Implicit Authentication through Learning User Behavior

Shi, Elaine; Niu, Yuan; Jakobsson, Markus; Chow, Richard

doi:10.1007/978-3-642-18178-8_9

Cited by 179 publications

(155 citation statements)

References 15 publications

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“…Keystroke dynamics for authenticating mobile phone users have also been widely studied [15,17,21,36,39]. A body of work has also been conducted on Unobtrusive methods for IA on mobile phones such as Gait-based IA schemes [11,35] and context aware schemes [5,16,33]. Our work differs significantly from the above mentioned schemes based on the reasons given in Section 5.…”

Section: Related Workmentioning

confidence: 99%

Touch and You’re Trapp(ck)ed: Quantifying the Uniqueness of Touch Gestures for Tracking

Masood

Zhao

Asghar

et al. 2018

Proceedings on Privacy Enhancing Technologies

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Abstract:We argue that touch-based gestures on touchscreen devices enable the threat of a form of persistent and ubiquitous tracking which we call touch-based tracking. Touch-based tracking goes beyond the tracking of virtual identities and has the potential for cross-device tracking as well as identifying multiple users using the same device. We demonstrate the likelihood of touchbased tracking by focusing on touch gestures widely used to interact with touch devices such as swipes and taps.. Our objective is to quantify and measure the information carried by touch-based gestures which may lead to tracking users. For this purpose, we develop an information theoretic method that measures the amount of information about users leaked by gestures when modelled as feature vectors. Our methodology allows us to evaluate the information leaked by individual features of gestures, samples of gestures, as well as samples of combinations of gestures. Through our purposebuilt app, called TouchTrack, we gather gesture samples from 89 users, and demonstrate that touch gestures contain sufficient information to uniquely identify and track users. Our results show that writing samples (on a touch pad) can reveal 73.7% of information (when measured in bits), and left swipes can reveal up to 68.6% of information. Combining different combinations of gestures results in higher uniqueness, with the combination of keystrokes, swipes and writing revealing up to 98.5% of information about users. We further show that, through our methodology, we can correctly re-identify returning users with a success rate of more than 90%.

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Section: Related Workmentioning

confidence: 99%

Touch and You’re Trapp(ck)ed: Quantifying the Uniqueness of Touch Gestures for Tracking

Masood

Zhao

Asghar

et al. 2018

Proceedings on Privacy Enhancing Technologies

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show abstract

“…As they become an increasingly indispensable and important part of our everyday life, it is essential to ensure secure access of these devices as they store not only personal but oftentimes sensitive and even critical information [24,86,93]. Driven by this increasing demand, research work on keystroke dynamics biometrics on mobile devices has mushroomed in recent years [22,42].…”

Section: Keystroke Dynamics For Mobile Devicesmentioning

confidence: 99%

A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

Zhong¹,

Deng²

2015

Gate to Computer Science and Research

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In this review paper we present a comprehensive survey of research efforts in the past couple of decades on keystroke dynamics biometrics. We review the literature in light of various feature extraction, feature matching and classification methods for keystroke dynamics. We also discuss recent trends in keystroke dynamics research, including its use in mobile environments, as a soft biometrics, and its fusion with other biometric modalities. We further address the evaluation of keystroke biometric systems, including traditional and new performance metrics, and list publicly available keystroke datasets for performance benchmarks to promote synergy in the research community.

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“…are all rich information that could be gathered and recorded almost entirely using smartphones. (Shi et al, 2011).…”

Section: Motivation and Proposalmentioning

confidence: 99%

“…According to Shi et al (2011) the implicit authentication is able to: i) act as a second factor for authentication passwords and supplements for greater assurance of a profitable and easy to use, ii) work as the primary authentication method to completely replace passwords, and iii) provide additional security for financial transactions such as credit card purchases, acting as an indicator of fraud. We note that in the latter scenario, the act of making the transaction does not require any user action on the device.…”

Section: Motivation and Proposalmentioning

confidence: 99%

CARS-AD Project: Context-Aware Recommender System for Authentication Decision in Pervasive and Mobile Environments

Lima¹,

Rocha²,

Augustin³

et al. 2012

Advances and Applications in Mobile Computing

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Implicit Authentication through Learning User Behavior

Cited by 179 publications

References 15 publications

Touch and You’re Trapp(ck)ed: Quantifying the Uniqueness of Touch Gestures for Tracking

Touch and You’re Trapp(ck)ed: Quantifying the Uniqueness of Touch Gestures for Tracking

A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

CARS-AD Project: Context-Aware Recommender System for Authentication Decision in Pervasive and Mobile Environments

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