Keystroke dynamics authentication for mobile phones

Maiorana, Emanuele; Campisi, Patrizio; González-Carballo, Noelia; Neri, Alessandro

doi:10.1145/1982185.1982190

Cited by 87 publications

(55 citation statements)

References 28 publications

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“…Authentication can be performed via fixed-text analysis (i.e., with the user typing some predetermined text) [7,13,20,25,28,31,32,36,42,46] or via free-text analysis (i.e., with the user typing freely on the keyboard) [14,41]. Keystroke dynamics techniques have been explored for a broad range of devices, equipped with either hardware [26,29] or software (also called "soft") keyboards [56]-with recent work on mobile devices largely falling into the latter category [23,55,56].…”

Section: Keystroke Dynamicsmentioning

confidence: 99%

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

Giuffrida¹,

Majdanik²,

Conti

et al. 2014

Detection of Intrusions and Malware, and Vulnerability Assessment

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Abstract. Mobile devices have become an important part of our everyday life, harvesting more and more confidential user information. Their portable nature and the great exposure to security attacks, however, call out for stronger authentication mechanisms than simple password-based identification. Biometric authentication techniques have shown potential in this context. Unfortunately, prior approaches are either excessively prone to forgery or have too low accuracy to foster widespread adoption.In this paper, we propose sensor-enhanced keystroke dynamics, a new biometric mechanism to authenticate users typing on mobile devices. The key idea is to characterize the typing behavior of the user via unique sensor features and rely on standard machine learning techniques to perform user authentication. To demonstrate the effectiveness of our approach, we implemented an Android prototype system termed Unagi. Our implementation supports several feature extraction and detection algorithms for evaluation and comparison purposes. Experimental results demonstrate that sensor-enhanced keystroke dynamics can improve the accuracy of recent gestured-based authentication mechanisms (i.e., EER > 0.5%) by one order of magnitude, and the accuracy of traditional keystroke dynamics (i.e., EER > 7%) by two orders of magnitude.

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Section: Keystroke Dynamicsmentioning

confidence: 99%

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

Giuffrida¹,

Majdanik²,

Conti

et al. 2014

Detection of Intrusions and Malware, and Vulnerability Assessment

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show abstract

“…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].…”

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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“…They achieved 12.8% EER using neural network classifiers on mobile handsets with a 12-key hardware keyboard [25,23]. Maiorana et al [73] also investigated the feasibility of using keystroke dynamics for user verification on mobile phones. They proposed a new statistical classifier which is computationally efficient for use in a mobile environment.…”

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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Keystroke dynamics authentication for mobile phones

Cited by 87 publications

References 28 publications

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

I Sensed It Was You: Authenticating Mobile Users with Sensor-Enhanced Keystroke Dynamics

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

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