DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms

Gupta, Sandeep; Buriro, Attaullah; Crispo, Bruno

doi:10.1016/j.cose.2019.01.007

Cited by 36 publications

(11 citation statements)

References 30 publications

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“…Our prototype applications use client-server architecture. The data generated, as a result of user's actions, i.e., swipe and voice command, was encrypted and packetized on the client device, i.e., smartphone, and was instantaneously transferred to the server, for further processing, i.e., verification of the user's identity [1].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

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A chimerical dataset combining physiological and behavioral biometric traits for reliable user authentication on smart devices and ecosystems

2020

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We present a chimerical dataset that combines both physiological and behavioral biometric traits, for reliable user authentication on smart devices and ecosystems [1]. The data are composed of statistical features computed from swipe-gesture, voice-prints, and face-images. The swipe and voice-prints data presented hereinafter are collected using a customized Android application - DriverAuth, however, the face data is obtained from the MOBIO Dataset [2].We collected 10,320 swipe and voice-prints samples from 86 users worldwide by collaborating with a professional crowd-sourcing platform and formed a chimerical dataset adjunct to the publicly available MOBIO dataset with our collected dataset. The dataset consists of various statistical features computed from the raw data for all three traits, i.e., swipe, voice-print, and face.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Related research article DriverAuth: A Risk-based Multi-modal Biometric-based Driver Authentication Scheme for Ride-sharing Platforms (Ref. COSE_1458) [1] https://doi.org/10.1016/j.cose.2019.01.007…”

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confidence: 99%

A chimerical dataset combining physiological and behavioral biometric traits for reliable user authentication on smart devices and ecosystems

2020

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“…On-demand ride and ride-sharing services that entirely depends on wireless connectivity have revolutionized the point-to-point transportation market [30]. In the coming years, connected automated vehicles (CAV) will facilitate economical transport services with better availability and safety on the road [31].…”

Section: ) Secondary Sector and Tertiary Sectormentioning

confidence: 99%

The Race for Future Wireless Internet and Socio-Economic Aspects

Gupta¹

2023

Preprint

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Wireless internet are among the fastest-growing segment of the communication industry. It is absolutely necessitated for future wireless internet to reach all terrestrial and aerial spaces covering densely populated areas to entire geographical regions across the globe to bring the cyber and the physical world further closer. The International Telecommunication Union (ITU) recommended that wireless internet research not only considers the technical aspects but also be able to contemplate economic development and societal needs. In this article, we first cover the pivotal technological aspects of 6G and satellite internet for rendering seamless wireless connectivity that can be reliable, affordable, and scalable for emerging use cases. Afterward, we discuss the impact of future wireless communications on economic growth and social benefits aspects.

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“…Swipe can be defined as a finite touch-events sequence that occurred as a result of users touching a smart device's touchscreen with their finger. Smart devices provide APIs to get touch coordinates, velocity, and pressure data for each touch-point [58]. Some of the spatial features that can be extracted from a swipe action are the touch-points timestamp, x-and y-coordinates, velocity, and acceleration.…”

Section: Swipementioning

confidence: 99%

“…On a dataset of 13 subjects, a SVM using radial-basis function (RBF) kernel is used for evaluation, achieving a success rate of 90%. DriverAuth computed statistical features after extracting MFCCs from a bandpass filter voice signal containing 2 channels sampled at 44,100 Hz with 16 bits per sample [58]. The authors used Q-SVM, ETB, Weighted kNN (W-kNN) classifiers for generating a multi-class classification model.…”

Section: Footstepmentioning

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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DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms

Cited by 36 publications

References 30 publications

A chimerical dataset combining physiological and behavioral biometric traits for reliable user authentication on smart devices and ecosystems

A chimerical dataset combining physiological and behavioral biometric traits for reliable user authentication on smart devices and ecosystems

The Race for Future Wireless Internet and Socio-Economic Aspects

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

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