A Framework for Continuous Authentication Based on Touch Dynamics Biometrics for Mobile Banking Applications

Estrela, Priscila Morais Argôlo Bonfim; Albuquerque, Robson de Oliveira; Amaral, Dino Macedo; Giozza, William F.; Júnior, Rafael Timóteo de Sousa

doi:10.3390/s21124212

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…The motivation behind this exploration is to investigate and foster comprehension of basic worries blunder-free, disappointment-free, more usable and best executable mmedical applications because the patient can't afford any misconception due to horridness [19]. This Idea will be founded on quantitative request.…”

Section: Methodsmentioning

confidence: 99%

ML-based Meta-Model Usability Evaluation of Mobile Medical Apps

Hamid,

Ibrar,

Delshadi

et al. 2024

IJACSA

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Mobile medical applications (MMAPPs) are one of the recent trends in mobile trading applications (Apps).MMAPPs permit users to resolve health issues easily and effectively in their place. However, the primary issue is effective usability for users in maps. Barely any examination breaks down usability issues subject to the user's age, orientation, trading accessories, or experience. The motivation behind this study is to decide the level of usability issues, concerning traits and experience of versatile clinical clients. The review utilizes a quantitative technique and performs client try and hypothetical insight through the survey by 677 members with six distinct assignments on the application's point of interaction. The posttry review is finished with concerning members. The Response surface method (RSM) is used for perceptional and experimental designs. In each case, participants are divided into 13 runs or groups. Experimental groups are involved after checking the perceptions about theoretical usability for different attributes according to the usability model through the questionnaire. The difference is recorded between the perception of users about usability (theoretical usability) and actual performance for usability. The study analyzed through Analysis of variance (ANOVA) that there is a need to improve mobile medical applications but it is also recommended to minimize the gap between the perception level of laymen and the actual performance of IT literate users in context with usability. The experimentation measures the tasks usability of various mobile medical applications concerning their effectiveness, efficiency, completeness, learnability, memorability, easiness, complexity, number of errors and satisfaction. Every design model also produces a mathematical expression to calculate usability with its attributes. The results of this study will help to improve the usability of MMAPPs for users in their convenient context.

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Section: Methodsmentioning

confidence: 99%

ML-based Meta-Model Usability Evaluation of Mobile Medical Apps

Hamid,

Ibrar,

Delshadi

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…Several performance indicators have shown that deep neural networks can provide the high accuracy required for verifying human identity. Estrela et al [9] presented a behavioral measure of biometrics for user verification in banking applications to prevent fraud and impersonation attacks. It achieved accuracies of 90.68% and 97.05% when using biometrics for banking authorization in various scenarios.…”

Section: Background and Related Workmentioning

confidence: 99%

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

2023

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In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer's KYC and anonymise the collected visual data to ensure the customer's privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of inperson banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations.

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“…In authentication problems, NB determines the probability that a user is genuine, given the known probabilities extracted from training data in the model. Estrela et al [ 54 ] proposed a framework using touch dynamics biometrics for continuous authentication in mobile banking applications. Touch dynamic is a biological recognition method based on individuals’ touch patterns.…”

Section: Machine Learning Models In Authentication Schemes Of Telehealthmentioning

confidence: 99%

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Fard

Gebali

Mamun

2022

Sensors

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Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting the privacy and security of users is crucial in such online systems. Although there are many protocols to provide security through strong authentication systems, sophisticated IoT attacks are becoming more prevalent. Using machine learning to handle biometric information or physical layer features is key to addressing authentication problems for human and IoT devices, respectively. This tutorial discusses machine learning applications to propose robust authentication protocols. Since machine learning methods are trained based on hidden concepts in biometric and physical layer data, these dynamic authentication models can be more reliable than traditional methods. The main advantage of these methods is that the behavioral traits of humans and devices are tough to counterfeit. Furthermore, machine learning facilitates continuous and context-aware authentication.

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A Framework for Continuous Authentication Based on Touch Dynamics Biometrics for Mobile Banking Applications

Cited by 15 publications

References 30 publications

ML-based Meta-Model Usability Evaluation of Mobile Medical Apps

ML-based Meta-Model Usability Evaluation of Mobile Medical Apps

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

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