Continuous User Authentication Using Machine Learning on Touch Dynamics

Budulan, Stefania; Burceanu, Elena; Rebedea, Traian; Chiru, Costin-Gabriel

doi:10.1007/978-3-319-26532-2_65

Cited by 12 publications

(1 citation statement)

References 5 publications

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“…Stefania Budulan et al [15] gathered data from 5 Android phones screens and extracted features based on 41 user's data sets. Touch-down and touch-up is considered as an input sequence action.…”

Section: Review Of Related Workmentioning

confidence: 99%

A Study of Touch Dynamics Biometrics Authentication

Agrawal¹

2022

IJSREM

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A lot of recent research activities have been fulfilled in keystroke and mouse dynamics in the past three decades but the work in touch screen biometric authentication still needs to be explored. The motivation of this paper is that touch dynamics behavioral biometric authentication can be easily deployed along with user normal activities. Behavioral biometric along with machine learning algorithms and risk-assessment techniques can be efficiently and effectively used as an adaptive authentication. With the rapid usage of smart phones and touch screens in other applications, touch dynamics can be an important authentication factor in multi-factor authentication. Though a numerous studies have been done in behavioral human biometric, there is indeed much scope in the area of touch dynamics. The objective of this study is to provide the throughout survey of techniques used in this field along with the advancements for further research work. Keywords Biometric, touch dynamics, Behavioral authentication, multi-factor authentication, adaptive authentication

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Section: Review Of Related Workmentioning

confidence: 99%

A Study of Touch Dynamics Biometrics Authentication

Agrawal¹

2022

IJSREM

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Continuous authentication using biometrics: An advanced review

Dahia

Jesus

Segundo

2020

WIREs Data Min & Knowl

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The shortcomings of conventional access control systems for high‐security environments have led to the concert of continuous authentication. Contrary to traditional verification, in which users are authenticated only once at the start of their session, continuous authentication systems regularly check users' identities to prevent hijackings. The challenges in this area involve balancing the security of protected assets by quickly detecting intruders with the system usability for genuine users. Biometric recognition plays a major role within this context, as it is the main way to assure that users are who they claim to be. A comparative analysis of the latest works revealed different aspects of this problem. First, some biometrics traits among those applied for continuous authentication are more suitable for this task than others. Second, systems combining multiple traits have advantages over those relying on a single one. Finally, many works fail to report proper evaluation metrics. With this in mind, we were able to identify new opportunities for researchers in the field. We highlight the potential for mining new datasets on the internet, which would benefit validation and benchmarking, and how recent deep learning techniques could address some of the open challenges in the area. This article is categorized under: Technologies > Prediction Technologies > Machine Learning Application Areas > Science and Technology

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The Research and Implementation of the Fine-Grained Implicit Authentication Framework for Android

Zhou

Yang

2018

Communications in Computer and Information Science

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Continuous User Authentication Using Machine Learning on Touch Dynamics

Cited by 12 publications

References 5 publications

A Study of Touch Dynamics Biometrics Authentication

A Study of Touch Dynamics Biometrics Authentication

Continuous authentication using biometrics: An advanced review

The Research and Implementation of the Fine-Grained Implicit Authentication Framework for Android

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