Biometric Authentication Mechanism Research Articles

Enhancing Digital Cheque Security: A Machine Learning Approach to Signature Fraud Mitigation

Enhancing Digital Cheque Security: A Machine Learning Approach to Signature Fraud Mitigation

Enhancing cloud data security: User-centric approaches and advanced mechanisms

Cloud storage has led to a transformative era of data management for organizations, but this paradigm shift has also introduced critical security challenges. This paper is motivated by the urgent need to strengthen cloud data security against unauthorized access and breaches. Our investigation revolves around the vulnerabilities stemming from distributed links in cloud storage, shedding light on the paramount importance of safeguarding crucial commercial records. These challenges encompass the menace posed by both external attackers and unscrupulous customers, amplified within the complex landscape of multi-tenant architectures. This work presents the Secure-Cloud-Guard algorithm to achieve these goals—a multifaceted approach integrating encryption, biometric authentication, and MAC address security mechanisms. The Secure-Cloud-Guard algorithm, rooted in the user-centric paradigm, enhances data protection in cloud storage environments by orchestrating multiple layers of security. The algorithm’s simulation involves thorough evaluation through encryption performance analysis, biometric authentication testing, and MAC address security validation. The simulation results reveal the effectiveness of our proposed algorithm. Encryption performance metrics showcase the encryption throughput and latency, thereby gauging the efficiency of the encryption process. The biometric authentication simulation calculates the false acceptance rate (FAR) to determine the algorithm’s accuracy and false rejection rate (FRR). The simulation of MAC address security illustrates the algorithm’s ability to authenticate devices through MAC addresses, providing insights into its authentication success rate (ASR). In conclusion, an, aligning with user-centric approaches and incorporating advanced mechanisms, such as encryption, biometric authentication, MAC address security, contribute to the ongoing efforts of fortifying the security landscape of cloud storage and computing

On the issue of ensuring secure access to information systems using biometric authentication based on a fuzzy image of the user’s identity and neural network transformations

Objective. In modern conditions, cryptographic personal authentication technologies are used to access information systems, based on processing biometric information and converting the user’s biometric images into his personal access code. A current area of research is the use of neural network technologies in organizing secure access to information systems. Biometric authentication tools can be classified as highly reliable only if they include cryptographic authentication mechanisms that work together with biometric authentication mechanisms through the conversion of biometric images into a unique cryptographic access code; in this case, a set of biometric identifiers of the user’s identity form his fuzzy image, which is used later during authentication. The purpose of the study is to develop an algorithm for providing secure access to information systems using biometric authentication based on a fuzzy image of the user’s identity and neural network transformations. Method. The development of the algorithm is based on the use of fuzzy logic methods and neural networks. Result. The work reveals the features of biometric identification of the user of information systems. An algorithm is proposed for providing secure access to information systems using biometric authentication based on a fuzzy image of the user’s personality and neural network transformations. Conclusions. Based on two-step authentication of the user’s identity, secure access to the information systems of registered users is implemented. A listing of the program code in Python for creating and training a neural network of a bioidentifier classifier is provided. The materials of the article are of practical value for specialists in the field of providing secure access to information systems using artificial intelligence.

EIDAS Interoperability and Cross-Border Compliance Issues

The eIDAS Regulation provides a common foundation for secure electronic interaction between citizens, businesses, and public authorities. We investigated and identified interoperability and cross-border compliance issues in this paper. We have identified the following weaknesses: Organizational independence, remote access to banking services, remote video identification, use of electronic signatures in public administration, commercial access to the eIDAS network, biometric authentication mechanisms, and, finally, some technical issues with the mechanisms used to provide security and authentication in eIDAS nodes.

SnapUnlock: A Contrastive Learning-Based Contactless Authentication via Heterogeneous Sensors

Contactless authentication is crucial to keep social distance and prevent bacterial infection. However, existing authentication approaches, such as fingerprinting and face recognition, leverage sensors to verify static biometric features. They either increase the probability of indirect infection or inconvenience the users wearing masks. To tackle these problems, we propose a contactless behavioral biometric authentication mechanism that makes use of heterogeneous sensors. We conduct a preliminary study to demonstrate the feasibility of finger snapping as a natural biometric behavior. A prototype-SnapUnlock system was designed and implemented for further real-world evaluation in various environments. SnapUnlock adopts the principle of contrastive-based representation learning to effectively encode the features of heterogeneous readings. With the representations learned, enrolled samples trained with the classifier can achieve superior performances. We extensively evaluate SnapUnlock involving 50 participants in different experimental settings. The results show that SnapUnlock can achieve a 4.2% average false reject rate and 0.73% average false accept rate. Even in a noisy environment, our system performs similar results.

PClusBA: A Novel Partition Clustering-Based Biometric Authentication Mechanism

In any communication paradigm, authentication plays an important role. Common authentication methods such as passwords are vulnerable to brute force attacks and cryptanalysis. Authentication mechanisms based on biometrics prove to be a better alternative owing to their permanence, uniqueness, and non-reproducibility. Fingerprint biometrics is more suitable in the context of network security applications due to its uniqueness and ease of processing. In general, each fingerprint has 30–40 unique points. The lesser the number of unique points extracted from the fingerprint, the more flexible it is to represent a Unique ID (UID). Clustering can be used to prune the unique point count. Since partition clustering gives us the choice of selecting the cluster count apriori, we have used partition clustering in the proposed biometric authentication mechanism named PClusBA. The cluster count is set to eight to obtain a UID of 192 bits in the proposed format. The length of the UID conforms to the NIST standards for enhancing security in commercial applications. The algorithm was implemented and tested using images from DB1 FVC 2002 database and the results were found to be unique for each image.

AuthNet: A Deep Learning Based Authentication Mechanism Using Temporal Facial Feature Movements (Student Abstract)

Deep learning algorithms are widely used to extend modern biometric authentication mechanisms in resource-constrained environments like smartphones, providing ease-of-use and user comfort, while maintaining a non-invasive nature. In this paper, an alternative is proposed, that uses both facial recognition and the unique movements of that particular face while uttering a password. The proposed model is language independent, the password doesn't necessarily need to be a set of meaningful words or numbers, and also, is a contact-less system. When evaluated on the standard MIRACL-VC1 dataset, the proposed model achieved a testing accuracy of 98.1%, underscoring its effectiveness.

A Machine Learning Framework for Biometric Authentication Using Electrocardiogram

This paper introduces a framework for how to appropriately adopt and adjust Machine Learning (ML) techniques used to construct Electrocardiogram (ECG) based biometric authentication schemes. The proposed framework can help investigators and developers on ECG based biometric authentication mechanisms define the boundaries of required datasets and get training data with good quality. To determine the boundaries of datasets, use case analysis is adopted. Based on various application scenarios on ECG based authentication, three distinct use cases (or authentication categories) are developed. With more qualified training data given to corresponding machine learning schemes, the precision on ML-based ECG biometric authentication mechanisms is increased in consequence. ECG time slicing technique with the R-peak anchoring is utilized in this framework to acquire ML training data with good quality. In the proposed framework four new measure metrics are introduced to evaluate the quality of ML training and testing data. In addition, a Matlab toolbox, containing all proposed mechanisms, metrics and sample data with demonstrations using various ML techniques, is developed and made publicly available for further investigation. For developing ML-based ECG biometric authentication, the proposed framework can guide researchers to prepare the proper ML setups and the ML training datasets along with three identified user case scenarios. For researchers adopting ML techniques to design new schemes in other research domains, the proposed framework is still useful for generating ML-based training and testing datasets with good quality and utilizing new measure metrics.

BiLock

User authentication on smart devices is indispensable to keep data privacy and security. It is especially significant for emerging wearable devices such as smartwatches considering data sensitivity in them. However, conventional authentication methods are not applicable for wearables due to constraints of size and hardware, which makes present wearable devices lack convenient, secure and low-cost authentication schemes. To tackle this problem, we reveal a novel biometric authentication mechanism which makes use of sounds of human dental occlusion (i.e., tooth click). We demonstrate its feasibility by comprehensive measurement study, and design a prototype-BiLock with two Android platforms. Extensive real-world experiments have been conducted to evaluate the accuracy, robustness and security of BiLock in different environments. The results show that BiLock can achieve less than 5% average false reject rate and 0.95% average false accept rate even in a noisy environment. Comparative experiments also demonstrate that BiLock possesses advantages in robustness to noise and security against replay and observation attacks over existing voiceprinting schemes.

Biometrics: Accessibility challenge or opportunity?

Biometric recognition is currently implemented in several authentication contexts, most recently in mobile devices where it is expected to complement or even replace traditional authentication modalities such as PIN (Personal Identification Number) or passwords. The assumed convenience characteristics of biometrics are transparency, reliability and ease-of-use, however, the question of whether biometric recognition is as intuitive and straightforward to use is open to debate. Can biometric systems make some tasks easier for people with accessibility concerns? To investigate this question, an accessibility evaluation of a mobile app was conducted where test subjects withdraw money from a fictitious ATM (Automated Teller Machine) scenario. The biometric authentication mechanisms used include face, voice, and fingerprint. Furthermore, we employed traditional modalities of PIN and pattern in order to check if biometric recognition is indeed a real improvement. The trial test subjects within this work were people with real-life accessibility concerns. A group of people without accessibility concerns also participated, providing a baseline performance. Experimental results are presented concerning performance, HCI (Human-Computer Interaction) and accessibility, grouped according to category of accessibility concern. Our results reveal links between individual modalities and user category establishing guidelines for future accessible biometric products.

Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection

Objectives: Nowadays cloud is becoming the major research area because of the security threats against cloud. According to the reports of CSA (cloud security alliance), much more security issues are related to authentication and improper data security etc. Methods/Statistical Analysis: Such problems can be neutralized by adopting biometric authentication mechanism in a novel way by providing protection in every direction since cloud is a medium where much more valuable computational data have been stored and transferred. Findings: In our approach, biometric authentication has been given a new dimension for the different level of security. And the public key and private key encryptions have been used in combination where data needs to be protected. By merging these biometric authentications along with multi-level encryption will give the enhanced security over the cloud and increase the confidentiality to use the cloud among cloud consumers as well as cloud providers. Applications/Improvements: Implementation of such methodology has given tremendous results over the security issues, authentication issues.

Small fingerprint scanners used in mobile devices: the impact on biometric performance

Biometrics has burst into mobile technology. Fingerprint scanners are being embedded in smartphones and tablets supplying these devices with the security and usability provided by biometric authentication mechanisms. However, performance results obtained by biometric systems cannot be extrapolated to mobile devices. The conditions change, especially at capture process, due to the reduced sensing area of the scanners used. The impact of small fingerprint scanners on the quality and biometric performance of the system is studied. A database using three different fingerprint scanners has been collected and reduced-size images (i.e. 12 × 12 mm 2 , 10 × 10 mm 2 and 8 × 8 mm 2 ) have been modelled by cropping the original ones. Performance testing has been conducted using one public and one commercial algorithm, and considering two application scenarios. One scenario in which enrolment and authentication are executed using the same small sensor included in the mobile device (i.e. cropped image against cropped image) and a second scenario in which enrolment is executed using an external larger sensor and authentication is done using the mobile device sensor (i.e. full image against cropped image). Results show the gradual worsening of quality and error rates as the size of the fingerprint scanner is reduced revealing a significant difference between the application scenarios analysed.

Faster person identification using compressed ECG in time critical wireless telecardiology applications

Adoption of compression technology is often required for wireless cardiovascular monitoring, due to the enormous size of electrocardiogram (ECG) signal and limited bandwidth of Internet. However, compressed ECG must be decompressed before performing human identification using present research on ECG based biometric techniques. This additional step of decompression creates a significant processing delay for identification task. This becomes an obvious burden on a system if this needs to be done for millions of compressed ECG segments by the hospital. This paper proposes a novel method of ECG biometric directly form compressed ECG harnessing data mining (DM) techniques like attribute selection and clustering. The biometric template created by this new technique is lower in size compared to the existing ECG based biometrics as well as other forms of biometrics like face, finger, retina, etc. The template size (and also the matching time) is up to 8533 times lower than face template, 61 times lower than existing percentage root mean square (PRD) ECG based biometric template and 9 times smaller than polynomial distance measurement (PDM) based ECG biometric. Smaller template size substantially reduces the one to many matching time for biometric recognition, resulting in a faster biometric authentication mechanism and ECG stream verification directly from compressed ECG.

Compressed ECG Biometric: A Fast, Secured and Efficient Method for Identification of CVD Patient

Adoption of compression technology is often required for wireless cardiovascular monitoring, due to the enormous size of Electrocardiography (ECG) signal and limited bandwidth of Internet. However, compressed ECG must be decompressed before performing human identification using present research on ECG based biometric techniques. This additional step of decompression creates a significant processing delay for identification task. This becomes an obvious burden on a system, if this needs to be done for a trillion of compressed ECG per hour by the hospital. Even though the hospital might be able to come up with an expensive infrastructure to tame the exuberant processing, for small intermediate nodes in a multihop network identification preceded by decompression is confronting. In this paper, we report a technique by which a person can be identified directly from his / her compressed ECG. This technique completely obviates the step of decompression and therefore upholds biometric identification less intimidating for the smaller nodes in a multihop network. The biometric template created by this new technique is lower in size compared to the existing ECG based biometrics as well as other forms of biometrics like face, finger, retina etc. (up to 8302 times lower than face template and 9 times lower than existing ECG based biometric template). Lower size of the template substantially reduces the one-to-many matching time for biometric recognition, resulting in a faster biometric authentication mechanism.

A non-intrusive biometric authentication mechanism utilising physiological characteristics of the human head

This paper proposes and evaluates a non-intrusive biometric authentication technique drawn from the discrete areas of biometrics and Auditory Evoked Responses. The technique forms a hybrid multi-modal biometric in which variations in the human voice due to the propagation effects of acoustic waves within the human head are used to verify the identity of a user. The resulting approach is known as the Head Authentication Technique (HAT). Evaluation of the HAT authentication process is realised in two stages. First, the generic authentication procedures of registration and verification are automated within a prototype implementation. Second, a HAT demonstrator is used to evaluate the authentication process through a series of experimental trials involving a representative user community. The results from the trials confirm that multiple HAT samples from the same user exhibit a high degree of correlation, yet samples between users exhibit a high degree of discrepancy. Statistical analysis of the prototype performance realised system error rates of 6% False Non-Match Rate (FNMR) and 0.025% False Match Rate (FMR).