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

Giuffrida, Cristiano; Majdanik, Kamil; Conti, Mauro; Bos, Herbert

doi:10.1007/978-3-319-08509-8_6

Cited by 79 publications

(85 citation statements)

References 48 publications

(71 reference statements)

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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].…”

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

“…First of all, we group them by an interval between pressing and releasing a key and discard the rest of them. Then there are 5 formulas for the grouped data: average value (mean), root mean square (RMS), sum of positive values (pos), sum of positive values (neg), and standard deviation (std) [11].…”

Section: Formulas For Motion Datamentioning

confidence: 99%

Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors

Lee

Hwang

Kim

et al. 2018

Security and Communication Networks

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Personal Identification Numbers (PINs) and pattern drawing have been used as common authentication methods especially on smartphones. Such methods, however, are very vulnerable to the shoulder surfing attack. Thus, keystroke dynamics that authenticate legitimate users based on their typing manner have been studied for years. However, many of the studies have focused on PC keyboard keystrokes. More studies on mobile and smartphones keystroke dynamics are warranted; as smartphones make progress in both hardware and software, features from smartphones have been diversified. In this paper, using various features including keystroke data such as time interval and motion data such as accelerometers and rotation values, we evaluate features with motion data and without motion data. We also compare 5 formulas for motion data, respectively. We also demonstrate that opposite gender match between a legitimate user and impostors has influence on authenticating by our experiment results.

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“…This imitation attack poses an threat to keystroke dynamics based user authentication. Giuffrida et al (2014) proposed using sensor-enhanced keystroke dynamics to authenticate users typing on mobile devices. A limitation of their solution is that it works on mobile devices only.…”

Section: Keystroke Dynamicsmentioning

confidence: 99%

Making a good thing better: enhancing password/PIN-based user authentication with smartwatch

Chang

Wang

et al. 2018

Cybersecur

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Wearing smartwatches becomes increasingly popular in people's lives. This paper shows that a smartwatch can help its bearer authenticate to a login system effectively and securely even if the bearer's password has already been revealed. This idea is motivated by our observation that a sensor-rich smartwatch is capable of tracking the wrist motions of its bearer typing a password or PIN, which can be used as an authentication factor. The major challenge in this research is that a sophisticated attacker may imitate a user's typing behavior as shown in previous research on keystroke dynamics based user authentication. We address this challenge by applying a set of machine learning and deep learning classifiers on the user's wrist motion data that are collected from a smartwatch worn by the user when inputting his/her password or PIN. Our solution is user-friendly since it does not require users to perform any additional actions when typing passwords or PINs other than wearing smartwatches. We conduct a user study involving 51 participants so as to evaluate the feasibility and performance of our solution. User study results show that the best classifier is the Bagged Decision Trees, which yields 4.58% FRR and 0.12% FAR on a QWERTY keyboard, and 6.13% FRR and 0.16% FAR on a numeric keypad.

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

Cited by 79 publications

References 48 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

Understanding Keystroke Dynamics for Smartphone Users Authentication and Keystroke Dynamics on Smartphones Built-In Motion Sensors

Making a good thing better: enhancing password/PIN-based user authentication with smartwatch

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