, and physical activity categories, achieved high accuracy rates, with call, SMS, and application usage surpassing . Incorporating the inertial measurement units significantly improved the accuracy of all question types, increasing from a maximum of to , while also enhancing the True Positive Rate from 0.79 to 0.99 compared to a previous study. This research presents a promising lightweight bi-model fallback authentication technique that leverages dynamic security questions and inertial measurement units data, demonstrating its effectiveness for enhancing smartphone security.

Review and Analysis of Mathematical Approaches for Adaptive Authentication using Machine Learning

In today’s technological era, the combined problem of assuring user productivity and maintaining strong security standards has become quite a key challenge. As security considerations, have not to hurt productivity, significant efforts must be taken to achieve such goals. The notion of adaptive authentication comes into play to avoid friction in user experience so that a balance between security and user productivity can be maintained. Traditional authentication frameworks are becoming more obsolete due to their ineffectiveness in incorporating additional risk criteria such as location, network type, and operating system into the authentication management process. Adaptive authentication is a combination of authentication and machine learning (ML) technologies that can help in realizing secure intelligent systems. The goal of this research is to provide a complete review of the current applications of various machine learning algorithms often employed in adaptive authentication techniques. Throughout this study, the advantages, limits, and suggestions for future research will be thoroughly discussed.

Performance Analysis of Authentication System: A Systematic Literature Review

Background: Data authentication is vital nowadays, as the development of the internet and its applications allow users to have all-time data availability, attracting attention towards security and privacy and leading to authenticating legitimate users. Methods: We have diversified means to gain access to our accounts, like passwords, biometrics, and smartcards, even by merging two or more techniques or various factors of authentication. This paper presents a systematic literature review of papers published from 2010 to 2022 and gives an overview of all authentication techniques available in the market. Results: Our study provides a comprehensive overview of all three authentication techniques with all performance metrics (Accuracy, Equal Error Rate (EER), False Acceptance Rate (FAR)), security, privacy, memory requirements, and usability (Acceptability by user)) that will help one choose a perfect authentication technique for an application. Conclusion: In addition, the study also explores the performance of multimodal and multifactor authentication and the application areas of authentication.

Analysis of authentication methods for full-stack applications and implementation of a web application with an integrated authentication system

The subject of research is methods and techniques for secure user authentication in web applications. The goal of the work is to analyse authentication methods and implement a web application with an authentication system integrating JWT tokens and the OAuth v2.0 standard. The article solves the tasks of analysis of the main protocols and methods of user authentication in web applications, implementation of authentication based on the OAuth 2.0 standard and JWT access/refresh token, and analysis of the risks of vulnerabilities and attacks for the implemented web applications. Methods used: comparison, empirical analysis, calculation methods. The next results have been obtained: analysed the protocols and methods of user authentication in web applications; selected authentication methods of JWT token and OAuth v2.0 standard for building modern web applications; created a web application based on the selected authentication methods in web applications; analysed the risks of vulnerabilities and attacks in web applications. Conclusions: The most well-known authentication methods for web applications are analyzed. It is established that most modern authentication methods have many disadvantages, which leads to increased risks when using these authentication methods. It is shown that one of the most reliable methods of web application user data security is the use of a combination of JWT Access/Refresh token and browser fingerprints. The implementation, configuration, and analysis of this methodology have shown that this combination provides the most reliable prevention of token theft and use from another computer. OAuth 2.0 authentication was also implemented. The study found that delegating authentication to services such as Facebook or Google can provide a low risk of attacks and vulnerabilities for a web application. It is noted that authentication using OAuth 2.0 can be compromised only at the beginning of the connection between the client and the server, or rather when the client first sends initial information from the browser fingerprints. This information is sent over the secure HTTPS (Hypertext Transfer Protocol Secure) protocol, so the risk of compromising OAuth 2.0 authentication is low.

Enhancing cell phone security through finger vein biometric authentication systems

The growing demand for secure mobile devices has led to the widespread adoption of biometric technologies. While personal identification numbers (PINs), passwords, gridlock patterns to safeguard phones and facial recognition have become common, they have certain limitations in terms of security and user convenience. These conventional security measures are riddled with susceptibilities, such as password pilferage, memory lapses in authentication details, and user mistakes in grid pattern creation. Due to the rising occurrence of cell phone incursions and theft, there is an urgent requirement for a strong security system that not only defends data but also shields the device itself. This paper proposes a resilient finger vein biometric security system as a more effective substitute for knowledge-based and password-based authentication techniques. The implementation of finger vein biometrics as a novel approach to enhancing cell phone security. The method employs infrared (IR) light transmission to capture the vein patterns and shadows resulting from the different thicknesses of finger muscles, bones, and tissues. The unique vascular patterns in fingers are difficult to forge, offering a higher level of security compared to other biometric systems. The simulations we conducted indicate an identification accuracy rate of 93.82%, indicating that this technique provides a substantial enhancement in preventing unauthorized access and theft of cell phones. This study examines the technology behind finger vein biometrics, its integration into mobile devices, and the security advantages it offers.

TAVA: Traceable anonymity-self-controllable V2X Authentication over dynamic multiple charging-service providers

The widespread deployment of Electric vehicles (EVs) leads to an increasing demand for charging piles and corresponding charging service (CS) from CS providers (CSPs). Pseudonym-based authentication mechanisms have been designed to resist the attacks which exploit the charging-authentication information to infer EV users’ identities and their driving routes. However, these existing mechanisms generated EV users' pseudonyms by relying on a trusted third entity, which affects the authentication system's resilience and EV user privacy-preservation.To this end, this paper proposes a Traceable Anonymity-self-controllable V2X Authentication (TAVA) scheme for the multiple-CSP (forming a CSP set) scenario, where each CSP independently manages its own CPs and a CSP randomly joins or leaves the CSP set. TAVA has a series of security capabilities. (1) First, it allows the mutual authentication between an EV user and a CP, while preserving EV user privacy and also assuring forward and backward security. This capability is achieved by using the multi-party computation technique to let all CSPs join the process of generating EV-users’ credentials but each CSP knows nothing about the credentials. (2) Secondly, TAVA has the capabilities of self-controllable anonymity and unlinkability by allowing each EV user to self-generate verifiable and unlinkable one-time pseudonyms based on bilinear- mapping technique. (3) At last, each EV user under TAVA is traceable. It is achieved by applying the two-factor authentication technique in TAVA and linking the one-time pseudonym to the two factors, namely, the credential and the EV user's biometric characteristics with low entropy rates. Note that all these security capabilities are achieved with less performance degradation in terms of communication and storage overheads in the dynamic environment. We conduct the informal and formal analysis of security capabilities and also make performance evaluations. The results indicate that, compared with the latest works, the computation overhead of the mutual authentication in TAVA is reduced by up to 89 %.

Enhancing Security in On-Chip Communication: A Survey of Threats and Solutions

As the complexity of integrated circuits (ICs) continues to increase, ensuring the security of on-chip communication becomes paramount. With the rise of interconnected devices and the Internet of Things (IoT), vulnerabilities in on-chip communication pose significant risks to data integrity, privacy, and system reliability. This article presents a comprehensive survey of the threats and solutions associated with enhancing security in on-chip communication. The survey begins with an overview of on-chip communication and its critical role in modern ICs. It then explores the diverse range of security threats faced by on-chip communication systems, including side-channel attacks, information leakage, unauthorized access, and the insertion of hardware Trojans. Each threat is examined in detail, highlighting its characteristics and potential impact on system security. In response to these threats, the survey examines various solutions and countermeasures aimed at enhancing on-chip communication security. These include encryption and authentication techniques, secure routing protocols, hardware-based security mechanisms, and software-based security solutions. Examples of real-world security breaches and successful implementations of security solutions are also presented to provide practical insights.

Design of Robotics Implementation of A Fingerprint Biometric System

Biometrics, rooted in the Greek terms "bios" (life) and "metrikos" (measure), represents a modern approach to identifying individuals based on their unique physical characteristics, such as fingerprints, facial features, and iris patterns. In today's increasingly digital landscape, traditional security methods like passwords and keys have become inadequate in safeguarding sensitive information. The rise of sophisticated cyberattacks and unauthorized access has highlighted the need for more advanced and secure authentication methods. This project aims to design and implement a fingerprint biometric system using robotics and FPGA technology. The system is intended to provide a more reliable and secure method of verifying identities by capturing and processing fingerprint images. It will extract minutiae points, the unique features within a fingerprint, to create a robust authentication mechanism. This approach promises to enhance security by offering an alternative to conventional methods that are often vulnerable to breaches. In developing this biometric system, the focus will be on ensuring that it can be effectively integrated into various applications where secure access control is crucial. By leveraging the unique and immutable nature of fingerprints, the system will reduce the risks associated with password reuse, theft, and unauthorized sharing. The inclusion of continuous authentication techniques will further strengthen security, ensuring that the user’s identity is verified throughout a session, not just at the initial login. Additionally, the project will incorporate version control mechanisms to track the development and deployment of the system, ensuring that any changes or updates can be easily managed and reviewed. This will help maintain the system's reliability and effectiveness over time, allowing it to adapt to evolving security challenges. Through this work, we aim to contribute to the broader adoption of biometric technologies in securing digital and physical environments, paving the way for more secure and seamless user experiences.

ShouldAR: Detecting Shoulder Surfing Attacks Using Multimodal Eye Tracking and Augmented Reality

Shoulder surfing attacks (SSAs) are a type of observation attack designed to illicitly gather sensitive data from "over the shoulder" of victims. This attack can be directed at mobile devices, desktop screens, Personal Identification Number (PIN) pads at an Automated Teller Machine (ATM), or written text. Existing solutions are generally focused on authentication techniques (e.g., logins) and are limited to specific attack scenarios (e.g., mobile devices or PIN Pads). We present ShotjldAR, a mobile and usable system to detect SSAs using multimodal eye gaze information (i.e., from both the potential attacker and victim). ShouldAR uses an augmented reality headset as a platform to incorporate user eye gaze tracking, rear-facing image collection and eye gaze analysis, and user notification of potential attacks. In a 24-participant study, we show that the prototype is capable of detecting 87.28% of SSAs against both physical and digital targets, a two-fold improvement on the baseline solution using a rear-facing mirror, a widely used solution to the SSA problem. The ShouldAR approach provides an AR-based, active SSA defense that applies to both digital and physical information entry in sensitive environments.

Retraction Note: A review of hashing based image authentication techniques

Retraction Note: A review of hashing based image authentication techniques

Fault-tolerant security-efficiency combined authentication scheme for manned-unmanned teaming

Manned-unmanned teaming (MUM-T) is an emerging network system which interconnects manned aerial vehicles (MAVs) with unmanned aerial vehicles (UAVs) to enhance mission effectiveness and reduce workloads. Like other wireless systems, MUM-T is prone to attacks from the open communication channel. Therefore, an authentication scheme is required to establish secure and trusted communication between the MAV and the UAV. However, existing schemes fail to provide adequate security features and necessary efficiency, mostly due to their incomprehensive threat modeling and improper use of authentication techniques. Moreover, traditional centralized architecture of the authentication server leads to the single point of failure (SPOF) problem, which jeopardizes the robustness and scalability of MUM-T. In this paper, we propose a fault-tolerant authentication scheme for MUM-T that will solve these problems. To enhance its security, we construct a new threat model consisting of adversary's capabilities, security features, and security challenges to ensure the security of a scheme under combination attacks. To preserve the highest possible efficiency, we propose the design principle of using lightweight primitives for authentication and applying public key operations in key establishment. To address the SPOF problem, we employ a distributed fault-tolerant mechanism to share registration information within authentication servers and defend against faulty nodes. As is demonstrated by the security proof and performance comparison, our scheme succeeds in improving security and reducing overall costs, which provides a better solution than existing schemes.

Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques

The global demand for organic foods, driven by health benefits and consumer preferences, necessitates reliable methods for distinguishing organic products from their inorganic counterparts. This study investigates the application of dual handheld near infrared (NIR) spectroscopy devices, SCiO and Tellspec, combined with chemometric techniques for the nondestructive differentiation of organic and inorganic pineapple juices. The objective was to establish a rapid and robust method to differentiate organic pineapple juice from inorganic juice using unique spectral data from the two devices. Eighty-four pineapple juice samples were analyzed with preprocessing techniques, including mean centering, multiplicative scatter correction, standard normal variate, first derivative, and second derivative applied to the spectral data. Partial least squares discriminant analysis (PLS-DA) was employed for classification, and variable importance in projection (VIP) was used for optimal wavelength selection. The results demonstrated that the Tellspec scanner, particularly with second derivative preprocessing, achieved high accuracy in differentiating organic from inorganic pineapple juice. The fusion of data from both SCiO (740–1070 nm) and Tellspec (900–1700 nm) scanners, without preprocessing, coupled with the PLS-DA model, achieved perfect classification accuracy, sensitivity, and specificity (100 %) in both training and testing sets. This study highlights the potential of integrating dual handheld NIR spectroscopy with chemometrics to effectively and accurately classify organic and inorganic pineapple juices. The findings support using these advanced techniques for quality assurance and authentication in the food industry.

PoAh 2.0: AI-empowered dynamic authentication based adaptive blockchain consensus for IoMT-edge workflow

This paper introduces a significant advancement in the Proof of Authentication (PoAh) consensus algorithm, designed specifically for resource-constrained Internet of Things (IoT) devices. Building upon the foundations of PoAh consensus, this enhanced iteration, known as PoAh 2.0, integrates Artificial Intelligence (AI) at the block creator node level. This novel approach allows for the generation of block transactions embedded with AI-determined sensitivity and other applicable transaction-related metadata, a pioneering concept in this domain. The verifier node, a trusted entity, is tasked with verifying incoming blocks, utilizing the block header and its metadata information to determine authenticity while preserving the privacy of the content of the block’s data. A core innovation of PoAh 2.0 is its dynamic authentication mechanism, which adapts to the sensitivity level of the data within each block, behaving in an adaptive way based on the situation. AI plays a crucial role in this process, ensuring the block’s integrity and security are maintained. To demonstrate the efficacy of this advanced AI-enabled PoAh 2.0 consensus, we conducted a case study in an Internet of Medical Things (IoMT)-based eHealth scenario. The results from this study reveal that our developed dynamic authentication technique not only significantly enhances the original PoAh version but also establishes a new benchmark in block validation and security for eHealth applications. The integration of AI and improved dynamic authentication, calibrated to the security needs of each block, marks a novel and significant stride in blockchain research. This development not only enriches the current understanding of blockchain applications in IoT, but also sets a new direction for future research in secure and efficient blockchain implementations in the IoMT-Edge centric eHealth landscape.

A reversible-zero watermarking scheme for medical images

The paper addresses the issue of ensuring the authenticity and copyright of medical images in telemedicine applications, with a specific emphasis on watermarking methods. While several systems only concentrate on identifying tampering in medical images, others also provide the capacity to restore the tampered regions upon detection. While several authentication techniques in medical imaging have successfully achieved their goals, previous research underscores a notable deficiency: the resilience of these schemes against unintentional attacks has not been sufficiently examined or emphasized in previous research. This indicates the need for further development and investigation in improving the robustness of medical image authentication techniques against unintentional attacks. This research proposes a Reversible-Zero Watermarking approach as a solution to address these problems. The new approach merges the advantages of both the reversible and zero watermarking techniques. This system is comprised of two parts. The first part is a zero-watermarking technique that uses VGG19-based feature extraction and watermark information to establish an ownership share. The second part incorporates this ownership share into the image in a reversible manner using a combination of a discrete wavelet transform, an integer wavelet transform, and a difference expansion. Research findings confirm that the suggested watermarking approach for medical images demonstrates substantial enhancements compared to current methodologies. Research findings indicate that NC values are often around 0.9 for different attacks, whereas BER values are close to 0. It demonstrates exceptional qualities in being imperceptible, distinguishable, and robust. Additionally, the system provides a persistent verification feature that functions independently of disputes or third-party storage, making it the preferred choice in the domain of medical image watermarking.