Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns

Acien, Alejandro; Morales, Aythami; Vera-Rodríguez, Rubén; Fiérrez, Julián

doi:10.1007/978-3-030-39489-9_9

Cited by 15 publications

(24 citation statements)

References 22 publications

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“…based (face recognition, fingerprint) is unobtrusive, continuous, less prone to attacks, and easily tracked through wearable devices, videos, and smartphones in the context of IoT environments. Gait-based authentication is studied either by itself (Qin et al, 2019) or as part of an authentication system that uses multiple modalities (Hintze et al, 2019) (Acien et al, 2019) (See Table 10).…”

Section: Referencementioning

confidence: 99%

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A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

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Section: Referencementioning

confidence: 99%

“…Authors (Acien et al, 2019) showed that the fusion with behavioral data improves the authentication system results.…”

Section: Referencementioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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“…As can be seen in the works cited above, it is common to use different kinds of data, with most authors using two or three. Reference [ 16 ] stands out for its design, where seven different kinds of data are used: Touch dynamics (touch gestures and keystroking), accelerometer, gyroscope, WiFi, GPS location, and app usage, are all collected during human-mobile interaction to authenticate the users. This system, called MultiLock, use a Support Vector Machine (SVM) with a radial basis function (RBF) kernel to perform the classification and the authors test the MultiLock system in different scenarios in the UMDAA-02 database, reporting an accuracy ranging from 82.2% to 97.1%.…”

Section: Related Workmentioning

confidence: 99%

S3: An AI-Enabled User Continuous Authentication for Smartphones Based on Sensors, Statistics and Speaker Information

López

Celdrán

Marín-Blázquez

et al. 2021

Sensors

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Continuous authentication systems have been proposed as a promising solution to authenticate users in smartphones in a non-intrusive way. However, current systems have important weaknesses related to the amount of data or time needed to build precise user profiles, together with high rates of false alerts. Voice is a powerful dimension for identifying subjects but its suitability and importance have not been deeply analyzed regarding its inclusion in continuous authentication systems. This work presents the S3 platform, an artificial intelligence-enabled continuous authentication system that combines data from sensors, applications statistics and voice to authenticate users in smartphones. Experiments have tested the relevance of each kind of data, explored different strategies to combine them, and determined how many days of training are needed to obtain good enough profiles. Results showed that voice is much more relevant than sensors and applications statistics when building a precise authenticating system, and the combination of individual models was the best strategy. Finally, the S3 platform reached a good performance with only five days of use available for training the users’ profiles. As an additional contribution, a dataset with 21 volunteers interacting freely with their smartphones for more than sixty days has been created and made available to the community.

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“…The first group of related approaches uses data collected directly on the frontend side. MutliLock [8] and T.P. Thao's [9] approach address user authentication by means of biometric and behavioral features.…”

Section: Related Workmentioning

confidence: 99%

SPCAuth: Scalable and Privacy-Preserving Continuous Authentication for Web Applications

Monschein

Waldhorst

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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As password-based authentication fails to provide adequate security for online activities such as financial transactions, additional authentication factors are required. Such factors should provide both ease of use and an adequate level of privacy protection, while being easy to implement, operate and maintain, even for applications with thousands of users. As an approach meeting these requirements, we outline Scalable and Privacy-Preserving Continuous Authentication (SPCAuth). SPCAuth determines risk levels for actions that should be authenticated, without requiring explicit user interactions. It analyzes different aspects of user behavior by means of machine learning methods, while preserving the privacy of the affected individuals. SPCAuth trains only a single model per aspect of user behavior being considered, based on observations of all users, ensuring scalability and increasing accuracy for users with infrequent activities. In two experiments, we have confirmed that this key concept enables a scalable and accurate user authentication.

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Mobile Active Authentication based on Multiple Biometric and Behavioral Patterns

Cited by 15 publications

References 22 publications

A Survey of Human Gait-Based Artificial Intelligence Applications

A Survey of Human Gait-Based Artificial Intelligence Applications

S3: An AI-Enabled User Continuous Authentication for Smartphones Based on Sensors, Statistics and Speaker Information

SPCAuth: Scalable and Privacy-Preserving Continuous Authentication for Web Applications

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