Touch to Authenticate — Continuous Biometric Authentication on Mobile Devices

Temper, Marlies; Tjoa, Simon; Kaiser, Manfred

doi:10.1109/icssa.2015.016

Cited by 12 publications

(11 citation statements)

References 13 publications

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“…Based on the observations made in the literature review, which are summarized in Table 19 , the continuous authentication framework proposed in this work, developed for continuous authentication based on touch dynamics biometrics, and focused on mobile banking applications presents better results than that in [ 19 ], which proposed models that also use continuous authentication based on both static and dynamic verification for authentication of the user, during the entire interaction with an application.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, the authors of [ 18 ] developed a banking analogous application with continuous static verification by considering password typing and by evaluating it on a set with 95 participating volunteers and data captured from the touch interaction and sensors available on smartphones, obtaining a 96% accuracy with the RF algorithm. In [ 19 ], using a fuzzy-based classifier, a static and dynamic continuous authentication model was proposed with the data captured from touchscreen and accelerometer interactions in an application developed using the characteristics of a real mobile banking application and from use by 22 volunteers, giving an EER of 11.5%.…”

Section: State-of-the-artmentioning

confidence: 99%

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

Estrela

Albuquerque

Amaral

et al. 2021

Sensors

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As smart devices have become commonly used to access internet banking applications, these devices constitute appealing targets for fraudsters. Impersonation attacks are an essential concern for internet banking providers. Therefore, user authentication countermeasures based on biometrics, whether physiological or behavioral, have been developed, including those based on touch dynamics biometrics. These measures take into account the unique behavior of a person when interacting with touchscreen devices, thus hindering identitification fraud because it is hard to impersonate natural user behaviors. Behavioral biometric measures also balance security and usability because they are important for human interfaces, thus requiring a measurement process that may be transparent to the user. This paper proposes an improvement to Biotouch, a supervised Machine Learning-based framework for continuous user authentication. The contributions of the proposal comprise the utilization of multiple scopes to create more resilient reasoning models and their respective datasets for the improved Biotouch framework. Another contribution highlighted is the testing of these models to evaluate the imposter False Acceptance Error (FAR). This proposal also improves the flow of data and computation within the improved framework. An evaluation of the multiple scope model proposed provides results between 90.68% and 97.05% for the harmonic mean between recall and precision (F1 Score). The percentages of unduly authenticated imposters and errors of legitimate user rejection (Equal Error Rate (EER)) are between 9.85% and 1.88% for static verification, login, user dynamics, and post-login. These results indicate the feasibility of the continuous multiple-scope authentication framework proposed as an effective layer of security for banking applications, eventually operating jointly with conventional measures such as password-based authentication.

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Section: Resultsmentioning

confidence: 99%

Section: State-of-the-artmentioning

confidence: 99%

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

Estrela

Albuquerque

Amaral

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Mobile device platform information, specially touch, is also used to some extent, e.g. [164], [178] or [44]. However, the use of body-related data, biosignals in particular, is experiencing a significant growth and in the last 3 years, in which 6 approaches have been proposed [194]…”

Section: Manuscript Submitted To Acmmentioning

confidence: 99%

“…For this purpose, a distance metrics (such as those described in Instance-Based Learning below) could be applied. In terms of CA, [42,69,127] work with different values of 'k', while [60] fixes 'k'=1, [171] fixes 'k'=4, [104] fixes 'k'=11, [56] fixes 'k' to {3, 10, 21} and [50,153,178] do not provide any configuration information.…”

Section: Enforcement Algorithm Selectionmentioning

confidence: 99%

Leveraging User-related Internet of Things for Continuous Authentication

2019

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Among all Internet of Things (IoT) devices, a subset of them are related to users. Leveraging these user-related IoT elements, it is possible to ensure the identity of the user for a period of time, thus avoiding impersonation. This need is known as Continuous Authentication (CA). Since 2009, a plethora of IoT-based CA academic research and industrial contributions have been proposed. We offer a comprehensive overview of 58 research papers regarding the main components of such a CA system. The status of the industry is studied as well, covering 32 market contributions, research projects, and related standards. Lessons learned, challenges, and open issues to foster further research in this area are finally presented.

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“…Em (M. Temper, S. Tjoa, and M. Kaiser, 2015), foi implementado um framework para aplicações bancárias mobile, baseado na autenticação contínua de usuários. O trabalho envolveu 22 voluntários, que interagiram com uma aplicação prototipada, baseada na interface de uma aplicação original de um banco.…”

Section: Trabalhos Relacionadosunclassified

Estudo Experimental Da Biometria Comportamental Para Autenticação Contínua De Usuários Em Aplicações Bancárias Mobile

Estrela¹,

Amaral²,

Albuquerque³

et al. 2019

Atas Da Conferência Ibero-Americana WWW/Internet 2019

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Neste artigo é proposto um framework multimodal para autenticação contínua e implícita em aplicações bancárias mobile, baseado na biometria comportamental no momento de digitação da senha, em tempo de login e na interação via touchscreen com a aplicação pós login, para geração de alertas de segurança caso um impostor tente se passar por um usuário legítimo na utilização da aplicação. Os resultados demonstram que a abordagem utilizada é promissora. Por exemplo, com o uso do algoritmo Random Forest foi possível atingir uma precisão média de 96% com Equal Error Rate (EER) de 3,57% para os modelos de digitação da senha, e 99% com EER de 0% para a interação via touchscreen com a aplicação pós login.

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Touch to Authenticate — Continuous Biometric Authentication on Mobile Devices

Cited by 12 publications

References 13 publications

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

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

Leveraging User-related Internet of Things for Continuous Authentication

Estudo Experimental Da Biometria Comportamental Para Autenticação Contínua De Usuários Em Aplicações Bancárias Mobile

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