Traditional Authentication Methods Research Articles

B-ERAC: Blockchain-Enabled Role-Based Access Control for Secure IoT Device Communication

Security risks are increasingly concerning as the Internet of Things (IoT) expands. Authentication, access control, and authorization present significant challenges for resource-constrained IoT devices. Traditional authentication methods often require enhancements for these devices, but Blockchain technology presents a potential solution. Decentralized and distributed, Blockchain eliminates a single point of failure and relies on Elliptic Curve Cryptography (ECC) for robust security. We have introduced a cutting-edge solution to fortify communication security within IoT devices across supply chain ecosystems. By harnessing the power of Blockchain technology, our framework incorporates smart contracts, adheres to ES256 encryption standards, and seamlessly integrates with Infura API. These components establish stringent access controls, ensure data integrity, and enhance transparency throughout supply chain processes. The framework’s robust architecture facilitates swift and secure transactions, bolsters traceability efforts, and effectively mitigates potential security risks. With its scalable design and reliable functionality, this framework emerges as a pivotal asset for optimizing IoT device communication within dynamic supply chain environments. The use of ProVerif in our analysis provides a formal guarantee of the correctness of our access control mechanisms.

CardioGuard: AI-driven ECG authentication hybrid neural network for predictive health monitoring in telehealth systems

The increasing integration of telehealth systems underscores the importance of robust and secure methods for patient data management. Traditional authentication methods, such as passwords and PINs, are prone to breaches, underscoring the need for more secure alternatives. Therefore, there is a need for alternative approaches that provide enhanced security and user convenience. Biometric-based authentication systems uses individuals unique physical or behavioral characteristics for identification, have emerged as a promising solution. Specifically, Electrocardiogram (ECG) signals have gained attention among various biometric modalities due to their uniqueness, stability, and non-invasiveness. This paper presents CardioGaurd, a deep learning-based authentication system that leverages ECG signals—unique, stable, and non-invasive biometric markers. The proposed system uses a hybrid Convolution and Long short-term memory based model to obtain rich characteristics from the ECG signal and classify it as authentic or fake. CardioGaurd not only ensures secure access but also serves as a predictive tool by analyzing ECG patterns that could indicate early signs of cardiovascular abnormalities. This dual functionality enhances patient security and contributes to AI-driven disease prevention and early detection. Our results demonstrate that CardioGaurd offers superior performance in both security and potential predictive health insights compared to traditional models, thus supporting a shift towards more proactive and personalized telehealth solutions.

Security authentication scheme based on chebyshev chaotic mapping for library network

With the rapid advancement of information technology, the security of digital libraries, as critical platforms for modern knowledge dissemination, has become increasingly significant. User identity authentication in digital libraries is pivotal to ensuring system security. Traditional authentication methods often have security vulnerabilities and cannot effectively prevent unauthorized access. Therefore, this paper proposes a security authentication scheme based on chebyshev chaotic mapping for library. It addresses security issues in identity authentication in digital libraries. The proposed scheme integrates smart card, password and biometric factor, leveraging the chebyshev chaotic mapping algorithm to achieve stringent user authentication and generate secure session keys, thereby effectively countering security threats. Additionally, the scheme employs physical unclonable function (PUF) to enhance the protection of stored confidential data. The security of this scheme is rigorously analyzed using the Scyther. Security analysis and performance experiments indicate that the scheme not only ensures security but also optimizes authentication efficiency.

A proposed biometric authentication hybrid approach using iris recognition for improving cloud security

Biometric systems have gained attention as a more secure alternative to traditional authentication methods. However, these systems are not without their technical limitations. This paper presents a hybrid approach that combines edge detection and segmentation techniques to enhance the security of cloud systems. The proposed method uses iris recognition as a biometric paradigm, taking advantage of the iris' unique patterns. We performed feature extraction and classification using hamming distance (HD) and convolutional neural networks (CNN). We validated the experimental findings using various datasets, such as MMU, IITD, and CASIA Iris Interval V4. We compared the proposed method's results to previous research, demonstrating recognition rates of 99.50 % on MMU using CNN, 97.18 % on IITD using CNN, and 95.07 % on CASIA using HD. These results indicate that the proposed method outperforms other classifiers used in previous research, showcasing its effectiveness in improving cloud security services.

Review of Authentication Systems based on Electroencephalogram

Traditional authentication methods, such as the use of passwords and fingerprints, are susceptible to the risks of theft, loss, and forgery. However, an innovative and secure alternative exists in the form of electroencephalogram (EEG)-based authentication systems, which operate by measuring distinctive brainwave patterns. This particular review undertakes a comprehensive analysis of the current state of EEG-based authentication, delving into its advantages, challenges, and potential future directions. In doing so, we examine the underlying principles governing the acquisition and processing of EEG signals, explore the various techniques employed for feature extraction and classification, and evaluate the performance of existing systems. Moreover, we emphasize the significant advantages offered by EEG-based authentication, including its exceptional accuracy, capacity for liveness detection, and robust resistance to spoofing attempts. Nevertheless, we must also acknowledge and address the various obstacles that must be overcome to facilitate wider adoption of this authentication method, encompassing concerns relating to hardware affordability, user acceptance, and data privacy. Finally, we outline a series of promising research avenues that can potentially address these challenges and unlock the complete potential of EEG-based authentication, thereby enabling secure and convenient access control across a diverse range of domains.

A Blockchain-Based Privacy Preserving Intellectual Property Authentication Method

With the continuous advancement of information technology, a growing number of works, including articles, paintings, and music, are being digitized. Digital content can be swiftly shared and disseminated via the Internet. However, it is also vulnerable to malicious plagiarism, which can seriously infringe upon the rights of creators and dampen their enthusiasm. To protect creators’ rights and interests, a sophisticated method is necessary to authenticate digital intellectual property rights. Traditional authentication methods rely on centralized, trustworthy organizations that are susceptible to single points of failure. Additionally, these methods are prone to network attacks that can lead to data loss, tampering, or leakage. Moreover, the circulation of copyright information often lacks transparency and traceability in traditional systems, which leads to information asymmetry and prevents creators from controlling the use and protection of their personal information during the authentication process. Blockchain technology, with its decentralized, tamper-proof, and traceable attributes, addresses these issues perfectly. In blockchain technology, each node is a peer, ensuring the symmetry of information. However, the transparent feature of blockchains can lead to the leakage of user privacy data. Therefore, this study designs and implements an Ethereum blockchain-based intellectual property authentication scheme with privacy protection. Firstly, we propose a method that combines elliptic curve cryptography (ECC) encryption with digital signatures to achieve selective encryption of user personal information. Subsequently, an authentication algorithm based on Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is adopted to complete the authentication of intellectual property ownership while encrypting personal privacy data. Finally, we adopt the InterPlanetary File System (IPFS) to store large files, solving the problem of blockchain storage space limitations.

AI-driven biometrics for secure fintech: Pioneering safety and trust

AI-driven biometrics is revolutionizing the security landscape in the financial technology (FinTech) sector, enhancing safety measures and fostering trust among users. This review explores the role of AI-driven biometrics in securing FinTech operations, highlighting its benefits and implications. AI-driven biometrics refers to the use of artificial intelligence (AI) algorithms to analyze biological data for authentication purposes. This technology has gained significant traction in the FinTech sector due to its ability to provide a higher level of security than traditional authentication methods. By analyzing unique biological traits such as fingerprints, facial features, and voice patterns, AI-driven biometrics can accurately verify the identity of users, making it difficult for unauthorized individuals to gain access to sensitive financial information. One of the key benefits of AI-driven biometrics in FinTech is its ability to enhance security measures. Traditional authentication methods such as passwords and PINs are increasingly vulnerable to hacking and fraud. AI-driven biometrics, on the other hand, offers a higher level of security by using biological traits that are unique to each individual. This makes it significantly more difficult for fraudsters to gain access to sensitive financial information. In addition to enhancing security, AI-driven biometrics also improves the user experience. By replacing traditional authentication methods with biometric authentication, users can access their accounts more quickly and conveniently, without the need to remember complex passwords or PINs. This not only improves the overall user experience but also reduces the risk of user error and account lockouts. Overall, AI-driven biometrics is pioneering safety and trust in the FinTech sector by providing a higher level of security and enhancing the user experience. As this technology continues to evolve, it is likely to play an increasingly important role in securing financial transactions and fostering trust among users.

Identifying Fake Products through a Barcode based Blockchain System

Abstract: Counterfeit products pose a significant threat to consumers, businesses, and economies worldwide. Traditional methods of authentication often fall short in effectively combating this issue. In response, this paper proposes a novel approach utilizing a barcode based blockchain system to authenticate products and detect counterfeit items. By integrating blockchain technology with barcode scanning, consumers and retailers can verify the authenticity of products throughout the supply chain. The system employs smart contracts to record product information and transactions securely, providing a transparent and immutable record of each item's journey. Through this approach, stakeholders can mitigate the risks associated with counterfeit goods, safeguard brand reputation, and enhance consumer trust. This paper outlines the architecture of the proposed system, discusses its potential benefits, and addresses implementation challenges. Furthermore, it explores the implications of integrating blockchain technology with barcode scanning for combating counterfeit products and enhancing supply chain transparency

Biometric ATM Authentication System

As technology progresses, traditional authentication methods like Personal Identification Numbers (PINs) and passwords are becoming more prone to security breaches. To address this issue, biometric-based authentication systems are being seen as a reliable solution to boost security in different areas, such as Automated Teller Machines (ATMs). This study delves into the deployment, advantages, obstacles, and future potential of a biometric-based ATM system. By examining current research, real-life examples, and technological innovations, the aim of this paper is to shed light on the efficiency and viability of biometric authentication in ATM systems. Key Words: Enhanced Security, Authentication, Efficiency, Reliability

Internet Users' Willingness to Disclose Biometric Data for Continuous Online Account Protection: An Empirical Investigation

Continuous authentication has emerged as a promising approach to increase user account security for online services. Unlike traditional authentication methods, continuous authentication provides ongoing security throughout the session, protecting against session takeover attacks due to illegitimate access. The effectiveness of continuous authentication systems relies on the continuous processing of users' sensitive biometric data. To balance security and privacy trade-offs, it's crucial to understand when users are willing to disclose biometric data for enhanced account security, addressing inevitable privacy concerns and user acceptance. To address this knowledge gap, we conducted an online study with 830 participants from the U.S., aiming to investigate user perceptions towards continuous authentication across different classes of online services. Our analysis identified four groups of biometric traits that directly reflect users' willingness to disclose them. Our findings demonstrate that willingness to disclose is influenced by both the specific biometric traits and the type of online service involved. User perceptions are strongly shaped by factors such as response efficacy, perceived privacy risks associated with the biometric traits, and concerns about the service providers' handling of such data. Our results emphasize the inadequacy of one-size-fits-all solutions and provide valuable insights for the design and implementation of continuous authentication systems.

Towards Secure Identification: A Comparative Analysis of Biometric Authentication Techniques

This paper provides an overview and analysis of existing biometric authentication methods. Biometric authentication enhances the security of traditional authentication methods by utilizing unique biological characteristics of individuals, such as fingerprints, facial features, or iris patterns, to verify identity. However, implementing biometric authentication may involve initial setup costs for service providers due to the need for specialized hardware and software. Additionally, users may experience some initial inconvenience during the enrollment process to register their biometric data. This paper presents a comparative analysis of the usability of biometric authentication techniques. Initially, a survey of existing work is conducted by comparing techniques, algorithms, and metrics used in research. Later the challenges of the distinct types of authentication techniques are discussed, and their problems are discovered. Subsequently, the findings of a quantitative study based on a are presented, aimed at assessing the usability of those techniques. The findings indicate that authentic biometric techniques are perceived as usable.

Risk Analysis Research on SMS Verification Code and Biometric Recognition Technology

This paper delves into the ongoing transformation in the domain of digital identity security. It specifically focuses on the paradigm shift from traditional methods of authentication, such as passwords and text message verification codes, towards the use of biometric recognition technology. By scrutinizing the inherent weaknesses linked with the utilization of text message verification codes, the paper underlines the demand for more trustworthy and secure solutions for authentication. Following this, biometric authentication is thoroughly examined, with a highlight on its unique features, the convenience it provides to users, and its capacity for ongoing verification. The discussion extends to practical applications of biometrics in a variety of sectors, including healthcare and high-security workplaces, as well as in situations where employing text message verification is unsuitable. A focal point of the discussion is multi-modal biometric authentication. While acknowledging the potential hurdles and future possibilities tied to the adoption of biometric technology, the paper offers an objective perspective. The overall intent of this research is to furnish insights that will enable informed decision-making regarding the deployment and improvement of digital identity verification methods across a wide range of industries.

EarSlide

Ear wearables (earables) are emerging platforms that are broadly adopted in various applications. There is an increasing demand for robust earables authentication because of the growing amount of sensitive information and the IoT devices that the earable could access. Traditional authentication methods become less feasible due to the limited input interface of earables. Nevertheless, the rich head-related sensing capabilities of earables can be exploited to capture human biometrics. In this paper, we propose EarSlide, an earable biometric authentication system utilizing the advanced sensing capacities of earables and the distinctive features of acoustic fingerprints when users slide their fingers on the face. It utilizes the inward-facing microphone of the earables and the face-ear channel of the ear canal to reliably capture the acoustic fingerprint. In particular, we study the theory of friction sound and categorize the characteristics of the acoustic fingerprints into three representative classes, pattern-class, ridge-groove-class, and coupling-class. Different from traditional fingerprint authentication only utilizes 2D patterns, we incorporate the 3D information in acoustic fingerprint and indirectly sense the fingerprint for authentication. We then design representative sliding gestures that carry rich information about the acoustic fingerprint while being easy to perform. It then extracts multi-class acoustic fingerprint features to reflect the inherent acoustic fingerprint characteristic for authentication. We also adopt an adaptable authentication model and a user behavior mitigation strategy to effectively authenticate legit users from adversaries. The key advantages of EarSlide are that it is resistant to spoofing attacks and its wide acceptability. Our evaluation of EarSlide in diverse real-world environments with intervals over one year shows that EarSlide achieves an average balanced accuracy rate of 98.37% with only one sliding gesture.

Multiple Approaches Towards Authentication Using Keystroke Dynamics

In the realm of global computer security, safeguarding sensitive data and computer systems stands as an ongoing challenge. Striking the delicate balance between convenient access for legitimate users and thwarting imposter attacks is imperative. Traditional authentication methods, primarily reliant on username and password schemes, have been the longstanding cornerstone of cyber system authentication. Yet, this conventional approach is marred by inherent vulnerabilities, including password exchange, shoulder surfng, brute force attacks, dictionary attacks, speculation, phishing, and emerging threats. In response to these shortcomings, biometrics technology has emerged as a reliable alternative for authentication and verification, with Keystroke Dynamics being a well-established method. This research advances Keystroke Dynamics-based authentication through cutting-edge technologies, including machine learning, deep learning, and neural networks. A quantitative assessment demonstrates remarkable results—Machine Learning achieves 99.9% accuracy with the Random Forest model, Deep Learning combines CNN with GRU to reach 99.31% accuracy, and the DBN model registers at 98.01%. In the realm of neural networks, Bi-CNN and FFM-NN excel, with accuracy rates of 96.8% and 94.7%, respectively. These findings lay the foundation for robust, secure authentication systems, ensuring the protection of sensitive data and computer assets in our interconnected world.

EFFECTIVENESS OF BLOCKCHAIN LOGGING AND SSO IN CYBER SECURITY MECHANISMS

With the rise of cyber threats in the era of digital transformation, protecting information systems becomes crucial for ensuring data reliability and security. This is especially true for authentication and logging systems, which are key elements in identifying and countering unauthorized access. The use of identical credentials and traditional authentication methods opens up wide opportunities for cybercriminals. This article explores the use of blockchain technology as a means to combat cyber threats through the implementation of immutable, decentralized logging and authentication systems. Blockchain offers unique advantages, such as data immutability and distributed storage, which can significantly complicate unauthorized interference in security systems. Current trends in the field of cybersecurity are examined, particularly the challenges associated with data compromise and ineffective information exchange between systems. An important part of the article is the analysis of recent research focused on the capabilities of blockchain in the development of identification and authentication systems based on decentralized identifiers and the integration of consensus technologies. The main goal of the research is to identify and develop technological solutions aimed at enhancing the security, resilience, and efficiency of logging and authentication systems through the application of blockchain. Additionally, innovative approaches to identification and authentication that can strengthen protection against cyber threats are considered.

A NavCom Signal Authentication Scheme Based on Twice Two-Way Satellite Time Transfer

Low Earth Orbit (LEO) satellite communication systems typically achieve identity authentication through the encryption and decryption of two-way information, which requires complex key management systems. In contrast, the integration of navigation and communication (NavCom) signals provides novel opportunities for physical observation and authentication solutions due to its measurement functions. This paper introduces a novel signal authentication scheme based on twice two-way satellite time transfer (TWSTT) for LEO satellite systems. It leverages the non-mutated nature of the clock difference to ascertain the legitimacy of the signal by measuring the clock difference of signals at different instances. Unlike traditional authentication methods, this approach directly exploits the temporal and spatial characteristics of the signal, negating the necessity for intricate authorization key systems. Additionally, it adeptly tackles the challenges posed by spoofing interference. The performance analysis indicates that this scheme can achieve a high detection probability for the repeater spoofing signal in the low carrier-to-noise ratio conditions.

Single-board device individual authentication based on hardware performance and autoencoder transformer models

The proliferation of the Internet of Things (IoT) has led to the emergence of crowdsensing applications, where a multitude of interconnected devices collaboratively collect and analyze data. Ensuring the authenticity and integrity of the data collected by these devices is crucial for reliable decision-making and maintaining trust in the system. Traditional authentication methods are often vulnerable to attacks or can be easily duplicated, posing challenges to securing crowdsensing applications. Besides, current solutions leveraging device behavior are mostly focused on device identification, which is a simpler task than authentication. To address these issues, an individual IoT device authentication framework based on hardware behavior fingerprinting and Transformer autoencoders is proposed in this work. To support the design, a threat model details the security problems faced when performing hardware-based authentication in IoT. This solution leverages the inherent imperfections and variations in IoT device hardware to differentiate between devices with identical specifications. By monitoring and analyzing the behavior of key hardware components, such as the CPU, GPU, RAM, and Storage on devices, unique fingerprints for each device are created. The performance samples are considered as time series data and used to train outlier detection transformer models, one per device and aiming to model its normal data distribution. Then, the framework is validated within a spectrum crowdsensing system leveraging Raspberry Pi devices. After a pool of experiments, the model from each device is able to individually authenticate it between the 45 devices employed for validation. An average True Positive Rate (TPR) of 0.74±0.13 and an average maximum False Positive Rate (FPR) of 0.06±0.09 demonstrate the effectiveness of this approach in enhancing authentication, security, and trust in crowdsensing applications.

Revolutionizing Authentication: Google Passkeys Technology

In the ever-evolving landscape of digital security, the traditional methods of authentication are facing increasing challenges in safeguarding sensitive information and user privacy. Google Passkeys technology emerges as a pioneering solution poised to redefine the way users access their accounts and protect their digital identities. This abstract delves into the innovation of Google Passkeys technology, exploring its fundamental principles and potential impact. Passkeys represent a paradigm shift in authentication by eliminating the need for passwords, which have long been susceptible to breaches and often result in user frustration. Instead, Passkeys harness cutting-edge cryptographic techniques to create a seamless and secure authentication process. At its core, Google Passkeys rely on a unique combination of hardware-based security keys and advanced cryptographic algorithms. Users are equipped with physical security keys, such as USB tokens or smartphones with built-in security features, which act as the "key" to their digital identities. These keys, paired with secure communication protocols, provide a multifaceted approach to authentication that significantly enhances security.

Detecting Risky Authentication Using the OpenID Connect Token Exchange Time.

With the rise in sophisticated cyber threats, traditional authentication methods are no longer sufficient. Risk-based authentication (RBA) plays a critical role in the context of the zero trust framework-a paradigm shift that assumes no trust within or outside the network. This research introduces a novel proposal as its core: utilization of the time required by OpenID Connect (OIDC) token exchanges as a new RBA feature. This innovative approach enables the detection of tunneled connections without any intervention from the user's browser or device. By analyzing the duration of OIDC token exchanges, the system can identify any irregularities that may signify unauthorized access attempts. This approach not only improves upon existing RBA frameworks but is also in alignment with the broader movement toward intelligent and responsive security systems.

An Innovative Architecture of DRAM PUF

Cryptographic solutions based on traditional authentication methods are susceptible to several attacks on secret keys. Dynamic random access memory (DRAM)-based physical unclonable functions (PUF) are described as promising security building blocks to enable cryptography and authentication services. PUFs frequently suffer reliability concerns since they are sensitive to both internal and exterior noises. The need for enhanced resistance and dependability results in significant additional overheads. In this work, we proposed a DRAM-PUF based on the inclusion of selective hardware features in the computation. The proposed solution offers a higher number of challenge-response pairs (CRP) without additional circuitry. An innovative structure of PUF is presented in the paper which offers a reliable challenge-response pair module for authentication and authorization based on the ubiquitous nature of memory without the need for an additional circuit. DRAM PUF utilized the random startup value initialized by a capacitor followed by a random number generator. Our proposed PUF shows a reliability of 99.46% with temperature variation, a reliability of 99.5% with supply voltage variation, a uniqueness of 49.46%, a bit aliasing of 46.875%, and a uniformity of 47.65%.