Biometric User Authentication Research Articles

Outlier detection for keystroke biometric user authentication.

User authentication is a fundamental aspect of information security, requiring robust measures against identity fraud and data breaches. In the domain of keystroke dynamics research, a significant challenge lies in the reliance on imposter datasets, particularly evident in real-world scenarios where obtaining authentic imposter data is exceedingly difficult. This article presents a novel approach to keystroke dynamics-based authentication, utilizing unsupervised outlier detection techniques, notably exemplified by the histogram-based outlier score (HBOS), eliminating the necessity for imposter samples. A comprehensive evaluation, comparing HBOS with 15 alternative outlier detection methods, highlights its superior performance. This departure from traditional dependence on imposter datasets signifies a substantial advancement in keystroke dynamics research. Key innovations include the introduction of an alternative outlier detection paradigm with HBOS, increased practical applicability by reducing reliance on extensive imposter data, resolution of real-world challenges in simulating fraudulent keystrokes, and addressing critical gaps in existing authentication methodologies. Rigorous testing on Carnegie Mellon University's (CMU) keystroke biometrics dataset validates the effectiveness of the proposed approach, yielding an impressive equal error rate (EER) of 5.97%, a notable area under the ROC curve of 97.79%, and a robust accuracy (ACC) of 89.23%. This article represents a significant advancement in keystroke dynamics-based authentication, offering a reliable and efficient solution characterized by substantial improvements in accuracy and practical applicability.

Multimodal biometric user authentication using improved decentralized fuzzy vault scheme based on Blockchain network

Biometric security has emerged as a major concern in the field of Biometric Systems. The existing centralized Biometric Cryptosystems approach still suffers from privacy issues and network-based attacks. In particular, the Biometric Cryptosystems are susceptible to Attack via Record Multiplicity, brute force and collusion attacks. To address these issues, this paper proposes a multimodal biometric user authentication scheme using a decentralized fuzzy vault based on Blockchain technology. In this scheme, the facial biometric images and dorsal hand images together form the multimodal biometric. The proposed security framework is implemented in two phases. In the first phase, multimodal biometric user authentication using an improved fuzzy vault scheme is implemented. In the second phase, a privacy preservation technique using a decentralized fuzzy vault biometric system based on the Blockchain network is implemented. The performance of the proposed framework is verified on the biometric datasets consisting of both genuine and imposter users, and a comparative analysis with the existing state-of-the-art approaches is also presented. The Detection Error Trade-off (DET) curve and the Receiver Operating Characteristic (ROC) curve, obtained for verifying the efficacy of the proposed framework, are also presented. The proposed framework yielded an accuracy of 99.8% and an Equal Error Rate of 0.08. Furthermore, to validate the biometric template protection, the security analysis of the proposed scheme based on irreversibility, unlinkability, and revocability criteria is described. The proposed framework achieves significant improvement in terms of a low False Acceptance Rate (0.08), and a high True Acceptance Rate (0.96).

ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors.

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenience due to passive data acquisition. This paper investigates an electrocardiogram (ECG) based biometric user authentication system using features derived from the Convolutional Neural Network (CNN) and self-supervised contrastive learning. Contrastive learning enables us to use large unlabeled datasets to train the model and establish its generalizability. We propose approaches enabling the CNN encoder to extract appropriate features that distinguish the user from other subjects. When evaluated using the PTB ECG database with 290 subjects, the proposed technique achieved an authentication accuracy of 99.15%. To test its generalizability, we applied the model to two new datasets, the MIT-BIH Arrhythmia Database and the ECG-ID Database, achieving over 98.5% accuracy without any modifications. Furthermore, we show that repeating the authentication step three times can increase accuracy to nearly 100% for both PTBDB and ECGIDDB. This paper also presents model optimizations for embedded device deployment, which makes the system more relevant to real-world scenarios. To deploy our model in IoT edge sensors, we optimized the model complexity by applying quantization and pruning. The optimized model achieves 98.67% accuracy on PTBDB, with 0.48% accuracy loss and 62.6% CPU cycles compared to the unoptimized model. An accuracy-vs-time-complexity tradeoff analysis is performed, and results are presented for different optimization levels.

Challenges and Opportunities of Biometric User Authentication in the Age of IoT: A Survey

While the Internet of Things (IoT) devices, such as smartwatches, provide a range of services from managing financial transactions to monitoring smart homes, these devices often lead to gateways for malicious access to a user’s cyber-physical space. Biometric-based authentications are becoming popular to secure IoT devices and provide other services. However, when to use what type of biometrics remains challenging due to various factors, including sensing and computing requirements, user interaction requirements, stability over time, and application scenarios, among others. Unlike soft biometrics, e.g., gait, traditional biometrics, e.g., iris, are more stable over time but require active user input and robust sensing and computing, which limits their continuous adaptability to secure a user’s cyber-physical space. Additionally, the integration of new sensors to IoT devices brings opportunistic data types that can individually or in combination with other common biometrics to identify a user. There is a dearth of knowledge about the limitations and applications of new opportunistic biometrics and their combinations with existing biometrics obtained from single or multiple IoT-connected devices. Therefore, this article thoroughly discusses different biometrics that can be implemented on IoT devices to understand the potential of biometric authentications better.

EdBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms

We present edBB-Demo, a demonstrator of an AI-powered research platform for student monitoring in remote education. The edBB platform aims to study the challenges associated to user recognition and behavior understanding in digital platforms. This platform has been developed for data collection, acquiring signals from a variety of sensors including keyboard, mouse, webcam, microphone, smartwatch, and an Electroencephalography band. The information captured from the sensors during the student sessions is modelled in a multimodal learning framework. The demonstrator includes: i) Biometric user authentication in an unsupervised environment; ii) Human action recognition based on remote video analysis; iii) Heart rate estimation from webcam video; and iv) Attention level estimation from facial expression analysis.

Biometric User Authentication System via Fingerprints Using Novel Hybrid Optimization Tuned Deep Learning Strategy

Biometric User Authentication System via Fingerprints Using Novel Hybrid Optimization Tuned Deep Learning Strategy

Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authentication

Powered by new advances in sensor development and artificial intelligence, the decreasing cost of computation, and the pervasiveness of handheld computation devices, biometric user authentication (and identification) is rapidly becoming ubiquitous. Modern approaches to biometric authentication, based on sophisticated machine learning techniques, cannot avoid storing either trained-classifier details or explicit user biometric data, thus exposing users’ credentials to falsification. In this paper, we introduce a secure way to handle user-specific information involved with the use of artificial neural networks for biometric authentication. Our proposed architecture, called a Neural Fuzzy Extractor (NFE), allows the coupling of pre-existing classifiers with fuzzy extractors, through an artificial-neuralnetwork-based buffer called an expander, with minimal or no performance degradation. The NFE thus offers all the performance advantages of modern deep-learningbased classifiers and all the security of standard fuzzy extractors. We demonstrate the NFE retrofit of a few classic artificial neural networks, for simple biometric authentication scenarios.

Multi-modal biometric fusion based continuous user authentication for E-proctoring using hybrid LCNN-Salp swarm optimization.

In Covid 19, pandemic remote proctoring of the employee or human being is evolved as a big challenge for the information retrieval process. On the other side, memory-based system access authentication is becoming outdated and less preferred for live applications, especially where data security and customer privacy are crucial. Multi-modal authentication has outperformed the unimodal process with high accuracy and improved security in the user authentication field. Multi-modal biometric verification includes user attributes such as keystrokes, iris, speech, face, etc. For real-time execution of multi-modal biometric fusion-based live tracking for compatible applications. The study proposes an efficient continuous biometric user authentication system for a new challenge of pandemic time, a live online authentication of the evaluation process (CBUA-OE). The proposed CBUA-OE system can address the challenges associated with live proctoring and is also compatible with real-time implementation, deployment of authentication systems. The modified wolf optimization algorithm and CUBA-OE's optimal feature fusion algorithm give an edge over the other contemporary methods and make it more robust. In modern forms of authentication, the classification stage affects the overall outcome of the system, and the model's performance is also a factor of varying quality of datasets. In contrast, a hybrid LCNN-Salp swarm optimization-based classifier is more efficient and consistent in continuous user authentication. Here the performance of the proposed hybrid LCNN-Salp swarm optimization classifier is analyzed with different standard datasets. The results are compared with the existing state-of-art classifiers regarding the accuracy, precision, recall, and F-measure. This projected work is novel in terms of usability factors and scalability to live tracking systems.

Problems of forensic identification of handwriting in forensic examination

The handwritten signature as a characteristic of authentication has been legally and publicly recognised for centuries and has been used in the forensic field for many decades to identify an author. Approaches to biometric user authentication developed in recent years are also based in part on handwriting characteristics, such as automatic signature verification. This article systematically identifies handwriting features published in forensics and analyses which of these properties can be adapted as biometrics to verify the user. A user verification procedure based on a set of 14 forensic characteristics is presented, which is integrated into the biometric verification procedure. The results of the verification of these forensic features are compared in detailed tests with the features of non-forensic data, and it is shown that significant improvements in false detection rates can be achieved by including forensics. For hundreds of years, handwritten signatures have been legally and socially recognised as authentication. The reason for this is the uniqueness of human handwriting. Although a forger with some practice may visually imitate another person’s text or signature, the typical traces resulting from the (studied) behaviour of a victim of forgery are difficult to copy. To check the record, the pressure force of all overlay points with a pen at the beginning or inside the word, written letters on the weight are used. In addition, the way a writer unconsciously or even consciously connects words, letters, or parts of letters to each other is a specific trait for that person. In a forensic examination, experts examine these and other features to prove or disprove the authenticity of signatures or documents. In the field of forensic science, there are many procedures offered based on physical handwriting samples to determine a writer

The Roles of IT Strategies and Security Investments in Reducing Organizational Security Breaches

ABSTRACT This research examines the joint effects of information technology (IT) strategies and security investments on organizational security breaches. We focus on two forms of IT strategies: digitalization and embeddedness in IT outsourcing networks. Our longitudinal analysis of U.S. hospitals demonstrates that IT security investments reduce security breaches in less digitalized organizations but increase security breaches for highly digitalized organizations. Investing in technical network control security systems such as anti-virus and intrusion detection systems reduces external breaches. Implementing identity and access management security systems such as biometric scanning and user authentication decreases internal breaches but increases external breaches. However, organizations’ embeddedness in IT outsourcing networks weakens the impacts of these technologies investments on external breaches but amplifies the negative relationship between identity and access management security systems and internal breaches. Our results offer an alternative understanding of organizational IT security investments and explain contrary results found in prior studies. Practical guidelines on organizational IT security strategies are discussed.

A METHOD FOR USER AUTHENTICATING TO CRITICAL INFRASTRUCTURE OBJECTS BASED ON VOICE MESSAGE IDENTIFICATION

The subject of the research is the protection of critical infrastructure objects from unauthorized access based on voice message identification. The purpose of the research is to consider the main methods and algorithms for identifying an individual by voice, as well as the development of a software implementation of the restricted zone access control system, which allows improving the security of the standard password system by introducing voice authorization technologies and improving the algorithm for selecting voice fragments in audio files. Therefore, the purpose determines following tasks : analysis of the advantages and disadvantages of existing methods of information protection to infrastructure objects; determination of the optimal method of identification by voice message; a description of the main features of the implementation and use of Mel-frequency kastelnik coefficients (MFKC). Are used such methods : system approach, system analysis, theory of algorithms. The following results were obtained: an algorithm was developed for storing users ' voice data for further use in authorization, the voice authorization subsystem; the corresponding software of the computer system has been developed. Conclusions: based on the example of applying the biometric method of user authentication to critical infrastructure objects based on voice message identification, this method of user authentication should be used as an additional method of multi-factor authentication by entering an additional password or another method of biometric user authentication.

Selection of handwritten signature dynamic indicators for user authentication

Selection and evaluation of handwritten signature indicators in user authentication systems are considered. An important and unresolved issue of information security is the effective identification of the user who accesses confidential information. Traditional password protection has a number of disadvantages. As an alternative to the password system or its addition, user identification by biometric characteristics is considered. An identifier that uses biometric characteristics is inextricably linked to the user and it is almost impossible to use it without authorization. The decision about the truth of the user of biometric authentication systems is made on the basis of comparison of his template with the data entered when trying to authenticate. The template is formed on the basis of studying the selected individual characteristics of the user. To do this, we use the biometric characteristics of users, which reflect the dynamic, behavioral characteristics of the person. It is proposed to use a handwritten signature as a biometric characteristic of the user. A handwritten signature is a socially and legally recognized biometric characteristic used for human authentication. It has a rather complex structure and high detail - all this makes solving the problem of user identification by mathematical methods quite complex and requires high computational costs. Another significant disadvantage is that handwritten authentication systems require the installation of additional specialized equipment, which makes the use of such systems as an ordinary means of authentication very expensive. To solve this problem, a scheme for implementing a computer data protection system against unauthorized access based on handwritten signatures using mobile devices based on the Android operating system as signature input devices is proposed. A method of biometric vector generation for use in dynamic biometric user authentication systems is proposed. The optimal characteristics are investigated and the efficiency of using the proposed form biometric characteristics vector is estimated. Certificates of copyrights registration for software applications developed during the work were received.

On the Design and Analysis of a Biometric Authentication System Using Keystroke Dynamics

This paper proposes a portable hardware token for user’s authentication; it is based on the use of keystroke dynamics to verify users biometrically. The proposed approach allows for a multifactor authentication scheme, in which a user cannot be granted access unless they provide a correct password on a hardware token and their biometric signature. The latter is extracted while the user is typing their password. This paper explains the design rationale of the proposed system and provides a comprehensive insight in the development of a hardware prototype of the same. The paper also presents a feasibility study that included a systematic analysis based on training data obtained from 32 users. Our results show that dynamic keystroke can be employed to construct a cost-efficient solution for biometric user authentication with an average error rate of 4.5%.

Fusion of Neuro-Signals and Dynamic Signatures for Person Authentication.

Many biometric systems based on physiological traits such as ones facial characteristics, iris, and fingerprint have been developed for authentication purposes. Such security systems, however, commonly suffer from impersonation attacks such as obfuscation, abrasion, latent samples, and covert attack. More conventional behavioral methods, such as passwords and signatures, suffer from similar issues and can easily be spoofed. With growing levels of private data readily available across the internet, a more robust authentication system is needed for use in emerging technologies and mobile applications. In this paper, we present a novel multimodal biometric user authentication framework by combining the behavioral dynamic signature with the the physiological electroencephalograph (EEG) to restrict unauthorized access. EEG signals of 33 genuine users were collected while signing on their mobile phones. The recorded sequences were modeled using a bidirectional long short-term memory neural network (BLSTM-NN) based sequential classifier to accomplish person identification and verification. An accuracy of 98.78% was obtained for identification using decision fusion of dynamic signatures and EEG signals. The robustness of the framework was also tested against 1650 impersonation attempts made by 25 forged users by imitating the dynamic signatures of genuine users. Verification performance was measured using detection error tradeoff (DET) curves and half total error rate (HTER) security matrices using true positive rate (TPR) and false acceptance rate (FAR), resulting in 3.75% FAR and 1.87% HTER with 100% TPR for forgery attempts.

Applying mobile devices in biometric user authentication systems

Розглянуто використання біометричних характеристик для підвищення ефективності автентифікації користувача. Ідентифікатор, що використовує біометричні характеристики, нерозривно пов’язаний з користувачем, і скористатися ним несанкціоновано практично неможливо. З огляду на це запропоновано використовувати динамічні біометричні характеристики користувачів. Їх перевагою є те, що завдяки наявності динамічної складової ймовірність їх підробки зловмисником є дуже низькою. В рамках багатофакторної системи автентифікації біометрія може значно зменшити шанси злому облікових записів користувачів, оскільки усуває недоліки класичних парольних систем і систем на основі карт доступу. Як біометрична характеристика користувача використовується рукописний підпис. Рукописний підпис є суспільно і законно визнаною біометричною характеристикою, що використовується для автентифікації людини. Він має достатньо складну структуру і високу деталізацію, все це робить вирішення даної проблеми математичними методами досить складним і потребує великих обчислювальних затрат. Також суттєвим недоліком є те, що системи автентифікації з використанням рукописного підпису вимагають встановлення додаткового спеціалізованого обладнання. Тому використання таких систем як рядового засобу автентифікації є дуже дорогим. Наявність сьогодні практично у всіх користувачів мобільних пристроїв, дозволило сформувати ідею використання їх в системах автентифікації. Завдяки цьому запропоновано схему реалізації системи захисту комп’ютерних даних від несанкціонованого доступу на основі рукописного підпису з використанням мобільних пристроїв на основі ОС Android в якості пристроїв введеня підпису. До того ж запропоновано схему встановлення з’єднання між комп’ютером та мобільним пристроєм. ЇЇ особливість в тому, що з’єднання між мобільним пристроєм і комп’ютеорм встановлюється шляхом сканування QR-коду відображеного на моніторі комп’ютера, модулем на мобільному пристрої. Практичну цінність отриманих результатів підтверджено свідоцтвом про реєстрацію авторських прав на розроблений в ході роботи програмний застосунок.

Biometric Smartcard Authentication for Fog Computing

In the IoT scenario, things at the edge can create significantly large amounts of data. Fog Computing has recently emerged as the paradigm to address the needs of edge computing in the Internet of Things (IoT) and Industrial Internet of Things (IIoT) applications. In a Fog Computing environment, much of the processing would take place closer to the edge in a router device, rather than having to be transmitted to the Fog. Authentication is an important issue for the security of fog computing since services are offered to massive-scale end users by front fog nodes.Fog computing faces new security and privacy challenges besides those inherited from cloud computing. Authentication helps to ensure and confirms a user's identity. The existing traditional password authentication does not provide enough security for the data and there have been instances when the password-based authentication has been manipulated to gain access into the data. Since the conventional methods such as passwords do not serve the purpose of data security, research worksare focused on biometric user authentication in fog computing environment. In this paper, we present biometric smartcard authentication to protect the fog computing environment.

BIOMETRIC SMARTCARD AUTHENTICATION FOR FOG COMPUTING

In the IoT scenario, things at the edge can create significantly large amounts of data. Fog Computing has recently emerged as the paradigm to address the needs of edge computing in the Internet of Things (IoT) and Industrial Internet of Things (IIoT) applications. In a Fog Computing environment, much of the processing would take place closer to the edge in a router device, rather than having to be transmitted to the Fog. Authentication is an important issue for the security of fog computing since services are offered to massive-scale end users by front fog nodes.Fog computing faces new security and privacy challenges besides those inherited from cloud computing. Authentication helps to ensure and confirms a user's identity. The existing traditional password authentication does not provide enough security for the data and there have been instances when the password-based authentication has been manipulated to gain access into the data. Since the conventional methods such as passwords do not serve the purpose of data security, research worksare focused on biometric user authentication in fog computing environment. In this paper, we present biometric smartcard authentication to protect the fog computing environment.

신뢰할 수 있는 공간에서 생체인식기반의 모바일 애플리케이션 사용자인증 기법

신뢰할 수 있는 공간에서 생체인식기반의 모바일 애플리케이션 사용자인증 기법

Enhancing the security of modern ICT systems with multimodal biometric cryptosystem and continuous user authentication

Most information and communication technology ICT systems authenticate the users with user name and password, only at the session establishment and no checks are performed further. Current security and transparency of the systems are enhanced by substituting user authentication with multimodal biometric techniques along with the continuous and sequential user authentication. In that the major apprehensions are outsourcing the biometric traits can be easily tainted by the non-legitimate users and the memory consumption will be high. To surmount these disquiets, in this paper, a framework pedestal on multimodal biometric cryptosystem with continuous and chronological authentication is proposed. In the enrolment phase, no raw biometric traits are accumulated instead encrypted and fused binary strings of the multimodal biometric traits are loaded. Subsequently in the authentication phase, authentication is continuous and sequential. As a result the recital of the proposed framework is enhanced in terms of security and memory utilisation.

스마트폰 가속도 센서를 이용한 사용자 인증 방법 연구

With the growth of financial industry with smartphone, interest on user authentication using smartphone has been arisen in these days. There are various type of biometric user authentication techniques, but gait recognition using accelerometer sensor in smartphone does not seem to develop remarkably. This paper suggests the method of user authentication using accelerometer sensor embedded in smartphone. Specifically, calibrate the sensor data from smartphone with 3D-transformation, extract features from transformed data and do principle component analysis, and learn model with using gaussian mixture model. Next, authenticate user data with confidence interval of GMM model. As result, proposed method is capable of user authentication with accelerometer sensor on smartphone as a high degree of accuracy(about 96%) even in the situation that environment control and limitation are minimum on the research.