Continuous Authentication of Smartphone Users via Swipes and Taps Analysis

Alina, Garbuz; Epishkina, Anna; Kogos, Konstantin

doi:10.1109/eisic49498.2019.9108780

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…This combination ensures secure and reliable authentication, providing an additional layer of protection for smartphone users [27]. Other combinations, such as swipe and tap, result in increased security through continuous user authentication by paying attention to factors such as vibrations from walking, the effects of different positions, and trembling hands in cold temperatures [31] and the number of tap gestures implemented without any combination [49], [50], [57].…”

Section: B Biometric Authenticatorsmentioning

confidence: 99%

“…Moreover, SVM classifiers are trained on facial characteristics using advanced methods like face warping and textual feature extraction, ensuring precise face identification [41]. The authentication process further incorporates the analysis of touchscreen interactions and subtle micro-gestures, enhancing the overall accuracy and reliability of the system [31].…”

Section: Behavioralmentioning

confidence: 99%

See 1 more Smart Citation

Study on the Implementation of Multimodal Continuous Authentication in Smartphones: A Systematic Review

Syalevi,

Prasetyo,

Aji

2024

IJACSA

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Profound societal shifts result from the inception of the 4.0 age of the Industrial Revolution and rapid technological advancements. The widespread adoption of e-services has resulted in substantial reliance on smartphones to access diverse offerings. Even so, account breaches and data leaks are risks that users take when they rely so heavily on their smartphones. Authentication is an essential method of safeguarding personal information. The purpose of this study is to undertake a thorough review of the literature on the deployment and trends of multimodal biometric authentication on smartphones. The studies will look at several biometric modalities, such as behavioral and physiological characteristics, and the algorithms for pattern recognition used in continuous authentication systems. The results show various biometric authenticators and emphasize the importance of behavioral features in smartphone authentication. In addition, the research underlines the significance of machine learning algorithms in pattern identification for rapid and accurate analysis. This study helps to understand the present authentication technique landscape and gives ideas for future advances in safe and user-friendly smartphone authentication systems.

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Section: B Biometric Authenticatorsmentioning

confidence: 99%

Section: Behavioralmentioning

confidence: 99%

Study on the Implementation of Multimodal Continuous Authentication in Smartphones: A Systematic Review

Syalevi,

Prasetyo,

Aji

2024

IJACSA

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show abstract

“…They investigated a statistical approach based on adapted Gaussian Mixture Models (GMM) for swipe gestures and achieved an EER of 20% (40 training samples) using a dataset with 90 subjects. Garbuz et al [91] proposed an approach that analyzed both swipes and taps to provide continuous authentication.…”

Section: Footstepmentioning

confidence: 99%

“…Moreover, behavioral biometrics have been evaluated for designing implicit [136,137,138], continuous [91,93,117], and risk-based [54,139] user recognition schemes.…”

Section: Securitymentioning

confidence: 99%

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

Gupta¹,

Maple²,

Crispo³

et al. 2022

Preprint

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The proliferation of Internet of Things (IoT) systems is having a profound impact across all aspects of life. Recognising and identifying particular users is central to delivering the personalised experience that citizens want to experience, and that organisations wish to deliver. This article presents a survey of human-computer interaction-based (HCI-based) and natural habits-based behavioral biometrics that can be acquired unobtrusively through smart devices or IoT sensors for user recognition purposes. Robust and usable user recognition is also a security requirement for emerging IoT ecosystems to protect them from adversaries. Typically, it can be specified as a fundamental building block for most types of human-to-things accountability principles and access-control methods. However, end-users are facing numerous security and usability challenges in using currently available knowledge- and token-based recognition (i.e., authentication and identification) schemes. To address the limitations of conventional recognition schemes, biometrics, naturally come as a first choice to supporting sophisticated user recognition solutions. We perform a comprehensive review of touch-stroke, swipe, touch signature, hand-movements, voice, gait and footstep behavioral biometrics modalities. This survey analyzes the recent state-of-the-art research of these behavioral biometrics with a goal to identify their attributes and features for generating unique identification signatures. Finally, we present security, privacy, and usability evaluations that can strengthen the designing of robust and usable user recognition schemes for IoT applications.

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Touch events and human activities for continuous authentication via smartphone

Gattulli

Impedovo

Pirlo

et al. 2023

Sci Rep

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The security of modern smartphones is related to the combination of Continuous Authentication approaches, Touch events, and Human Activities. The approaches of Continuous Authentication, Touch Events, and Human Activities are silent to the user but are a great source of data for Machine Learning Algorithms. This work aims to develop a method for continuous authentication while the user is sitting and scrolling documents on the smartphone. Touch Events and Smartphone Sensor Features (from the well-known H-MOG Dataset) were used with the addition, for each sensor, of the feature called Signal Vector Magnitude. Several Machine Learning Models have been considered with different experiment setups, 1-class, and 2-class, for evaluation. The results show that the 1-class SVM achieves an accuracy of 98.9% and an F1-score of 99.4%, considering the selected features and the feature Signal Vector Magnitude very significant.

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Continuous Authentication of Smartphone Users via Swipes and Taps Analysis

Cited by 4 publications

References 14 publications

Study on the Implementation of Multimodal Continuous Authentication in Smartphones: A Systematic Review

Study on the Implementation of Multimodal Continuous Authentication in Smartphones: A Systematic Review

A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

Touch events and human activities for continuous authentication via smartphone

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